2025-03-18,18:35:55 | INFO | Running with a single process. Device cuda. 2025-03-18,18:35:55 | INFO | Loaded crisp_1 model config. 2025-03-18,18:35:57 | INFO | Model: 2025-03-18,18:35:57 | INFO | TextTextCLIP( (text1): HFTextEncoder( (transformer): ESMplusplusModel( (embed): Embedding(64, 960) (transformer): TransformerStack( (blocks): ModuleList( (0-29): 30 x UnifiedTransformerBlock( (attn): MultiHeadAttention( (layernorm_qkv): Sequential( (0): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (1): Linear(in_features=960, out_features=2880, bias=False) ) (out_proj): Linear(in_features=960, out_features=960, bias=False) (q_ln): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (k_ln): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (rotary): RotaryEmbedding() ) (ffn): Sequential( (0): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (1): Linear(in_features=960, out_features=5120, bias=False) (2): SwiGLU() (3): Linear(in_features=2560, out_features=960, bias=False) ) (dropout): Dropout(p=0.0, inplace=False) ) ) (norm): LayerNorm((960,), eps=1e-05, elementwise_affine=True) ) ) (pooler): MeanPooler() (proj): Sequential( (0): Linear(in_features=960, out_features=736, bias=False) (1): GELU(approximate='none') (2): Linear(in_features=736, out_features=512, bias=False) ) ) (text2): HFTextEncoder( (transformer): Qwen2Model( (embed_tokens): Embedding(151936, 896) (layers): ModuleList( (0-23): 24 x Qwen2DecoderLayer( (self_attn): Qwen2Attention( (q_proj): Linear(in_features=896, out_features=896, bias=True) (k_proj): Linear(in_features=896, out_features=128, bias=True) (v_proj): Linear(in_features=896, out_features=128, bias=True) (o_proj): Linear(in_features=896, out_features=896, bias=False) ) (mlp): Qwen2MLP( (gate_proj): Linear(in_features=896, out_features=4864, bias=False) (up_proj): Linear(in_features=896, out_features=4864, bias=False) (down_proj): Linear(in_features=4864, out_features=896, bias=False) (act_fn): SiLU() ) (input_layernorm): Qwen2RMSNorm((896,), eps=1e-06) (post_attention_layernorm): Qwen2RMSNorm((896,), eps=1e-06) ) ) (norm): Qwen2RMSNorm((896,), eps=1e-06) (rotary_emb): Qwen2RotaryEmbedding() ) (pooler): MeanPooler() (proj): Sequential( (0): Linear(in_features=896, out_features=704, bias=False) (1): GELU(approximate='none') (2): Linear(in_features=704, out_features=512, bias=False) ) ) (text): HFTextEncoder( (transformer): ESMplusplusModel( (embed): Embedding(64, 960) (transformer): TransformerStack( (blocks): ModuleList( (0-29): 30 x UnifiedTransformerBlock( (attn): MultiHeadAttention( (layernorm_qkv): Sequential( (0): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (1): Linear(in_features=960, out_features=2880, bias=False) ) (out_proj): Linear(in_features=960, out_features=960, bias=False) (q_ln): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (k_ln): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (rotary): RotaryEmbedding() ) (ffn): Sequential( (0): LayerNorm((960,), eps=1e-05, elementwise_affine=True) (1): Linear(in_features=960, out_features=5120, bias=False) (2): SwiGLU() (3): Linear(in_features=2560, out_features=960, bias=False) ) (dropout): Dropout(p=0.0, inplace=False) ) ) (norm): LayerNorm((960,), eps=1e-05, elementwise_affine=True) ) ) (pooler): MeanPooler() (proj): Sequential( (0): Linear(in_features=960, out_features=736, bias=False) (1): GELU(approximate='none') (2): Linear(in_features=736, out_features=512, bias=False) ) ) (visual): HFTextEncoder( (transformer): Qwen2Model( (embed_tokens): Embedding(151936, 896) (layers): ModuleList( (0-23): 24 x Qwen2DecoderLayer( (self_attn): Qwen2Attention( (q_proj): Linear(in_features=896, out_features=896, bias=True) (k_proj): Linear(in_features=896, out_features=128, bias=True) (v_proj): Linear(in_features=896, out_features=128, bias=True) (o_proj): Linear(in_features=896, out_features=896, bias=False) ) (mlp): Qwen2MLP( (gate_proj): Linear(in_features=896, out_features=4864, bias=False) (up_proj): Linear(in_features=896, out_features=4864, bias=False) (down_proj): Linear(in_features=4864, out_features=896, bias=False) (act_fn): SiLU() ) (input_layernorm): Qwen2RMSNorm((896,), eps=1e-06) (post_attention_layernorm): Qwen2RMSNorm((896,), eps=1e-06) ) ) (norm): Qwen2RMSNorm((896,), eps=1e-06) (rotary_emb): Qwen2RotaryEmbedding() ) (pooler): MeanPooler() (proj): Sequential( (0): Linear(in_features=896, out_features=704, bias=False) (1): GELU(approximate='none') (2): Linear(in_features=704, out_features=512, bias=False) ) ) ) 2025-03-18,18:35:57 | INFO | Params: 2025-03-18,18:35:57 | INFO | accum_freq: 1 2025-03-18,18:35:57 | INFO | aug_cfg: {} 2025-03-18,18:35:57 | INFO | batch_size: 32 2025-03-18,18:35:57 | INFO | beta1: 0.9 2025-03-18,18:35:57 | INFO | beta2: 0.999 2025-03-18,18:35:57 | INFO | cache_dir: None 2025-03-18,18:35:57 | INFO | checkpoint_path: ./logs/2025_03_18-18_35_55-model_crisp_1-lr_0.0002-b_32-j_1-p_amp_bfloat16/checkpoints 2025-03-18,18:35:57 | INFO | coca_caption_loss_weight: 2.0 2025-03-18,18:35:57 | INFO | coca_contrastive_loss_weight: 1.0 2025-03-18,18:35:57 | INFO | copy_codebase: False 2025-03-18,18:35:57 | INFO | csv_caption_key: title 2025-03-18,18:35:57 | INFO | csv_img_key: filepath 2025-03-18,18:35:57 | INFO | csv_separator: 2025-03-18,18:35:57 | INFO | dataset_resampled: False 2025-03-18,18:35:57 | INFO | dataset_type: hf 2025-03-18,18:35:57 | INFO | ddp_static_graph: False 2025-03-18,18:35:57 | INFO | debug: False 2025-03-18,18:35:57 | INFO | delete_previous_checkpoint: False 2025-03-18,18:35:57 | INFO | device: cuda 2025-03-18,18:35:57 | INFO | dist_backend: None 2025-03-18,18:35:57 | INFO | dist_url: None 2025-03-18,18:35:57 | INFO | distill: False 2025-03-18,18:35:57 | INFO | distill_model: None 2025-03-18,18:35:57 | INFO | distill_pretrained: None 2025-03-18,18:35:57 | INFO | distributed: False 2025-03-18,18:35:57 | INFO | epochs: 30 2025-03-18,18:35:57 | INFO | epochs_cooldown: None 2025-03-18,18:35:57 | INFO | eps: 1e-08 2025-03-18,18:35:57 | INFO | force_custom_text: False 2025-03-18,18:35:57 | INFO | force_image_size: None 2025-03-18,18:35:57 | INFO | force_patch_dropout: None 2025-03-18,18:35:57 | INFO | force_quick_gelu: False 2025-03-18,18:35:57 | INFO | gather_with_grad: False 2025-03-18,18:35:57 | INFO | grad_checkpointing: False 2025-03-18,18:35:57 | INFO | grad_clip_norm: None 2025-03-18,18:35:57 | INFO | hf_dataset: photonmz/opi_function_packed 2025-03-18,18:35:57 | INFO | horovod: False 2025-03-18,18:35:57 | INFO | image_interpolation: None 2025-03-18,18:35:57 | INFO | image_mean: None 2025-03-18,18:35:57 | INFO | image_resize_mode: None 2025-03-18,18:35:57 | INFO | image_std: None 2025-03-18,18:35:57 | INFO | imagenet_v2: None 2025-03-18,18:35:57 | INFO | imagenet_val: None 2025-03-18,18:35:57 | INFO | local_loss: False 2025-03-18,18:35:57 | INFO | local_rank: 0 2025-03-18,18:35:57 | INFO | lock_image: False 2025-03-18,18:35:57 | INFO | lock_image_freeze_bn_stats: False 2025-03-18,18:35:57 | INFO | lock_image_unlocked_groups: 0 2025-03-18,18:35:57 | INFO | lock_text: False 2025-03-18,18:35:57 | INFO | lock_text_freeze_layer_norm: False 2025-03-18,18:35:57 | INFO | lock_text_unlocked_layers: 0 2025-03-18,18:35:57 | INFO | log_every_n_steps: 100 2025-03-18,18:35:57 | INFO | log_level: 20 2025-03-18,18:35:57 | INFO | log_local: False 2025-03-18,18:35:57 | INFO | log_path: ./logs/2025_03_18-18_35_55-model_crisp_1-lr_0.0002-b_32-j_1-p_amp_bfloat16/out.log 2025-03-18,18:35:57 | INFO | logs: ./logs/ 2025-03-18,18:35:57 | INFO | loss_dist_impl: None 2025-03-18,18:35:57 | INFO | lr: 0.0002 2025-03-18,18:35:57 | INFO | lr_cooldown_end: 0.0 2025-03-18,18:35:57 | INFO | lr_cooldown_power: 1.0 2025-03-18,18:35:57 | INFO | lr_scheduler: cosine 2025-03-18,18:35:57 | INFO | model: crisp_1 2025-03-18,18:35:57 | INFO | momentum: None 2025-03-18,18:35:57 | INFO | name: 2025_03_18-18_35_55-model_crisp_1-lr_0.0002-b_32-j_1-p_amp_bfloat16 2025-03-18,18:35:57 | INFO | no_set_device_rank: False 2025-03-18,18:35:57 | INFO | opt: adamw 2025-03-18,18:35:57 | INFO | precision: amp_bfloat16 2025-03-18,18:35:57 | INFO | pretrained: 2025-03-18,18:35:57 | INFO | pretrained_image: False 2025-03-18,18:35:57 | INFO | rank: 0 2025-03-18,18:35:57 | INFO | remote_sync: None 2025-03-18,18:35:57 | INFO | remote_sync_frequency: 300 2025-03-18,18:35:57 | INFO | remote_sync_protocol: s3 2025-03-18,18:35:57 | INFO | report_to: wandb 2025-03-18,18:35:57 | INFO | resume: None 2025-03-18,18:35:57 | INFO | save_frequency: 1 2025-03-18,18:35:57 | INFO | save_most_recent: True 2025-03-18,18:35:57 | INFO | seed: 0 2025-03-18,18:35:57 | INFO | siglip: False 2025-03-18,18:35:57 | INFO | skip_scheduler: False 2025-03-18,18:35:57 | INFO | tensorboard: False 2025-03-18,18:35:57 | INFO | tensorboard_path: 2025-03-18,18:35:57 | INFO | torchcompile: False 2025-03-18,18:35:57 | INFO | torchscript: False 2025-03-18,18:35:57 | INFO | trace: False 2025-03-18,18:35:57 | INFO | train_data: stub 2025-03-18,18:35:57 | INFO | train_data_upsampling_factors: None 2025-03-18,18:35:57 | INFO | train_num_samples: None 2025-03-18,18:35:57 | INFO | use_bn_sync: False 2025-03-18,18:35:57 | INFO | use_bnb_linear: None 2025-03-18,18:35:57 | INFO | val_data: stub 2025-03-18,18:35:57 | INFO | val_frequency: 1 2025-03-18,18:35:57 | INFO | val_num_samples: None 2025-03-18,18:35:57 | INFO | wandb: True 2025-03-18,18:35:57 | INFO | wandb_notes: 2025-03-18,18:35:57 | INFO | wandb_project_name: open-clip 2025-03-18,18:35:57 | INFO | warmup: 10000 2025-03-18,18:35:57 | INFO | wd: 0.1 2025-03-18,18:35:57 | INFO | workers: 1 2025-03-18,18:35:57 | INFO | world_size: 1 2025-03-18,18:35:57 | INFO | zeroshot_frequency: 1 2025-03-18,18:35:57 | INFO | Created AdamW (adamw) optimizer: lr: 0.0002, betas: (0.9, 0.999), eps: 1e-08, weight_decay: 0.1, amsgrad: False, maximize: False, foreach: None, capturable: False, differentiable: False, fused: None, decoupled_weight_decay: True 2025-03-18,18:35:59 | INFO | Overwrite dataset info from restored data version if exists. 2025-03-18,18:35:59 | INFO | Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/photonmz___opi_function_packed/default/0.0.0/58d0c1269d58ac8b94548cccb030cbeb9e97b397 2025-03-18,18:35:59 | INFO | Found cached dataset opi_function_packed (/home/ubuntu/.cache/huggingface/datasets/photonmz___opi_function_packed/default/0.0.0/58d0c1269d58ac8b94548cccb030cbeb9e97b397) 2025-03-18,18:35:59 | INFO | Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/photonmz___opi_function_packed/default/0.0.0/58d0c1269d58ac8b94548cccb030cbeb9e97b397 2025-03-18,18:36:00 | INFO | Overwrite dataset info from restored data version if exists. 2025-03-18,18:36:00 | INFO | Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/photonmz___opi_function_packed/default/0.0.0/58d0c1269d58ac8b94548cccb030cbeb9e97b397 2025-03-18,18:36:00 | INFO | Found cached dataset opi_function_packed (/home/ubuntu/.cache/huggingface/datasets/photonmz___opi_function_packed/default/0.0.0/58d0c1269d58ac8b94548cccb030cbeb9e97b397) 2025-03-18,18:36:00 | INFO | Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/photonmz___opi_function_packed/default/0.0.0/58d0c1269d58ac8b94548cccb030cbeb9e97b397 2025-03-18,18:36:01 | INFO | Start epoch 0 2025-03-18,18:36:03 | INFO | Train Epoch: 0 [ 32/766009 (0%)] Data (t): 0.195 Batch (t): 1.361, 23.5122/s, 23.5122/s/gpu LR: 0.000000 Logit Scale: 14.286 Contrastive_loss: 3.5366 (3.5366) Loss: 3.5366 (3.5366) 2025-03-18,18:36:24 | INFO | Train Epoch: 0 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 151.104/s, 151.104/s/gpu LR: 0.000002 Logit Scale: 14.286 Contrastive_loss: 3.1866 (3.3616) Loss: 3.1866 (3.3616) 2025-03-18,18:36:45 | INFO | Train Epoch: 0 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.385/s, 149.385/s/gpu LR: 0.000004 Logit Scale: 14.292 Contrastive_loss: 2.6773 (3.1335) Loss: 2.6773 (3.1335) 2025-03-18,18:37:07 | INFO | Train Epoch: 0 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 148.558/s, 148.558/s/gpu LR: 0.000006 Logit Scale: 14.303 Contrastive_loss: 2.0693 (2.8675) Loss: 2.0693 (2.8675) 2025-03-18,18:37:28 | INFO | Train Epoch: 0 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 149.448/s, 149.448/s/gpu LR: 0.000008 Logit Scale: 14.315 Contrastive_loss: 1.7336 (2.6407) Loss: 1.7336 (2.6407) 2025-03-18,18:37:50 | INFO | Train Epoch: 0 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.990/s, 149.990/s/gpu LR: 0.000010 Logit Scale: 14.330 Contrastive_loss: 1.3359 (2.4232) Loss: 1.3359 (2.4232) 2025-03-18,18:38:11 | INFO | Train Epoch: 0 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 147.498/s, 147.498/s/gpu LR: 0.000012 Logit Scale: 14.346 Contrastive_loss: 1.0595 (2.2284) Loss: 1.0595 (2.2284) 2025-03-18,18:38:33 | INFO | Train Epoch: 0 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 148.494/s, 148.494/s/gpu LR: 0.000014 Logit Scale: 14.364 Contrastive_loss: 1.7269 (2.1657) Loss: 1.7269 (2.1657) 2025-03-18,18:38:54 | INFO | Train Epoch: 0 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.461/s, 149.461/s/gpu LR: 0.000016 Logit Scale: 14.383 Contrastive_loss: 0.95371 (2.0311) Loss: 0.95371 (2.0311) 2025-03-18,18:39:16 | INFO | Train Epoch: 0 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 149.896/s, 149.896/s/gpu LR: 0.000018 Logit Scale: 14.401 Contrastive_loss: 0.81324 (1.9093) Loss: 0.81324 (1.9093) 2025-03-18,18:39:37 | INFO | Train Epoch: 0 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 149.670/s, 149.670/s/gpu LR: 0.000020 Logit Scale: 14.422 Contrastive_loss: 0.70392 (1.7997) Loss: 0.70392 (1.7997) 2025-03-18,18:39:59 | INFO | Train Epoch: 0 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.560/s, 149.560/s/gpu LR: 0.000022 Logit Scale: 14.445 Contrastive_loss: 0.92076 (1.7265) Loss: 0.92076 (1.7265) 2025-03-18,18:40:20 | INFO | Train Epoch: 0 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 148.134/s, 148.134/s/gpu LR: 0.000024 Logit Scale: 14.466 Contrastive_loss: 0.75298 (1.6516) Loss: 0.75298 (1.6516) 2025-03-18,18:40:42 | INFO | Train Epoch: 0 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 147.742/s, 147.742/s/gpu LR: 0.000026 Logit Scale: 14.490 Contrastive_loss: 0.82818 (1.5928) Loss: 0.82818 (1.5928) 2025-03-18,18:41:03 | INFO | Train Epoch: 0 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 149.580/s, 149.580/s/gpu LR: 0.000028 Logit Scale: 14.515 Contrastive_loss: 0.58722 (1.5257) Loss: 0.58722 (1.5257) 2025-03-18,18:41:25 | INFO | Train Epoch: 0 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.219, 146.251/s, 146.251/s/gpu LR: 0.000030 Logit Scale: 14.540 Contrastive_loss: 1.0969 (1.4989) Loss: 1.0969 (1.4989) 2025-03-18,18:41:47 | INFO | Train Epoch: 0 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.220, 145.668/s, 145.668/s/gpu LR: 0.000032 Logit Scale: 14.570 Contrastive_loss: 1.1598 (1.4790) Loss: 1.1598 (1.4790) 2025-03-18,18:42:09 | INFO | Train Epoch: 0 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.220, 145.490/s, 145.490/s/gpu LR: 0.000034 Logit Scale: 14.600 Contrastive_loss: 0.90433 (1.4470) Loss: 0.90433 (1.4470) 2025-03-18,18:42:31 | INFO | Train Epoch: 0 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.220, 146.810/s, 146.810/s/gpu LR: 0.000036 Logit Scale: 14.627 Contrastive_loss: 0.60070 (1.4025) Loss: 0.60070 (1.4025) 2025-03-18,18:42:53 | INFO | Train Epoch: 0 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.221, 145.110/s, 145.110/s/gpu LR: 0.000038 Logit Scale: 14.659 Contrastive_loss: 0.54631 (1.3597) Loss: 0.54631 (1.3597) 2025-03-18,18:43:16 | INFO | Train Epoch: 0 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.222, 143.674/s, 143.674/s/gpu LR: 0.000040 Logit Scale: 14.687 Contrastive_loss: 1.0657 (1.3457) Loss: 1.0657 (1.3457) 2025-03-18,18:43:38 | INFO | Train Epoch: 0 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.221, 144.264/s, 144.264/s/gpu LR: 0.000042 Logit Scale: 14.718 Contrastive_loss: 0.61521 (1.3125) Loss: 0.61521 (1.3125) 2025-03-18,18:44:00 | INFO | Train Epoch: 0 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.222, 147.994/s, 147.994/s/gpu LR: 0.000044 Logit Scale: 14.753 Contrastive_loss: 0.59076 (1.2811) Loss: 0.59076 (1.2811) 2025-03-18,18:44:22 | INFO | Train Epoch: 0 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.218, 147.232/s, 147.232/s/gpu LR: 0.000046 Logit Scale: 14.784 Contrastive_loss: 0.65319 (1.2549) Loss: 0.65319 (1.2549) 2025-03-18,18:44:44 | INFO | Train Epoch: 0 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 145.559/s, 145.559/s/gpu LR: 0.000048 Logit Scale: 14.818 Contrastive_loss: 0.87707 (1.2398) Loss: 0.87707 (1.2398) 2025-03-18,18:45:05 | INFO | Train Epoch: 0 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.213, 151.543/s, 151.543/s/gpu LR: 0.000050 Logit Scale: 14.856 Contrastive_loss: 0.77800 (1.2221) Loss: 0.77800 (1.2221) 2025-03-18,18:45:26 | INFO | Train Epoch: 0 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.211, 150.923/s, 150.923/s/gpu LR: 0.000052 Logit Scale: 14.895 Contrastive_loss: 0.78834 (1.2060) Loss: 0.78834 (1.2060) 2025-03-18,18:45:47 | INFO | Train Epoch: 0 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.212, 153.272/s, 153.272/s/gpu LR: 0.000054 Logit Scale: 14.935 Contrastive_loss: 0.54544 (1.1824) Loss: 0.54544 (1.1824) 2025-03-18,18:46:08 | INFO | Train Epoch: 0 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.212, 149.803/s, 149.803/s/gpu LR: 0.000056 Logit Scale: 14.973 Contrastive_loss: 0.77424 (1.1683) Loss: 0.77424 (1.1683) 2025-03-18,18:46:30 | INFO | Train Epoch: 0 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 146.491/s, 146.491/s/gpu LR: 0.000058 Logit Scale: 15.010 Contrastive_loss: 0.51591 (1.1466) Loss: 0.51591 (1.1466) 2025-03-18,18:46:52 | INFO | Train Epoch: 0 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.221, 148.739/s, 148.739/s/gpu LR: 0.000060 Logit Scale: 15.049 Contrastive_loss: 0.61581 (1.1295) Loss: 0.61581 (1.1295) 2025-03-18,18:47:14 | INFO | Train Epoch: 0 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 148.455/s, 148.455/s/gpu LR: 0.000062 Logit Scale: 15.092 Contrastive_loss: 0.45511 (1.1084) Loss: 0.45511 (1.1084) 2025-03-18,18:47:35 | INFO | Train Epoch: 0 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 151.035/s, 151.035/s/gpu LR: 0.000064 Logit Scale: 15.138 Contrastive_loss: 0.31943 (1.0845) Loss: 0.31943 (1.0845) 2025-03-18,18:47:57 | INFO | Train Epoch: 0 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.785/s, 148.785/s/gpu LR: 0.000066 Logit Scale: 15.183 Contrastive_loss: 0.75157 (1.0747) Loss: 0.75157 (1.0747) 2025-03-18,18:48:18 | INFO | Train Epoch: 0 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.533/s, 148.533/s/gpu LR: 0.000068 Logit Scale: 15.226 Contrastive_loss: 0.56892 (1.0602) Loss: 0.56892 (1.0602) 2025-03-18,18:48:40 | INFO | Train Epoch: 0 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.463/s, 148.463/s/gpu LR: 0.000070 Logit Scale: 15.270 Contrastive_loss: 1.0967 (1.0613) Loss: 1.0967 (1.0613) 2025-03-18,18:49:01 | INFO | Train Epoch: 0 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 148.287/s, 148.287/s/gpu LR: 0.000072 Logit Scale: 15.311 Contrastive_loss: 0.63214 (1.0497) Loss: 0.63214 (1.0497) 2025-03-18,18:49:23 | INFO | Train Epoch: 0 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.937/s, 149.937/s/gpu LR: 0.000074 Logit Scale: 15.355 Contrastive_loss: 0.41869 (1.0331) Loss: 0.41869 (1.0331) 2025-03-18,18:49:44 | INFO | Train Epoch: 0 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 144.864/s, 144.864/s/gpu LR: 0.000076 Logit Scale: 15.408 Contrastive_loss: 0.68632 (1.0242) Loss: 0.68632 (1.0242) 2025-03-18,18:50:07 | INFO | Train Epoch: 0 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.222, 145.099/s, 145.099/s/gpu LR: 0.000078 Logit Scale: 15.450 Contrastive_loss: 0.75660 (1.0175) Loss: 0.75660 (1.0175) 2025-03-18,18:50:28 | INFO | Train Epoch: 0 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 147.423/s, 147.423/s/gpu LR: 0.000080 Logit Scale: 15.506 Contrastive_loss: 0.67806 (1.0092) Loss: 0.67806 (1.0092) 2025-03-18,18:50:50 | INFO | Train Epoch: 0 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 149.754/s, 149.754/s/gpu LR: 0.000082 Logit Scale: 15.564 Contrastive_loss: 0.33340 (0.99311) Loss: 0.33340 (0.99311) 2025-03-18,18:51:12 | INFO | Train Epoch: 0 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 145.744/s, 145.744/s/gpu LR: 0.000084 Logit Scale: 15.610 Contrastive_loss: 0.81212 (0.98890) Loss: 0.81212 (0.98890) 2025-03-18,18:51:34 | INFO | Train Epoch: 0 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 151.307/s, 151.307/s/gpu LR: 0.000086 Logit Scale: 15.659 Contrastive_loss: 0.82178 (0.98510) Loss: 0.82178 (0.98510) 2025-03-18,18:51:55 | INFO | Train Epoch: 0 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 151.223/s, 151.223/s/gpu LR: 0.000088 Logit Scale: 15.699 Contrastive_loss: 0.58870 (0.97629) Loss: 0.58870 (0.97629) 2025-03-18,18:52:16 | INFO | Train Epoch: 0 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 145.176/s, 145.176/s/gpu LR: 0.000090 Logit Scale: 15.761 Contrastive_loss: 0.49782 (0.96589) Loss: 0.49782 (0.96589) 2025-03-18,18:52:38 | INFO | Train Epoch: 0 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 150.168/s, 150.168/s/gpu LR: 0.000092 Logit Scale: 15.823 Contrastive_loss: 0.98494 (0.96629) Loss: 0.98494 (0.96629) 2025-03-18,18:53:00 | INFO | Train Epoch: 0 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.219, 142.821/s, 142.821/s/gpu LR: 0.000094 Logit Scale: 15.867 Contrastive_loss: 0.44833 (0.95550) Loss: 0.44833 (0.95550) 2025-03-18,18:53:22 | INFO | Train Epoch: 0 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.221, 147.843/s, 147.843/s/gpu LR: 0.000096 Logit Scale: 15.921 Contrastive_loss: 0.43867 (0.94496) Loss: 0.43867 (0.94496) 2025-03-18,18:53:44 | INFO | Train Epoch: 0 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.026/s, 149.026/s/gpu LR: 0.000098 Logit Scale: 15.961 Contrastive_loss: 0.58488 (0.93775) Loss: 0.58488 (0.93775) 2025-03-18,18:54:05 | INFO | Train Epoch: 0 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 150.002/s, 150.002/s/gpu LR: 0.000100 Logit Scale: 16.020 Contrastive_loss: 0.70842 (0.93326) Loss: 0.70842 (0.93326) 2025-03-18,18:54:26 | INFO | Train Epoch: 0 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 151.019/s, 151.019/s/gpu LR: 0.000102 Logit Scale: 16.077 Contrastive_loss: 0.34912 (0.92202) Loss: 0.34912 (0.92202) 2025-03-18,18:54:48 | INFO | Train Epoch: 0 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 150.142/s, 150.142/s/gpu LR: 0.000104 Logit Scale: 16.123 Contrastive_loss: 1.2204 (0.92765) Loss: 1.2204 (0.92765) 2025-03-18,18:55:09 | INFO | Train Epoch: 0 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 150.868/s, 150.868/s/gpu LR: 0.000106 Logit Scale: 16.176 Contrastive_loss: 0.50988 (0.91992) Loss: 0.50988 (0.91992) 2025-03-18,18:55:31 | INFO | Train Epoch: 0 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.213, 151.290/s, 151.290/s/gpu LR: 0.000108 Logit Scale: 16.193 Contrastive_loss: 0.85286 (0.91870) Loss: 0.85286 (0.91870) 2025-03-18,18:55:53 | INFO | Train Epoch: 0 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 143.188/s, 143.188/s/gpu LR: 0.000110 Logit Scale: 16.242 Contrastive_loss: 0.52560 (0.91168) Loss: 0.52560 (0.91168) 2025-03-18,18:56:15 | INFO | Train Epoch: 0 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.220, 149.903/s, 149.903/s/gpu LR: 0.000112 Logit Scale: 16.254 Contrastive_loss: 0.94249 (0.91222) Loss: 0.94249 (0.91222) 2025-03-18,18:56:36 | INFO | Train Epoch: 0 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 145.673/s, 145.673/s/gpu LR: 0.000114 Logit Scale: 16.283 Contrastive_loss: 0.42598 (0.90384) Loss: 0.42598 (0.90384) 2025-03-18,18:56:58 | INFO | Train Epoch: 0 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 148.381/s, 148.381/s/gpu LR: 0.000116 Logit Scale: 16.335 Contrastive_loss: 0.74533 (0.90115) Loss: 0.74533 (0.90115) 2025-03-18,18:57:19 | INFO | Train Epoch: 0 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 148.910/s, 148.910/s/gpu LR: 0.000118 Logit Scale: 16.371 Contrastive_loss: 0.52350 (0.89486) Loss: 0.52350 (0.89486) 2025-03-18,18:57:41 | INFO | Train Epoch: 0 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 150.795/s, 150.795/s/gpu LR: 0.000120 Logit Scale: 16.418 Contrastive_loss: 0.60069 (0.89003) Loss: 0.60069 (0.89003) 2025-03-18,18:58:03 | INFO | Train Epoch: 0 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.220, 132.536/s, 132.536/s/gpu LR: 0.000122 Logit Scale: 16.465 Contrastive_loss: 0.61894 (0.88566) Loss: 0.61894 (0.88566) 2025-03-18,18:58:25 | INFO | Train Epoch: 0 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.220, 146.253/s, 146.253/s/gpu LR: 0.000124 Logit Scale: 16.523 Contrastive_loss: 0.75196 (0.88354) Loss: 0.75196 (0.88354) 2025-03-18,18:58:46 | INFO | Train Epoch: 0 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.217, 149.987/s, 149.987/s/gpu LR: 0.000126 Logit Scale: 16.561 Contrastive_loss: 0.65671 (0.87999) Loss: 0.65671 (0.87999) 2025-03-18,18:59:08 | INFO | Train Epoch: 0 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 152.063/s, 152.063/s/gpu LR: 0.000128 Logit Scale: 16.588 Contrastive_loss: 0.66792 (0.87673) Loss: 0.66792 (0.87673) 2025-03-18,18:59:29 | INFO | Train Epoch: 0 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.515/s, 149.515/s/gpu LR: 0.000130 Logit Scale: 16.650 Contrastive_loss: 0.50773 (0.87114) Loss: 0.50773 (0.87114) 2025-03-18,18:59:51 | INFO | Train Epoch: 0 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 146.171/s, 146.171/s/gpu LR: 0.000132 Logit Scale: 16.681 Contrastive_loss: 0.41085 (0.86427) Loss: 0.41085 (0.86427) 2025-03-18,19:00:13 | INFO | Train Epoch: 0 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 149.540/s, 149.540/s/gpu LR: 0.000134 Logit Scale: 16.735 Contrastive_loss: 0.90330 (0.86485) Loss: 0.90330 (0.86485) 2025-03-18,19:00:34 | INFO | Train Epoch: 0 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.319/s, 148.319/s/gpu LR: 0.000136 Logit Scale: 16.770 Contrastive_loss: 0.63140 (0.86146) Loss: 0.63140 (0.86146) 2025-03-18,19:00:55 | INFO | Train Epoch: 0 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 148.000/s, 148.000/s/gpu LR: 0.000138 Logit Scale: 16.798 Contrastive_loss: 0.59975 (0.85772) Loss: 0.59975 (0.85772) 2025-03-18,19:01:17 | INFO | Train Epoch: 0 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.646/s, 148.646/s/gpu LR: 0.000140 Logit Scale: 16.857 Contrastive_loss: 0.24062 (0.84903) Loss: 0.24062 (0.84903) 2025-03-18,19:01:39 | INFO | Train Epoch: 0 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 148.771/s, 148.771/s/gpu LR: 0.000142 Logit Scale: 16.899 Contrastive_loss: 0.33349 (0.84187) Loss: 0.33349 (0.84187) 2025-03-18,19:02:00 | INFO | Train Epoch: 0 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.444/s, 149.444/s/gpu LR: 0.000144 Logit Scale: 16.924 Contrastive_loss: 1.0546 (0.84479) Loss: 1.0546 (0.84479) 2025-03-18,19:02:22 | INFO | Train Epoch: 0 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 151.603/s, 151.603/s/gpu LR: 0.000146 Logit Scale: 16.953 Contrastive_loss: 0.93062 (0.84595) Loss: 0.93062 (0.84595) 2025-03-18,19:02:43 | INFO | Train Epoch: 0 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 145.115/s, 145.115/s/gpu LR: 0.000148 Logit Scale: 16.975 Contrastive_loss: 0.56164 (0.84215) Loss: 0.56164 (0.84215) 2025-03-18,19:03:05 | INFO | Train Epoch: 0 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 149.347/s, 149.347/s/gpu LR: 0.000150 Logit Scale: 17.038 Contrastive_loss: 0.41935 (0.83659) Loss: 0.41935 (0.83659) 2025-03-18,19:03:27 | INFO | Train Epoch: 0 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.220, 145.438/s, 145.438/s/gpu LR: 0.000152 Logit Scale: 17.091 Contrastive_loss: 0.63029 (0.83391) Loss: 0.63029 (0.83391) 2025-03-18,19:03:49 | INFO | Train Epoch: 0 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.219, 146.188/s, 146.188/s/gpu LR: 0.000154 Logit Scale: 17.139 Contrastive_loss: 0.46479 (0.82918) Loss: 0.46479 (0.82918) 2025-03-18,19:04:10 | INFO | Train Epoch: 0 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 149.979/s, 149.979/s/gpu LR: 0.000156 Logit Scale: 17.170 Contrastive_loss: 0.73758 (0.82802) Loss: 0.73758 (0.82802) 2025-03-18,19:04:32 | INFO | Train Epoch: 0 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 146.007/s, 146.007/s/gpu LR: 0.000158 Logit Scale: 17.228 Contrastive_loss: 0.71902 (0.82666) Loss: 0.71902 (0.82666) 2025-03-18,19:04:54 | INFO | Train Epoch: 0 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 150.145/s, 150.145/s/gpu LR: 0.000160 Logit Scale: 17.226 Contrastive_loss: 0.32917 (0.82052) Loss: 0.32917 (0.82052) 2025-03-18,19:05:16 | INFO | Train Epoch: 0 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.217, 151.165/s, 151.165/s/gpu LR: 0.000162 Logit Scale: 17.268 Contrastive_loss: 0.82181 (0.82053) Loss: 0.82181 (0.82053) 2025-03-18,19:05:37 | INFO | Train Epoch: 0 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.219, 143.644/s, 143.644/s/gpu LR: 0.000164 Logit Scale: 17.301 Contrastive_loss: 0.80872 (0.82039) Loss: 0.80872 (0.82039) 2025-03-18,19:05:59 | INFO | Train Epoch: 0 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.220, 145.949/s, 145.949/s/gpu LR: 0.000166 Logit Scale: 17.348 Contrastive_loss: 0.71698 (0.81916) Loss: 0.71698 (0.81916) 2025-03-18,19:06:21 | INFO | Train Epoch: 0 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.218, 148.523/s, 148.523/s/gpu LR: 0.000168 Logit Scale: 17.381 Contrastive_loss: 0.85756 (0.81961) Loss: 0.85756 (0.81961) 2025-03-18,19:06:43 | INFO | Train Epoch: 0 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.219, 145.903/s, 145.903/s/gpu LR: 0.000170 Logit Scale: 17.419 Contrastive_loss: 0.31816 (0.81378) Loss: 0.31816 (0.81378) 2025-03-18,19:07:05 | INFO | Train Epoch: 0 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.452/s, 149.452/s/gpu LR: 0.000172 Logit Scale: 17.463 Contrastive_loss: 0.51306 (0.81032) Loss: 0.51306 (0.81032) 2025-03-18,19:07:26 | INFO | Train Epoch: 0 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.387/s, 149.387/s/gpu LR: 0.000174 Logit Scale: 17.478 Contrastive_loss: 1.0768 (0.81335) Loss: 1.0768 (0.81335) 2025-03-18,19:07:48 | INFO | Train Epoch: 0 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 147.873/s, 147.873/s/gpu LR: 0.000176 Logit Scale: 17.481 Contrastive_loss: 1.1675 (0.81733) Loss: 1.1675 (0.81733) 2025-03-18,19:08:09 | INFO | Train Epoch: 0 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 149.402/s, 149.402/s/gpu LR: 0.000178 Logit Scale: 17.493 Contrastive_loss: 0.49808 (0.81378) Loss: 0.49808 (0.81378) 2025-03-18,19:08:31 | INFO | Train Epoch: 0 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 147.360/s, 147.360/s/gpu LR: 0.000180 Logit Scale: 17.502 Contrastive_loss: 0.63969 (0.81187) Loss: 0.63969 (0.81187) 2025-03-18,19:08:52 | INFO | Train Epoch: 0 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 150.242/s, 150.242/s/gpu LR: 0.000182 Logit Scale: 17.517 Contrastive_loss: 1.1302 (0.81533) Loss: 1.1302 (0.81533) 2025-03-18,19:09:14 | INFO | Train Epoch: 0 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 147.606/s, 147.606/s/gpu LR: 0.000184 Logit Scale: 17.539 Contrastive_loss: 0.72426 (0.81435) Loss: 0.72426 (0.81435) 2025-03-18,19:09:35 | INFO | Train Epoch: 0 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.217, 149.088/s, 149.088/s/gpu LR: 0.000186 Logit Scale: 17.573 Contrastive_loss: 0.97265 (0.81603) Loss: 0.97265 (0.81603) 2025-03-18,19:09:57 | INFO | Train Epoch: 0 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.229/s, 149.229/s/gpu LR: 0.000188 Logit Scale: 17.589 Contrastive_loss: 0.80656 (0.81593) Loss: 0.80656 (0.81593) 2025-03-18,19:10:19 | INFO | Train Epoch: 0 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 146.371/s, 146.371/s/gpu LR: 0.000190 Logit Scale: 17.619 Contrastive_loss: 0.44076 (0.81203) Loss: 0.44076 (0.81203) 2025-03-18,19:10:40 | INFO | Train Epoch: 0 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.218, 148.103/s, 148.103/s/gpu LR: 0.000192 Logit Scale: 17.653 Contrastive_loss: 1.1244 (0.81525) Loss: 1.1244 (0.81525) 2025-03-18,19:11:02 | INFO | Train Epoch: 0 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.219, 143.397/s, 143.397/s/gpu LR: 0.000194 Logit Scale: 17.685 Contrastive_loss: 0.86872 (0.81579) Loss: 0.86872 (0.81579) 2025-03-18,19:11:24 | INFO | Train Epoch: 0 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.775/s, 148.775/s/gpu LR: 0.000196 Logit Scale: 17.694 Contrastive_loss: 0.60628 (0.81368) Loss: 0.60628 (0.81368) 2025-03-18,19:11:45 | INFO | Train Epoch: 0 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 145.839/s, 145.839/s/gpu LR: 0.000198 Logit Scale: 17.716 Contrastive_loss: 0.86558 (0.81420) Loss: 0.86558 (0.81420) 2025-03-18,19:12:07 | INFO | Train Epoch: 0 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 150.752/s, 150.752/s/gpu LR: 0.000200 Logit Scale: 17.758 Contrastive_loss: 0.37387 (0.80984) Loss: 0.37387 (0.80984) 2025-03-18,19:12:28 | INFO | Train Epoch: 0 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.439/s, 149.439/s/gpu LR: 0.000200 Logit Scale: 17.758 Contrastive_loss: 0.40929 (0.80591) Loss: 0.40929 (0.80591) 2025-03-18,19:12:50 | INFO | Train Epoch: 0 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 145.680/s, 145.680/s/gpu LR: 0.000200 Logit Scale: 17.795 Contrastive_loss: 0.75008 (0.80537) Loss: 0.75008 (0.80537) 2025-03-18,19:13:11 | INFO | Train Epoch: 0 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 149.041/s, 149.041/s/gpu LR: 0.000200 Logit Scale: 17.857 Contrastive_loss: 0.24801 (0.80001) Loss: 0.24801 (0.80001) 2025-03-18,19:13:33 | INFO | Train Epoch: 0 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.034/s, 149.034/s/gpu LR: 0.000200 Logit Scale: 17.841 Contrastive_loss: 0.78107 (0.79983) Loss: 0.78107 (0.79983) 2025-03-18,19:13:54 | INFO | Train Epoch: 0 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 149.838/s, 149.838/s/gpu LR: 0.000200 Logit Scale: 17.832 Contrastive_loss: 0.96369 (0.80137) Loss: 0.96369 (0.80137) 2025-03-18,19:14:16 | INFO | Train Epoch: 0 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 147.843/s, 147.843/s/gpu LR: 0.000200 Logit Scale: 17.847 Contrastive_loss: 1.4150 (0.80711) Loss: 1.4150 (0.80711) 2025-03-18,19:14:38 | INFO | Train Epoch: 0 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 150.180/s, 150.180/s/gpu LR: 0.000200 Logit Scale: 17.855 Contrastive_loss: 1.0367 (0.80923) Loss: 1.0367 (0.80923) 2025-03-18,19:14:59 | INFO | Train Epoch: 0 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 150.776/s, 150.776/s/gpu LR: 0.000200 Logit Scale: 17.891 Contrastive_loss: 0.32325 (0.80478) Loss: 0.32325 (0.80478) 2025-03-18,19:15:20 | INFO | Train Epoch: 0 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 151.523/s, 151.523/s/gpu LR: 0.000200 Logit Scale: 17.910 Contrastive_loss: 1.0325 (0.80685) Loss: 1.0325 (0.80685) 2025-03-18,19:15:42 | INFO | Train Epoch: 0 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 144.798/s, 144.798/s/gpu LR: 0.000200 Logit Scale: 17.955 Contrastive_loss: 0.92910 (0.80795) Loss: 0.92910 (0.80795) 2025-03-18,19:16:04 | INFO | Train Epoch: 0 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.219, 147.715/s, 147.715/s/gpu LR: 0.000200 Logit Scale: 17.993 Contrastive_loss: 0.83439 (0.80818) Loss: 0.83439 (0.80818) 2025-03-18,19:16:26 | INFO | Train Epoch: 0 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 152.447/s, 152.447/s/gpu LR: 0.000200 Logit Scale: 18.010 Contrastive_loss: 1.0920 (0.81069) Loss: 1.0920 (0.81069) 2025-03-18,19:16:48 | INFO | Train Epoch: 0 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.218, 148.472/s, 148.472/s/gpu LR: 0.000200 Logit Scale: 18.055 Contrastive_loss: 0.72341 (0.80993) Loss: 0.72341 (0.80993) 2025-03-18,19:17:09 | INFO | Train Epoch: 0 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.915/s, 149.915/s/gpu LR: 0.000200 Logit Scale: 18.116 Contrastive_loss: 0.61425 (0.80823) Loss: 0.61425 (0.80823) 2025-03-18,19:17:31 | INFO | Train Epoch: 0 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.150/s, 148.150/s/gpu LR: 0.000200 Logit Scale: 18.163 Contrastive_loss: 0.51979 (0.80574) Loss: 0.51979 (0.80574) 2025-03-18,19:17:52 | INFO | Train Epoch: 0 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 147.370/s, 147.370/s/gpu LR: 0.000200 Logit Scale: 18.178 Contrastive_loss: 0.69751 (0.80482) Loss: 0.69751 (0.80482) 2025-03-18,19:18:14 | INFO | Train Epoch: 0 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.219, 148.466/s, 148.466/s/gpu LR: 0.000200 Logit Scale: 18.159 Contrastive_loss: 0.79138 (0.80470) Loss: 0.79138 (0.80470) 2025-03-18,19:18:36 | INFO | Train Epoch: 0 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 142.374/s, 142.374/s/gpu LR: 0.000200 Logit Scale: 18.185 Contrastive_loss: 0.33028 (0.80072) Loss: 0.33028 (0.80072) 2025-03-18,19:18:58 | INFO | Train Epoch: 0 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 149.330/s, 149.330/s/gpu LR: 0.000200 Logit Scale: 18.259 Contrastive_loss: 0.36630 (0.79710) Loss: 0.36630 (0.79710) 2025-03-18,19:19:19 | INFO | Train Epoch: 0 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 147.290/s, 147.290/s/gpu LR: 0.000200 Logit Scale: 18.248 Contrastive_loss: 0.48849 (0.79454) Loss: 0.48849 (0.79454) 2025-03-18,19:19:41 | INFO | Train Epoch: 0 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.401/s, 149.401/s/gpu LR: 0.000200 Logit Scale: 18.306 Contrastive_loss: 0.23085 (0.78992) Loss: 0.23085 (0.78992) 2025-03-18,19:20:02 | INFO | Train Epoch: 0 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.910/s, 149.910/s/gpu LR: 0.000200 Logit Scale: 18.337 Contrastive_loss: 0.59078 (0.78831) Loss: 0.59078 (0.78831) 2025-03-18,19:20:24 | INFO | Train Epoch: 0 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.362/s, 149.362/s/gpu LR: 0.000200 Logit Scale: 18.378 Contrastive_loss: 0.58292 (0.78665) Loss: 0.58292 (0.78665) 2025-03-18,19:20:45 | INFO | Train Epoch: 0 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 149.605/s, 149.605/s/gpu LR: 0.000200 Logit Scale: 18.393 Contrastive_loss: 0.63568 (0.78544) Loss: 0.63568 (0.78544) 2025-03-18,19:21:06 | INFO | Train Epoch: 0 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 150.922/s, 150.922/s/gpu LR: 0.000200 Logit Scale: 18.427 Contrastive_loss: 0.24206 (0.78113) Loss: 0.24206 (0.78113) 2025-03-18,19:21:28 | INFO | Train Epoch: 0 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 147.551/s, 147.551/s/gpu LR: 0.000200 Logit Scale: 18.431 Contrastive_loss: 1.0198 (0.78301) Loss: 1.0198 (0.78301) 2025-03-18,19:21:50 | INFO | Train Epoch: 0 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 147.753/s, 147.753/s/gpu LR: 0.000200 Logit Scale: 18.471 Contrastive_loss: 0.91681 (0.78405) Loss: 0.91681 (0.78405) 2025-03-18,19:22:11 | INFO | Train Epoch: 0 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 146.264/s, 146.264/s/gpu LR: 0.000200 Logit Scale: 18.514 Contrastive_loss: 0.59468 (0.78259) Loss: 0.59468 (0.78259) 2025-03-18,19:22:33 | INFO | Train Epoch: 0 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 150.966/s, 150.966/s/gpu LR: 0.000200 Logit Scale: 18.520 Contrastive_loss: 0.54956 (0.78079) Loss: 0.54956 (0.78079) 2025-03-18,19:22:54 | INFO | Train Epoch: 0 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.212, 150.149/s, 150.149/s/gpu LR: 0.000200 Logit Scale: 18.531 Contrastive_loss: 0.46011 (0.77835) Loss: 0.46011 (0.77835) 2025-03-18,19:23:15 | INFO | Train Epoch: 0 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 149.930/s, 149.930/s/gpu LR: 0.000200 Logit Scale: 18.557 Contrastive_loss: 0.49999 (0.77624) Loss: 0.49999 (0.77624) 2025-03-18,19:23:37 | INFO | Train Epoch: 0 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 149.801/s, 149.801/s/gpu LR: 0.000200 Logit Scale: 18.580 Contrastive_loss: 0.50498 (0.77420) Loss: 0.50498 (0.77420) 2025-03-18,19:23:58 | INFO | Train Epoch: 0 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 148.393/s, 148.393/s/gpu LR: 0.000200 Logit Scale: 18.596 Contrastive_loss: 0.82480 (0.77457) Loss: 0.82480 (0.77457) 2025-03-18,19:24:20 | INFO | Train Epoch: 0 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 150.270/s, 150.270/s/gpu LR: 0.000200 Logit Scale: 18.631 Contrastive_loss: 0.87828 (0.77534) Loss: 0.87828 (0.77534) 2025-03-18,19:24:42 | INFO | Train Epoch: 0 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 148.300/s, 148.300/s/gpu LR: 0.000200 Logit Scale: 18.651 Contrastive_loss: 0.25744 (0.77153) Loss: 0.25744 (0.77153) 2025-03-18,19:25:03 | INFO | Train Epoch: 0 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.213, 150.614/s, 150.614/s/gpu LR: 0.000200 Logit Scale: 18.662 Contrastive_loss: 0.23675 (0.76763) Loss: 0.23675 (0.76763) 2025-03-18,19:25:24 | INFO | Train Epoch: 0 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 145.266/s, 145.266/s/gpu LR: 0.000200 Logit Scale: 18.712 Contrastive_loss: 0.42240 (0.76513) Loss: 0.42240 (0.76513) 2025-03-18,19:25:46 | INFO | Train Epoch: 0 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 150.248/s, 150.248/s/gpu LR: 0.000200 Logit Scale: 18.749 Contrastive_loss: 0.66020 (0.76437) Loss: 0.66020 (0.76437) 2025-03-18,19:26:08 | INFO | Train Epoch: 0 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.376/s, 147.376/s/gpu LR: 0.000200 Logit Scale: 18.787 Contrastive_loss: 0.48221 (0.76236) Loss: 0.48221 (0.76236) 2025-03-18,19:26:29 | INFO | Train Epoch: 0 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.828/s, 149.828/s/gpu LR: 0.000200 Logit Scale: 18.836 Contrastive_loss: 0.59901 (0.76120) Loss: 0.59901 (0.76120) 2025-03-18,19:26:50 | INFO | Train Epoch: 0 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 148.720/s, 148.720/s/gpu LR: 0.000200 Logit Scale: 18.857 Contrastive_loss: 0.31144 (0.75803) Loss: 0.31144 (0.75803) 2025-03-18,19:27:12 | INFO | Train Epoch: 0 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.184/s, 149.184/s/gpu LR: 0.000200 Logit Scale: 18.869 Contrastive_loss: 0.65743 (0.75733) Loss: 0.65743 (0.75733) 2025-03-18,19:27:33 | INFO | Train Epoch: 0 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 148.913/s, 148.913/s/gpu LR: 0.000200 Logit Scale: 18.892 Contrastive_loss: 0.28980 (0.75408) Loss: 0.28980 (0.75408) 2025-03-18,19:27:55 | INFO | Train Epoch: 0 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 151.227/s, 151.227/s/gpu LR: 0.000200 Logit Scale: 18.839 Contrastive_loss: 0.32120 (0.75110) Loss: 0.32120 (0.75110) 2025-03-18,19:28:16 | INFO | Train Epoch: 0 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 150.244/s, 150.244/s/gpu LR: 0.000200 Logit Scale: 18.858 Contrastive_loss: 0.46939 (0.74917) Loss: 0.46939 (0.74917) 2025-03-18,19:28:38 | INFO | Train Epoch: 0 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 148.311/s, 148.311/s/gpu LR: 0.000200 Logit Scale: 18.896 Contrastive_loss: 0.83642 (0.74976) Loss: 0.83642 (0.74976) 2025-03-18,19:28:59 | INFO | Train Epoch: 0 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 147.588/s, 147.588/s/gpu LR: 0.000200 Logit Scale: 18.916 Contrastive_loss: 0.99545 (0.75142) Loss: 0.99545 (0.75142) 2025-03-18,19:29:21 | INFO | Train Epoch: 0 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 148.630/s, 148.630/s/gpu LR: 0.000200 Logit Scale: 18.941 Contrastive_loss: 0.57558 (0.75024) Loss: 0.57558 (0.75024) 2025-03-18,19:29:42 | INFO | Train Epoch: 0 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 147.522/s, 147.522/s/gpu LR: 0.000200 Logit Scale: 18.924 Contrastive_loss: 0.84312 (0.75086) Loss: 0.84312 (0.75086) 2025-03-18,19:30:04 | INFO | Train Epoch: 0 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 150.491/s, 150.491/s/gpu LR: 0.000200 Logit Scale: 18.973 Contrastive_loss: 0.43014 (0.74874) Loss: 0.43014 (0.74874) 2025-03-18,19:30:25 | INFO | Train Epoch: 0 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.217, 142.740/s, 142.740/s/gpu LR: 0.000200 Logit Scale: 18.983 Contrastive_loss: 0.71170 (0.74849) Loss: 0.71170 (0.74849) 2025-03-18,19:30:47 | INFO | Train Epoch: 0 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.220, 143.407/s, 143.407/s/gpu LR: 0.000200 Logit Scale: 19.011 Contrastive_loss: 0.58300 (0.74741) Loss: 0.58300 (0.74741) 2025-03-18,19:31:09 | INFO | Train Epoch: 0 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.220, 147.551/s, 147.551/s/gpu LR: 0.000200 Logit Scale: 19.028 Contrastive_loss: 0.37528 (0.74500) Loss: 0.37528 (0.74500) 2025-03-18,19:31:31 | INFO | Train Epoch: 0 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.220, 143.652/s, 143.652/s/gpu LR: 0.000200 Logit Scale: 19.036 Contrastive_loss: 0.87728 (0.74585) Loss: 0.87728 (0.74585) 2025-03-18,19:31:54 | INFO | Train Epoch: 0 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.222, 143.364/s, 143.364/s/gpu LR: 0.000200 Logit Scale: 19.073 Contrastive_loss: 0.62027 (0.74504) Loss: 0.62027 (0.74504) 2025-03-18,19:32:16 | INFO | Train Epoch: 0 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.220, 142.716/s, 142.716/s/gpu LR: 0.000200 Logit Scale: 19.090 Contrastive_loss: 0.49027 (0.74342) Loss: 0.49027 (0.74342) 2025-03-18,19:32:38 | INFO | Train Epoch: 0 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 147.178/s, 147.178/s/gpu LR: 0.000200 Logit Scale: 19.135 Contrastive_loss: 0.83216 (0.74398) Loss: 0.83216 (0.74398) 2025-03-18,19:32:59 | INFO | Train Epoch: 0 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.218, 147.360/s, 147.360/s/gpu LR: 0.000200 Logit Scale: 19.180 Contrastive_loss: 0.24587 (0.74085) Loss: 0.24587 (0.74085) 2025-03-18,19:33:21 | INFO | Train Epoch: 0 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 145.274/s, 145.274/s/gpu LR: 0.000200 Logit Scale: 19.212 Contrastive_loss: 0.45218 (0.73905) Loss: 0.45218 (0.73905) 2025-03-18,19:33:43 | INFO | Train Epoch: 0 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.220, 145.987/s, 145.987/s/gpu LR: 0.000200 Logit Scale: 19.224 Contrastive_loss: 0.55304 (0.73789) Loss: 0.55304 (0.73789) 2025-03-18,19:34:05 | INFO | Train Epoch: 0 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 149.479/s, 149.479/s/gpu LR: 0.000200 Logit Scale: 19.225 Contrastive_loss: 0.83290 (0.73848) Loss: 0.83290 (0.73848) 2025-03-18,19:34:26 | INFO | Train Epoch: 0 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 149.377/s, 149.377/s/gpu LR: 0.000200 Logit Scale: 19.227 Contrastive_loss: 0.60399 (0.73765) Loss: 0.60399 (0.73765) 2025-03-18,19:34:48 | INFO | Train Epoch: 0 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 142.289/s, 142.289/s/gpu LR: 0.000200 Logit Scale: 19.203 Contrastive_loss: 0.74767 (0.73771) Loss: 0.74767 (0.73771) 2025-03-18,19:35:09 | INFO | Train Epoch: 0 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 145.262/s, 145.262/s/gpu LR: 0.000200 Logit Scale: 19.248 Contrastive_loss: 0.66702 (0.73728) Loss: 0.66702 (0.73728) 2025-03-18,19:35:31 | INFO | Train Epoch: 0 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 144.178/s, 144.178/s/gpu LR: 0.000200 Logit Scale: 19.318 Contrastive_loss: 0.22974 (0.73423) Loss: 0.22974 (0.73423) 2025-03-18,19:35:54 | INFO | Train Epoch: 0 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 148.992/s, 148.992/s/gpu LR: 0.000200 Logit Scale: 19.332 Contrastive_loss: 0.53186 (0.73301) Loss: 0.53186 (0.73301) 2025-03-18,19:36:15 | INFO | Train Epoch: 0 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.220, 145.076/s, 145.076/s/gpu LR: 0.000200 Logit Scale: 19.346 Contrastive_loss: 0.43247 (0.73123) Loss: 0.43247 (0.73123) 2025-03-18,19:36:37 | INFO | Train Epoch: 0 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.220, 148.358/s, 148.358/s/gpu LR: 0.000200 Logit Scale: 19.385 Contrastive_loss: 0.53458 (0.73006) Loss: 0.53458 (0.73006) 2025-03-18,19:36:59 | INFO | Train Epoch: 0 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 147.947/s, 147.947/s/gpu LR: 0.000200 Logit Scale: 19.380 Contrastive_loss: 0.22109 (0.72707) Loss: 0.22109 (0.72707) 2025-03-18,19:37:21 | INFO | Train Epoch: 0 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 147.246/s, 147.246/s/gpu LR: 0.000200 Logit Scale: 19.412 Contrastive_loss: 0.56506 (0.72612) Loss: 0.56506 (0.72612) 2025-03-18,19:37:43 | INFO | Train Epoch: 0 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.218, 147.396/s, 147.396/s/gpu LR: 0.000200 Logit Scale: 19.421 Contrastive_loss: 0.61271 (0.72546) Loss: 0.61271 (0.72546) 2025-03-18,19:38:05 | INFO | Train Epoch: 0 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.222, 143.204/s, 143.204/s/gpu LR: 0.000200 Logit Scale: 19.414 Contrastive_loss: 0.66915 (0.72514) Loss: 0.66915 (0.72514) 2025-03-18,19:38:27 | INFO | Train Epoch: 0 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.221, 144.854/s, 144.854/s/gpu LR: 0.000200 Logit Scale: 19.416 Contrastive_loss: 0.35829 (0.72303) Loss: 0.35829 (0.72303) 2025-03-18,19:38:49 | INFO | Train Epoch: 0 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.220, 144.843/s, 144.843/s/gpu LR: 0.000200 Logit Scale: 19.434 Contrastive_loss: 0.46200 (0.72154) Loss: 0.46200 (0.72154) 2025-03-18,19:39:11 | INFO | Train Epoch: 0 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.221, 145.435/s, 145.435/s/gpu LR: 0.000200 Logit Scale: 19.473 Contrastive_loss: 0.68142 (0.72131) Loss: 0.68142 (0.72131) 2025-03-18,19:39:33 | INFO | Train Epoch: 0 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.221, 145.492/s, 145.492/s/gpu LR: 0.000200 Logit Scale: 19.498 Contrastive_loss: 0.50231 (0.72007) Loss: 0.50231 (0.72007) 2025-03-18,19:39:55 | INFO | Train Epoch: 0 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.221, 148.901/s, 148.901/s/gpu LR: 0.000200 Logit Scale: 19.519 Contrastive_loss: 0.95070 (0.72137) Loss: 0.95070 (0.72137) 2025-03-18,19:40:17 | INFO | Train Epoch: 0 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.217, 148.079/s, 148.079/s/gpu LR: 0.000200 Logit Scale: 19.532 Contrastive_loss: 0.54841 (0.72040) Loss: 0.54841 (0.72040) 2025-03-18,19:40:39 | INFO | Train Epoch: 0 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.218, 147.598/s, 147.598/s/gpu LR: 0.000200 Logit Scale: 19.559 Contrastive_loss: 0.84969 (0.72112) Loss: 0.84969 (0.72112) 2025-03-18,19:41:01 | INFO | Train Epoch: 0 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.220, 144.247/s, 144.247/s/gpu LR: 0.000200 Logit Scale: 19.603 Contrastive_loss: 0.72807 (0.72116) Loss: 0.72807 (0.72116) 2025-03-18,19:41:23 | INFO | Train Epoch: 0 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.220, 146.612/s, 146.612/s/gpu LR: 0.000200 Logit Scale: 19.669 Contrastive_loss: 0.34024 (0.71906) Loss: 0.34024 (0.71906) 2025-03-18,19:41:45 | INFO | Train Epoch: 0 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 147.895/s, 147.895/s/gpu LR: 0.000200 Logit Scale: 19.650 Contrastive_loss: 0.46127 (0.71766) Loss: 0.46127 (0.71766) 2025-03-18,19:42:07 | INFO | Train Epoch: 0 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 146.302/s, 146.302/s/gpu LR: 0.000200 Logit Scale: 19.617 Contrastive_loss: 0.74948 (0.71783) Loss: 0.74948 (0.71783) 2025-03-18,19:42:29 | INFO | Train Epoch: 0 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.219, 146.970/s, 146.970/s/gpu LR: 0.000200 Logit Scale: 19.640 Contrastive_loss: 0.20940 (0.71508) Loss: 0.20940 (0.71508) 2025-03-18,19:42:51 | INFO | Train Epoch: 0 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.219, 146.896/s, 146.896/s/gpu LR: 0.000200 Logit Scale: 19.661 Contrastive_loss: 0.39647 (0.71337) Loss: 0.39647 (0.71337) 2025-03-18,19:43:13 | INFO | Train Epoch: 0 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.223, 144.083/s, 144.083/s/gpu LR: 0.000200 Logit Scale: 19.673 Contrastive_loss: 0.47892 (0.71211) Loss: 0.47892 (0.71211) 2025-03-18,19:43:35 | INFO | Train Epoch: 0 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.220, 145.883/s, 145.883/s/gpu LR: 0.000200 Logit Scale: 19.679 Contrastive_loss: 0.58141 (0.71142) Loss: 0.58141 (0.71142) 2025-03-18,19:43:57 | INFO | Train Epoch: 0 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.217, 147.225/s, 147.225/s/gpu LR: 0.000200 Logit Scale: 19.706 Contrastive_loss: 0.081659 (0.70809) Loss: 0.081659 (0.70809) 2025-03-18,19:44:18 | INFO | Train Epoch: 0 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.219, 142.796/s, 142.796/s/gpu LR: 0.000200 Logit Scale: 19.754 Contrastive_loss: 0.39798 (0.70645) Loss: 0.39798 (0.70645) 2025-03-18,19:44:40 | INFO | Train Epoch: 0 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.219, 147.067/s, 147.067/s/gpu LR: 0.000200 Logit Scale: 19.777 Contrastive_loss: 0.18973 (0.70375) Loss: 0.18973 (0.70375) 2025-03-18,19:45:02 | INFO | Train Epoch: 0 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.220, 147.201/s, 147.201/s/gpu LR: 0.000200 Logit Scale: 19.802 Contrastive_loss: 0.52666 (0.70283) Loss: 0.52666 (0.70283) 2025-03-18,19:45:24 | INFO | Train Epoch: 0 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.220, 145.590/s, 145.590/s/gpu LR: 0.000200 Logit Scale: 19.825 Contrastive_loss: 0.57438 (0.70216) Loss: 0.57438 (0.70216) 2025-03-18,19:45:46 | INFO | Train Epoch: 0 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 146.221/s, 146.221/s/gpu LR: 0.000200 Logit Scale: 19.832 Contrastive_loss: 0.73556 (0.70233) Loss: 0.73556 (0.70233) 2025-03-18,19:46:08 | INFO | Train Epoch: 0 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 147.167/s, 147.167/s/gpu LR: 0.000200 Logit Scale: 19.857 Contrastive_loss: 0.26224 (0.70008) Loss: 0.26224 (0.70008) 2025-03-18,19:46:30 | INFO | Train Epoch: 0 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 145.451/s, 145.451/s/gpu LR: 0.000200 Logit Scale: 19.882 Contrastive_loss: 0.60935 (0.69961) Loss: 0.60935 (0.69961) 2025-03-18,19:46:52 | INFO | Train Epoch: 0 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.221, 145.552/s, 145.552/s/gpu LR: 0.000200 Logit Scale: 19.887 Contrastive_loss: 0.43475 (0.69827) Loss: 0.43475 (0.69827) 2025-03-18,19:47:14 | INFO | Train Epoch: 0 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.221, 144.011/s, 144.011/s/gpu LR: 0.000200 Logit Scale: 19.880 Contrastive_loss: 0.53412 (0.69744) Loss: 0.53412 (0.69744) 2025-03-18,19:47:36 | INFO | Train Epoch: 0 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.219, 144.010/s, 144.010/s/gpu LR: 0.000200 Logit Scale: 19.926 Contrastive_loss: 0.44397 (0.69617) Loss: 0.44397 (0.69617) 2025-03-18,19:47:58 | INFO | Train Epoch: 0 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.221, 147.250/s, 147.250/s/gpu LR: 0.000200 Logit Scale: 19.942 Contrastive_loss: 0.50428 (0.69521) Loss: 0.50428 (0.69521) 2025-03-18,19:48:20 | INFO | Train Epoch: 0 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.220, 145.851/s, 145.851/s/gpu LR: 0.000200 Logit Scale: 19.952 Contrastive_loss: 1.1045 (0.69724) Loss: 1.1045 (0.69724) 2025-03-18,19:48:43 | INFO | Train Epoch: 0 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.222, 142.375/s, 142.375/s/gpu LR: 0.000200 Logit Scale: 19.944 Contrastive_loss: 0.37429 (0.69564) Loss: 0.37429 (0.69564) 2025-03-18,19:49:05 | INFO | Train Epoch: 0 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.221, 145.568/s, 145.568/s/gpu LR: 0.000200 Logit Scale: 19.952 Contrastive_loss: 0.61086 (0.69523) Loss: 0.61086 (0.69523) 2025-03-18,19:49:27 | INFO | Train Epoch: 0 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.222, 144.517/s, 144.517/s/gpu LR: 0.000200 Logit Scale: 19.984 Contrastive_loss: 0.80362 (0.69576) Loss: 0.80362 (0.69576) 2025-03-18,19:49:49 | INFO | Train Epoch: 0 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.219, 145.078/s, 145.078/s/gpu LR: 0.000200 Logit Scale: 19.996 Contrastive_loss: 0.25310 (0.69360) Loss: 0.25310 (0.69360) 2025-03-18,19:50:11 | INFO | Train Epoch: 0 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.219, 148.565/s, 148.565/s/gpu LR: 0.000200 Logit Scale: 19.996 Contrastive_loss: 0.57574 (0.69303) Loss: 0.57574 (0.69303) 2025-03-18,19:50:32 | INFO | Train Epoch: 0 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 146.074/s, 146.074/s/gpu LR: 0.000200 Logit Scale: 20.053 Contrastive_loss: 0.30819 (0.69117) Loss: 0.30819 (0.69117) 2025-03-18,19:50:54 | INFO | Train Epoch: 0 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 147.387/s, 147.387/s/gpu LR: 0.000200 Logit Scale: 20.047 Contrastive_loss: 0.15533 (0.68859) Loss: 0.15533 (0.68859) 2025-03-18,19:51:15 | INFO | Train Epoch: 0 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 152.418/s, 152.418/s/gpu LR: 0.000200 Logit Scale: 20.078 Contrastive_loss: 0.92203 (0.68971) Loss: 0.92203 (0.68971) 2025-03-18,19:51:37 | INFO | Train Epoch: 0 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.212, 149.854/s, 149.854/s/gpu LR: 0.000200 Logit Scale: 20.125 Contrastive_loss: 0.27173 (0.68772) Loss: 0.27173 (0.68772) 2025-03-18,19:51:58 | INFO | Train Epoch: 0 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.212, 151.271/s, 151.271/s/gpu LR: 0.000200 Logit Scale: 20.179 Contrastive_loss: 0.63226 (0.68745) Loss: 0.63226 (0.68745) 2025-03-18,19:52:19 | INFO | Train Epoch: 0 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.212, 149.733/s, 149.733/s/gpu LR: 0.000200 Logit Scale: 20.192 Contrastive_loss: 0.78775 (0.68793) Loss: 0.78775 (0.68793) 2025-03-18,19:52:41 | INFO | Train Epoch: 0 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.585/s, 148.585/s/gpu LR: 0.000200 Logit Scale: 20.156 Contrastive_loss: 0.78168 (0.68837) Loss: 0.78168 (0.68837) 2025-03-18,19:53:02 | INFO | Train Epoch: 0 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.213, 151.764/s, 151.764/s/gpu LR: 0.000200 Logit Scale: 20.171 Contrastive_loss: 0.69113 (0.68838) Loss: 0.69113 (0.68838) 2025-03-18,19:53:23 | INFO | Train Epoch: 0 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 150.371/s, 150.371/s/gpu LR: 0.000200 Logit Scale: 20.195 Contrastive_loss: 0.53751 (0.68768) Loss: 0.53751 (0.68768) 2025-03-18,19:53:45 | INFO | Train Epoch: 0 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 149.500/s, 149.500/s/gpu LR: 0.000200 Logit Scale: 20.198 Contrastive_loss: 0.52468 (0.68692) Loss: 0.52468 (0.68692) 2025-03-18,19:54:06 | INFO | Train Epoch: 0 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.000/s, 150.000/s/gpu LR: 0.000200 Logit Scale: 20.250 Contrastive_loss: 0.69789 (0.68698) Loss: 0.69789 (0.68698) 2025-03-18,19:54:28 | INFO | Train Epoch: 0 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 149.660/s, 149.660/s/gpu LR: 0.000200 Logit Scale: 20.263 Contrastive_loss: 0.18287 (0.68466) Loss: 0.18287 (0.68466) 2025-03-18,19:54:49 | INFO | Train Epoch: 0 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 147.686/s, 147.686/s/gpu LR: 0.000200 Logit Scale: 20.307 Contrastive_loss: 0.33479 (0.68307) Loss: 0.33479 (0.68307) 2025-03-18,19:55:11 | INFO | Train Epoch: 0 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 150.201/s, 150.201/s/gpu LR: 0.000200 Logit Scale: 20.334 Contrastive_loss: 0.44668 (0.68199) Loss: 0.44668 (0.68199) 2025-03-18,19:55:32 | INFO | Train Epoch: 0 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 149.890/s, 149.890/s/gpu LR: 0.000200 Logit Scale: 20.358 Contrastive_loss: 0.30876 (0.68030) Loss: 0.30876 (0.68030) 2025-03-18,19:55:54 | INFO | Train Epoch: 0 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 145.425/s, 145.425/s/gpu LR: 0.000200 Logit Scale: 20.383 Contrastive_loss: 0.51766 (0.67957) Loss: 0.51766 (0.67957) 2025-03-18,19:56:15 | INFO | Train Epoch: 0 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 150.596/s, 150.596/s/gpu LR: 0.000200 Logit Scale: 20.365 Contrastive_loss: 0.51772 (0.67884) Loss: 0.51772 (0.67884) 2025-03-18,19:56:37 | INFO | Train Epoch: 0 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 148.924/s, 148.924/s/gpu LR: 0.000200 Logit Scale: 20.375 Contrastive_loss: 0.83076 (0.67952) Loss: 0.83076 (0.67952) 2025-03-18,19:56:58 | INFO | Train Epoch: 0 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 149.314/s, 149.314/s/gpu LR: 0.000200 Logit Scale: 20.399 Contrastive_loss: 0.30600 (0.67786) Loss: 0.30600 (0.67786) 2025-03-18,19:57:20 | INFO | Train Epoch: 0 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.759/s, 149.759/s/gpu LR: 0.000200 Logit Scale: 20.450 Contrastive_loss: 0.55970 (0.67734) Loss: 0.55970 (0.67734) 2025-03-18,19:57:41 | INFO | Train Epoch: 0 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 144.725/s, 144.725/s/gpu LR: 0.000200 Logit Scale: 20.471 Contrastive_loss: 0.27836 (0.67558) Loss: 0.27836 (0.67558) 2025-03-18,19:58:03 | INFO | Train Epoch: 0 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.220, 146.841/s, 146.841/s/gpu LR: 0.000200 Logit Scale: 20.488 Contrastive_loss: 0.57129 (0.67512) Loss: 0.57129 (0.67512) 2025-03-18,19:58:25 | INFO | Train Epoch: 0 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.220, 145.104/s, 145.104/s/gpu LR: 0.000200 Logit Scale: 20.514 Contrastive_loss: 0.71709 (0.67531) Loss: 0.71709 (0.67531) 2025-03-18,19:58:47 | INFO | Train Epoch: 0 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.221, 146.050/s, 146.050/s/gpu LR: 0.000200 Logit Scale: 20.548 Contrastive_loss: 0.83404 (0.67600) Loss: 0.83404 (0.67600) 2025-03-18,19:59:09 | INFO | Train Epoch: 0 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.219, 147.872/s, 147.872/s/gpu LR: 0.000200 Logit Scale: 20.577 Contrastive_loss: 0.42530 (0.67491) Loss: 0.42530 (0.67491) 2025-03-18,19:59:31 | INFO | Train Epoch: 0 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 146.786/s, 146.786/s/gpu LR: 0.000200 Logit Scale: 20.586 Contrastive_loss: 0.78899 (0.67540) Loss: 0.78899 (0.67540) 2025-03-18,19:59:52 | INFO | Train Epoch: 0 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 148.332/s, 148.332/s/gpu LR: 0.000200 Logit Scale: 20.597 Contrastive_loss: 0.81933 (0.67602) Loss: 0.81933 (0.67602) 2025-03-18,20:00:14 | INFO | Train Epoch: 0 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.217, 150.847/s, 150.847/s/gpu LR: 0.000200 Logit Scale: 20.619 Contrastive_loss: 0.18569 (0.67393) Loss: 0.18569 (0.67393) 2025-03-18,20:00:35 | INFO | Train Epoch: 0 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 149.649/s, 149.649/s/gpu LR: 0.000200 Logit Scale: 20.591 Contrastive_loss: 0.77670 (0.67436) Loss: 0.77670 (0.67436) 2025-03-18,20:00:57 | INFO | Train Epoch: 0 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 144.803/s, 144.803/s/gpu LR: 0.000200 Logit Scale: 20.597 Contrastive_loss: 0.57767 (0.67395) Loss: 0.57767 (0.67395) 2025-03-18,20:01:19 | INFO | Train Epoch: 0 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.219, 141.885/s, 141.885/s/gpu LR: 0.000200 Logit Scale: 20.617 Contrastive_loss: 0.39604 (0.67278) Loss: 0.39604 (0.67278) 2025-03-18,20:01:41 | INFO | Train Epoch: 0 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.220, 143.221/s, 143.221/s/gpu LR: 0.000200 Logit Scale: 20.610 Contrastive_loss: 0.27170 (0.67110) Loss: 0.27170 (0.67110) 2025-03-18,20:02:03 | INFO | Train Epoch: 0 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.221, 146.683/s, 146.683/s/gpu LR: 0.000200 Logit Scale: 20.664 Contrastive_loss: 0.25488 (0.66935) Loss: 0.25488 (0.66935) 2025-03-18,20:02:25 | INFO | Train Epoch: 0 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.218, 148.100/s, 148.100/s/gpu LR: 0.000200 Logit Scale: 20.665 Contrastive_loss: 0.45180 (0.66845) Loss: 0.45180 (0.66845) 2025-03-18,20:02:33 | INFO | Train Epoch: 0 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.219, 146.874/s, 146.874/s/gpu LR: 0.000200 Logit Scale: 20.675 Contrastive_loss: 0.75342 (0.66880) Loss: 0.75342 (0.66880) 2025-03-18,20:02:33 | INFO | Eval Epoch: 1 [32 / 7443] Clip Loss: 3.269798 2025-03-18,20:02:39 | INFO | Eval Epoch: 1 [3232 / 7443] Clip Loss: 1.113828 2025-03-18,20:02:45 | INFO | Eval Epoch: 1 [6432 / 7443] Clip Loss: 0.877663 2025-03-18,20:02:47 | INFO | Eval Epoch: 1 image_to_text_mean_rank: 150.0777 image_to_text_median_rank: 11.0000 image_to_text_R@1: 0.0871 image_to_text_R@5: 0.3272 image_to_text_R@10: 0.4866 text_to_image_mean_rank: 106.4135 text_to_image_median_rank: 11.0000 text_to_image_R@1: 0.0910 text_to_image_R@5: 0.3257 text_to_image_R@10: 0.4874 clip_val_loss: 0.8324 epoch: 1.0000 num_samples: 7443.0000 2025-03-18,20:03:13 | INFO | Start epoch 1 2025-03-18,20:03:14 | INFO | Train Epoch: 1 [ 32/766009 (0%)] Data (t): 0.177 Batch (t): 0.387, 82.7842/s, 82.7842/s/gpu LR: 0.000200 Logit Scale: 20.673 Contrastive_loss: 0.60600 (0.60600) Loss: 0.60600 (0.60600) 2025-03-18,20:03:35 | INFO | Train Epoch: 1 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.216, 148.386/s, 148.386/s/gpu LR: 0.000200 Logit Scale: 20.715 Contrastive_loss: 0.24411 (0.42506) Loss: 0.24411 (0.42506) 2025-03-18,20:03:57 | INFO | Train Epoch: 1 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.571/s, 149.571/s/gpu LR: 0.000200 Logit Scale: 20.730 Contrastive_loss: 0.49773 (0.44928) Loss: 0.49773 (0.44928) 2025-03-18,20:04:18 | INFO | Train Epoch: 1 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 149.706/s, 149.706/s/gpu LR: 0.000200 Logit Scale: 20.738 Contrastive_loss: 0.54460 (0.47311) Loss: 0.54460 (0.47311) 2025-03-18,20:04:40 | INFO | Train Epoch: 1 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 147.383/s, 147.383/s/gpu LR: 0.000200 Logit Scale: 20.751 Contrastive_loss: 0.48455 (0.47540) Loss: 0.48455 (0.47540) 2025-03-18,20:05:02 | INFO | Train Epoch: 1 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.217, 149.145/s, 149.145/s/gpu LR: 0.000200 Logit Scale: 20.795 Contrastive_loss: 0.67233 (0.50822) Loss: 0.67233 (0.50822) 2025-03-18,20:05:23 | INFO | Train Epoch: 1 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.218, 148.183/s, 148.183/s/gpu LR: 0.000200 Logit Scale: 20.846 Contrastive_loss: 0.40494 (0.49347) Loss: 0.40494 (0.49347) 2025-03-18,20:05:45 | INFO | Train Epoch: 1 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 146.085/s, 146.085/s/gpu LR: 0.000200 Logit Scale: 20.899 Contrastive_loss: 0.27304 (0.46591) Loss: 0.27304 (0.46591) 2025-03-18,20:06:07 | INFO | Train Epoch: 1 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 143.922/s, 143.922/s/gpu LR: 0.000200 Logit Scale: 20.924 Contrastive_loss: 0.66164 (0.48766) Loss: 0.66164 (0.48766) 2025-03-18,20:06:28 | INFO | Train Epoch: 1 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 145.152/s, 145.152/s/gpu LR: 0.000200 Logit Scale: 20.944 Contrastive_loss: 0.54579 (0.49347) Loss: 0.54579 (0.49347) 2025-03-18,20:06:50 | INFO | Train Epoch: 1 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.222, 132.516/s, 132.516/s/gpu LR: 0.000200 Logit Scale: 20.953 Contrastive_loss: 0.45719 (0.49017) Loss: 0.45719 (0.49017) 2025-03-18,20:07:12 | INFO | Train Epoch: 1 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.220, 132.361/s, 132.361/s/gpu LR: 0.000200 Logit Scale: 20.987 Contrastive_loss: 0.59306 (0.49875) Loss: 0.59306 (0.49875) 2025-03-18,20:07:34 | INFO | Train Epoch: 1 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.222, 144.781/s, 144.781/s/gpu LR: 0.000200 Logit Scale: 21.009 Contrastive_loss: 0.24926 (0.47956) Loss: 0.24926 (0.47956) 2025-03-18,20:07:56 | INFO | Train Epoch: 1 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 153.734/s, 153.734/s/gpu LR: 0.000200 Logit Scale: 21.006 Contrastive_loss: 0.28319 (0.46553) Loss: 0.28319 (0.46553) 2025-03-18,20:08:18 | INFO | Train Epoch: 1 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.220, 148.107/s, 148.107/s/gpu LR: 0.000200 Logit Scale: 21.006 Contrastive_loss: 0.26265 (0.45201) Loss: 0.26265 (0.45201) 2025-03-18,20:08:40 | INFO | Train Epoch: 1 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.217, 148.276/s, 148.276/s/gpu LR: 0.000200 Logit Scale: 21.020 Contrastive_loss: 0.58175 (0.46011) Loss: 0.58175 (0.46011) 2025-03-18,20:09:01 | INFO | Train Epoch: 1 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 142.065/s, 142.065/s/gpu LR: 0.000200 Logit Scale: 20.977 Contrastive_loss: 0.49367 (0.46209) Loss: 0.49367 (0.46209) 2025-03-18,20:09:23 | INFO | Train Epoch: 1 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 147.938/s, 147.938/s/gpu LR: 0.000200 Logit Scale: 21.037 Contrastive_loss: 0.66537 (0.47338) Loss: 0.66537 (0.47338) 2025-03-18,20:09:45 | INFO | Train Epoch: 1 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.813/s, 149.813/s/gpu LR: 0.000200 Logit Scale: 21.019 Contrastive_loss: 0.49602 (0.47457) Loss: 0.49602 (0.47457) 2025-03-18,20:10:06 | INFO | Train Epoch: 1 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 148.586/s, 148.586/s/gpu LR: 0.000200 Logit Scale: 21.023 Contrastive_loss: 0.31302 (0.46650) Loss: 0.31302 (0.46650) 2025-03-18,20:10:28 | INFO | Train Epoch: 1 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 148.867/s, 148.867/s/gpu LR: 0.000200 Logit Scale: 21.052 Contrastive_loss: 0.50242 (0.46821) Loss: 0.50242 (0.46821) 2025-03-18,20:10:49 | INFO | Train Epoch: 1 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.213, 149.210/s, 149.210/s/gpu LR: 0.000200 Logit Scale: 21.046 Contrastive_loss: 0.70529 (0.47898) Loss: 0.70529 (0.47898) 2025-03-18,20:11:10 | INFO | Train Epoch: 1 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 147.414/s, 147.414/s/gpu LR: 0.000200 Logit Scale: 21.068 Contrastive_loss: 0.43281 (0.47698) Loss: 0.43281 (0.47698) 2025-03-18,20:11:32 | INFO | Train Epoch: 1 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 148.152/s, 148.152/s/gpu LR: 0.000200 Logit Scale: 21.042 Contrastive_loss: 0.76804 (0.48910) Loss: 0.76804 (0.48910) 2025-03-18,20:11:54 | INFO | Train Epoch: 1 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 143.532/s, 143.532/s/gpu LR: 0.000200 Logit Scale: 21.073 Contrastive_loss: 0.44129 (0.48719) Loss: 0.44129 (0.48719) 2025-03-18,20:12:16 | INFO | Train Epoch: 1 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 152.568/s, 152.568/s/gpu LR: 0.000200 Logit Scale: 21.088 Contrastive_loss: 0.46213 (0.48623) Loss: 0.46213 (0.48623) 2025-03-18,20:12:38 | INFO | Train Epoch: 1 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 147.672/s, 147.672/s/gpu LR: 0.000200 Logit Scale: 21.097 Contrastive_loss: 0.19719 (0.47552) Loss: 0.19719 (0.47552) 2025-03-18,20:12:59 | INFO | Train Epoch: 1 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 148.098/s, 148.098/s/gpu LR: 0.000200 Logit Scale: 21.111 Contrastive_loss: 0.40206 (0.47290) Loss: 0.40206 (0.47290) 2025-03-18,20:13:21 | INFO | Train Epoch: 1 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.423/s, 148.423/s/gpu LR: 0.000200 Logit Scale: 21.105 Contrastive_loss: 0.33959 (0.46830) Loss: 0.33959 (0.46830) 2025-03-18,20:13:42 | INFO | Train Epoch: 1 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.841/s, 148.841/s/gpu LR: 0.000200 Logit Scale: 21.116 Contrastive_loss: 0.26428 (0.46150) Loss: 0.26428 (0.46150) 2025-03-18,20:14:04 | INFO | Train Epoch: 1 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 148.955/s, 148.955/s/gpu LR: 0.000200 Logit Scale: 21.093 Contrastive_loss: 0.39474 (0.45935) Loss: 0.39474 (0.45935) 2025-03-18,20:14:25 | INFO | Train Epoch: 1 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 149.643/s, 149.643/s/gpu LR: 0.000200 Logit Scale: 21.103 Contrastive_loss: 0.47858 (0.45995) Loss: 0.47858 (0.45995) 2025-03-18,20:14:47 | INFO | Train Epoch: 1 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 148.398/s, 148.398/s/gpu LR: 0.000200 Logit Scale: 21.115 Contrastive_loss: 0.64488 (0.46555) Loss: 0.64488 (0.46555) 2025-03-18,20:15:09 | INFO | Train Epoch: 1 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 146.531/s, 146.531/s/gpu LR: 0.000200 Logit Scale: 21.145 Contrastive_loss: 0.57394 (0.46874) Loss: 0.57394 (0.46874) 2025-03-18,20:15:30 | INFO | Train Epoch: 1 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 145.340/s, 145.340/s/gpu LR: 0.000200 Logit Scale: 21.156 Contrastive_loss: 0.38731 (0.46641) Loss: 0.38731 (0.46641) 2025-03-18,20:15:52 | INFO | Train Epoch: 1 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 147.683/s, 147.683/s/gpu LR: 0.000200 Logit Scale: 21.208 Contrastive_loss: 0.56840 (0.46925) Loss: 0.56840 (0.46925) 2025-03-18,20:16:14 | INFO | Train Epoch: 1 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.399/s, 149.399/s/gpu LR: 0.000200 Logit Scale: 21.177 Contrastive_loss: 0.32731 (0.46541) Loss: 0.32731 (0.46541) 2025-03-18,20:16:35 | INFO | Train Epoch: 1 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 152.604/s, 152.604/s/gpu LR: 0.000200 Logit Scale: 21.215 Contrastive_loss: 0.47179 (0.46558) Loss: 0.47179 (0.46558) 2025-03-18,20:16:57 | INFO | Train Epoch: 1 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.218, 146.616/s, 146.616/s/gpu LR: 0.000200 Logit Scale: 21.228 Contrastive_loss: 0.41059 (0.46417) Loss: 0.41059 (0.46417) 2025-03-18,20:17:19 | INFO | Train Epoch: 1 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.218, 145.394/s, 145.394/s/gpu LR: 0.000200 Logit Scale: 21.196 Contrastive_loss: 0.23357 (0.45840) Loss: 0.23357 (0.45840) 2025-03-18,20:17:40 | INFO | Train Epoch: 1 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 145.699/s, 145.699/s/gpu LR: 0.000200 Logit Scale: 21.216 Contrastive_loss: 0.58072 (0.46139) Loss: 0.58072 (0.46139) 2025-03-18,20:18:02 | INFO | Train Epoch: 1 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.220, 146.864/s, 146.864/s/gpu LR: 0.000200 Logit Scale: 21.216 Contrastive_loss: 0.37953 (0.45944) Loss: 0.37953 (0.45944) 2025-03-18,20:18:24 | INFO | Train Epoch: 1 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 149.909/s, 149.909/s/gpu LR: 0.000200 Logit Scale: 21.250 Contrastive_loss: 0.49489 (0.46026) Loss: 0.49489 (0.46026) 2025-03-18,20:18:46 | INFO | Train Epoch: 1 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 150.285/s, 150.285/s/gpu LR: 0.000200 Logit Scale: 21.237 Contrastive_loss: 0.74697 (0.46678) Loss: 0.74697 (0.46678) 2025-03-18,20:19:07 | INFO | Train Epoch: 1 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 151.722/s, 151.722/s/gpu LR: 0.000200 Logit Scale: 21.251 Contrastive_loss: 0.31482 (0.46340) Loss: 0.31482 (0.46340) 2025-03-18,20:19:29 | INFO | Train Epoch: 1 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 144.180/s, 144.180/s/gpu LR: 0.000200 Logit Scale: 21.271 Contrastive_loss: 0.46524 (0.46344) Loss: 0.46524 (0.46344) 2025-03-18,20:19:50 | INFO | Train Epoch: 1 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 146.819/s, 146.819/s/gpu LR: 0.000200 Logit Scale: 21.258 Contrastive_loss: 0.34142 (0.46085) Loss: 0.34142 (0.46085) 2025-03-18,20:20:12 | INFO | Train Epoch: 1 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 151.078/s, 151.078/s/gpu LR: 0.000200 Logit Scale: 21.270 Contrastive_loss: 0.36072 (0.45876) Loss: 0.36072 (0.45876) 2025-03-18,20:20:33 | INFO | Train Epoch: 1 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.213, 152.076/s, 152.076/s/gpu LR: 0.000200 Logit Scale: 21.279 Contrastive_loss: 0.30616 (0.45565) Loss: 0.30616 (0.45565) 2025-03-18,20:20:55 | INFO | Train Epoch: 1 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 147.995/s, 147.995/s/gpu LR: 0.000200 Logit Scale: 21.312 Contrastive_loss: 0.69517 (0.46044) Loss: 0.69517 (0.46044) 2025-03-18,20:21:16 | INFO | Train Epoch: 1 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 150.000/s, 150.000/s/gpu LR: 0.000200 Logit Scale: 21.277 Contrastive_loss: 0.22687 (0.45586) Loss: 0.22687 (0.45586) 2025-03-18,20:21:38 | INFO | Train Epoch: 1 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.526/s, 149.526/s/gpu LR: 0.000200 Logit Scale: 21.320 Contrastive_loss: 0.29322 (0.45273) Loss: 0.29322 (0.45273) 2025-03-18,20:21:59 | INFO | Train Epoch: 1 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 149.676/s, 149.676/s/gpu LR: 0.000200 Logit Scale: 21.319 Contrastive_loss: 0.40502 (0.45183) Loss: 0.40502 (0.45183) 2025-03-18,20:22:21 | INFO | Train Epoch: 1 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 150.303/s, 150.303/s/gpu LR: 0.000200 Logit Scale: 21.327 Contrastive_loss: 0.65042 (0.45551) Loss: 0.65042 (0.45551) 2025-03-18,20:22:42 | INFO | Train Epoch: 1 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.212, 151.674/s, 151.674/s/gpu LR: 0.000200 Logit Scale: 21.347 Contrastive_loss: 0.74310 (0.46073) Loss: 0.74310 (0.46073) 2025-03-18,20:23:03 | INFO | Train Epoch: 1 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 145.876/s, 145.876/s/gpu LR: 0.000200 Logit Scale: 21.401 Contrastive_loss: 0.13186 (0.45486) Loss: 0.13186 (0.45486) 2025-03-18,20:23:25 | INFO | Train Epoch: 1 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 151.634/s, 151.634/s/gpu LR: 0.000200 Logit Scale: 21.416 Contrastive_loss: 0.44832 (0.45475) Loss: 0.44832 (0.45475) 2025-03-18,20:23:47 | INFO | Train Epoch: 1 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 146.706/s, 146.706/s/gpu LR: 0.000200 Logit Scale: 21.444 Contrastive_loss: 0.26874 (0.45154) Loss: 0.26874 (0.45154) 2025-03-18,20:24:09 | INFO | Train Epoch: 1 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.222, 144.062/s, 144.062/s/gpu LR: 0.000200 Logit Scale: 21.417 Contrastive_loss: 0.14339 (0.44632) Loss: 0.14339 (0.44632) 2025-03-18,20:24:31 | INFO | Train Epoch: 1 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.221, 147.267/s, 147.267/s/gpu LR: 0.000200 Logit Scale: 21.460 Contrastive_loss: 0.24427 (0.44295) Loss: 0.24427 (0.44295) 2025-03-18,20:24:53 | INFO | Train Epoch: 1 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 145.325/s, 145.325/s/gpu LR: 0.000200 Logit Scale: 21.460 Contrastive_loss: 0.10582 (0.43742) Loss: 0.10582 (0.43742) 2025-03-18,20:25:15 | INFO | Train Epoch: 1 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.220, 143.914/s, 143.914/s/gpu LR: 0.000200 Logit Scale: 21.473 Contrastive_loss: 0.30114 (0.43523) Loss: 0.30114 (0.43523) 2025-03-18,20:25:37 | INFO | Train Epoch: 1 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.219, 145.850/s, 145.850/s/gpu LR: 0.000200 Logit Scale: 21.506 Contrastive_loss: 0.41449 (0.43490) Loss: 0.41449 (0.43490) 2025-03-18,20:25:59 | INFO | Train Epoch: 1 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.220, 146.585/s, 146.585/s/gpu LR: 0.000200 Logit Scale: 21.506 Contrastive_loss: 0.54917 (0.43668) Loss: 0.54917 (0.43668) 2025-03-18,20:26:20 | INFO | Train Epoch: 1 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.220, 146.235/s, 146.235/s/gpu LR: 0.000200 Logit Scale: 21.517 Contrastive_loss: 0.59425 (0.43911) Loss: 0.59425 (0.43911) 2025-03-18,20:26:42 | INFO | Train Epoch: 1 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.220, 145.247/s, 145.247/s/gpu LR: 0.000200 Logit Scale: 21.510 Contrastive_loss: 0.18351 (0.43523) Loss: 0.18351 (0.43523) 2025-03-18,20:27:04 | INFO | Train Epoch: 1 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.217, 148.770/s, 148.770/s/gpu LR: 0.000200 Logit Scale: 21.553 Contrastive_loss: 0.27897 (0.43290) Loss: 0.27897 (0.43290) 2025-03-18,20:27:26 | INFO | Train Epoch: 1 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.219, 145.868/s, 145.868/s/gpu LR: 0.000200 Logit Scale: 21.563 Contrastive_loss: 0.26395 (0.43042) Loss: 0.26395 (0.43042) 2025-03-18,20:27:48 | INFO | Train Epoch: 1 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 144.546/s, 144.546/s/gpu LR: 0.000200 Logit Scale: 21.584 Contrastive_loss: 0.44651 (0.43065) Loss: 0.44651 (0.43065) 2025-03-18,20:28:10 | INFO | Train Epoch: 1 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.223, 146.385/s, 146.385/s/gpu LR: 0.000200 Logit Scale: 21.605 Contrastive_loss: 0.20578 (0.42744) Loss: 0.20578 (0.42744) 2025-03-18,20:28:32 | INFO | Train Epoch: 1 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.221, 148.565/s, 148.565/s/gpu LR: 0.000200 Logit Scale: 21.623 Contrastive_loss: 0.37235 (0.42666) Loss: 0.37235 (0.42666) 2025-03-18,20:28:54 | INFO | Train Epoch: 1 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 148.310/s, 148.310/s/gpu LR: 0.000200 Logit Scale: 21.623 Contrastive_loss: 0.53607 (0.42818) Loss: 0.53607 (0.42818) 2025-03-18,20:29:15 | INFO | Train Epoch: 1 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 145.172/s, 145.172/s/gpu LR: 0.000200 Logit Scale: 21.622 Contrastive_loss: 0.25284 (0.42578) Loss: 0.25284 (0.42578) 2025-03-18,20:29:37 | INFO | Train Epoch: 1 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.222, 150.838/s, 150.838/s/gpu LR: 0.000200 Logit Scale: 21.591 Contrastive_loss: 0.39731 (0.42539) Loss: 0.39731 (0.42539) 2025-03-18,20:29:59 | INFO | Train Epoch: 1 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 146.308/s, 146.308/s/gpu LR: 0.000200 Logit Scale: 21.595 Contrastive_loss: 0.33518 (0.42419) Loss: 0.33518 (0.42419) 2025-03-18,20:30:21 | INFO | Train Epoch: 1 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.223, 149.575/s, 149.575/s/gpu LR: 0.000200 Logit Scale: 21.559 Contrastive_loss: 0.30763 (0.42266) Loss: 0.30763 (0.42266) 2025-03-18,20:30:43 | INFO | Train Epoch: 1 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.219, 148.223/s, 148.223/s/gpu LR: 0.000200 Logit Scale: 21.594 Contrastive_loss: 1.0034 (0.43020) Loss: 1.0034 (0.43020) 2025-03-18,20:31:05 | INFO | Train Epoch: 1 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.217, 147.690/s, 147.690/s/gpu LR: 0.000200 Logit Scale: 21.587 Contrastive_loss: 0.35966 (0.42929) Loss: 0.35966 (0.42929) 2025-03-18,20:31:26 | INFO | Train Epoch: 1 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.784/s, 149.784/s/gpu LR: 0.000200 Logit Scale: 21.610 Contrastive_loss: 0.26950 (0.42727) Loss: 0.26950 (0.42727) 2025-03-18,20:31:48 | INFO | Train Epoch: 1 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.268/s, 150.268/s/gpu LR: 0.000200 Logit Scale: 21.611 Contrastive_loss: 0.29544 (0.42562) Loss: 0.29544 (0.42562) 2025-03-18,20:32:09 | INFO | Train Epoch: 1 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 151.296/s, 151.296/s/gpu LR: 0.000200 Logit Scale: 21.595 Contrastive_loss: 0.47710 (0.42626) Loss: 0.47710 (0.42626) 2025-03-18,20:32:30 | INFO | Train Epoch: 1 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.213, 150.774/s, 150.774/s/gpu LR: 0.000200 Logit Scale: 21.587 Contrastive_loss: 0.58554 (0.42820) Loss: 0.58554 (0.42820) 2025-03-18,20:32:52 | INFO | Train Epoch: 1 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.212, 152.067/s, 152.067/s/gpu LR: 0.000200 Logit Scale: 21.603 Contrastive_loss: 0.40611 (0.42794) Loss: 0.40611 (0.42794) 2025-03-18,20:33:13 | INFO | Train Epoch: 1 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.212, 150.730/s, 150.730/s/gpu LR: 0.000200 Logit Scale: 21.607 Contrastive_loss: 0.41399 (0.42777) Loss: 0.41399 (0.42777) 2025-03-18,20:33:34 | INFO | Train Epoch: 1 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 150.000/s, 150.000/s/gpu LR: 0.000200 Logit Scale: 21.625 Contrastive_loss: 0.54213 (0.42912) Loss: 0.54213 (0.42912) 2025-03-18,20:33:56 | INFO | Train Epoch: 1 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.621/s, 149.621/s/gpu LR: 0.000200 Logit Scale: 21.665 Contrastive_loss: 0.33932 (0.42807) Loss: 0.33932 (0.42807) 2025-03-18,20:34:17 | INFO | Train Epoch: 1 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.743/s, 149.743/s/gpu LR: 0.000200 Logit Scale: 21.646 Contrastive_loss: 0.78632 (0.43219) Loss: 0.78632 (0.43219) 2025-03-18,20:34:39 | INFO | Train Epoch: 1 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 146.667/s, 146.667/s/gpu LR: 0.000199 Logit Scale: 21.651 Contrastive_loss: 0.65862 (0.43476) Loss: 0.65862 (0.43476) 2025-03-18,20:35:00 | INFO | Train Epoch: 1 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 148.495/s, 148.495/s/gpu LR: 0.000199 Logit Scale: 21.678 Contrastive_loss: 0.19100 (0.43202) Loss: 0.19100 (0.43202) 2025-03-18,20:35:22 | INFO | Train Epoch: 1 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 149.941/s, 149.941/s/gpu LR: 0.000199 Logit Scale: 21.694 Contrastive_loss: 0.22829 (0.42976) Loss: 0.22829 (0.42976) 2025-03-18,20:35:44 | INFO | Train Epoch: 1 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.220, 145.353/s, 145.353/s/gpu LR: 0.000199 Logit Scale: 21.691 Contrastive_loss: 0.16316 (0.42683) Loss: 0.16316 (0.42683) 2025-03-18,20:36:06 | INFO | Train Epoch: 1 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.220, 146.242/s, 146.242/s/gpu LR: 0.000199 Logit Scale: 21.696 Contrastive_loss: 0.49181 (0.42754) Loss: 0.49181 (0.42754) 2025-03-18,20:36:28 | INFO | Train Epoch: 1 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.219, 145.076/s, 145.076/s/gpu LR: 0.000199 Logit Scale: 21.722 Contrastive_loss: 0.42236 (0.42748) Loss: 0.42236 (0.42748) 2025-03-18,20:36:50 | INFO | Train Epoch: 1 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.222, 141.542/s, 141.542/s/gpu LR: 0.000199 Logit Scale: 21.722 Contrastive_loss: 0.28634 (0.42598) Loss: 0.28634 (0.42598) 2025-03-18,20:37:12 | INFO | Train Epoch: 1 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.219, 148.117/s, 148.117/s/gpu LR: 0.000199 Logit Scale: 21.701 Contrastive_loss: 0.49296 (0.42668) Loss: 0.49296 (0.42668) 2025-03-18,20:37:33 | INFO | Train Epoch: 1 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.128/s, 149.128/s/gpu LR: 0.000199 Logit Scale: 21.731 Contrastive_loss: 0.77726 (0.43034) Loss: 0.77726 (0.43034) 2025-03-18,20:37:55 | INFO | Train Epoch: 1 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.462/s, 149.462/s/gpu LR: 0.000199 Logit Scale: 21.707 Contrastive_loss: 0.50464 (0.43110) Loss: 0.50464 (0.43110) 2025-03-18,20:38:16 | INFO | Train Epoch: 1 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.317/s, 149.317/s/gpu LR: 0.000199 Logit Scale: 21.783 Contrastive_loss: 0.21554 (0.42890) Loss: 0.21554 (0.42890) 2025-03-18,20:38:38 | INFO | Train Epoch: 1 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 147.790/s, 147.790/s/gpu LR: 0.000199 Logit Scale: 21.804 Contrastive_loss: 0.27178 (0.42732) Loss: 0.27178 (0.42732) 2025-03-18,20:38:59 | INFO | Train Epoch: 1 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 149.615/s, 149.615/s/gpu LR: 0.000199 Logit Scale: 21.780 Contrastive_loss: 0.81132 (0.43116) Loss: 0.81132 (0.43116) 2025-03-18,20:39:21 | INFO | Train Epoch: 1 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.222, 145.995/s, 145.995/s/gpu LR: 0.000199 Logit Scale: 21.748 Contrastive_loss: 0.19194 (0.42879) Loss: 0.19194 (0.42879) 2025-03-18,20:39:43 | INFO | Train Epoch: 1 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.219, 147.730/s, 147.730/s/gpu LR: 0.000199 Logit Scale: 21.779 Contrastive_loss: 0.48073 (0.42930) Loss: 0.48073 (0.42930) 2025-03-18,20:40:06 | INFO | Train Epoch: 1 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.222, 144.108/s, 144.108/s/gpu LR: 0.000199 Logit Scale: 21.796 Contrastive_loss: 0.35798 (0.42860) Loss: 0.35798 (0.42860) 2025-03-18,20:40:28 | INFO | Train Epoch: 1 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.221, 144.653/s, 144.653/s/gpu LR: 0.000199 Logit Scale: 21.797 Contrastive_loss: 0.35557 (0.42790) Loss: 0.35557 (0.42790) 2025-03-18,20:40:50 | INFO | Train Epoch: 1 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.219, 145.821/s, 145.821/s/gpu LR: 0.000199 Logit Scale: 21.806 Contrastive_loss: 0.56526 (0.42921) Loss: 0.56526 (0.42921) 2025-03-18,20:41:11 | INFO | Train Epoch: 1 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.218, 149.933/s, 149.933/s/gpu LR: 0.000199 Logit Scale: 21.825 Contrastive_loss: 0.36315 (0.42859) Loss: 0.36315 (0.42859) 2025-03-18,20:41:33 | INFO | Train Epoch: 1 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.218, 145.085/s, 145.085/s/gpu LR: 0.000199 Logit Scale: 21.805 Contrastive_loss: 0.77091 (0.43179) Loss: 0.77091 (0.43179) 2025-03-18,20:41:55 | INFO | Train Epoch: 1 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.217, 146.191/s, 146.191/s/gpu LR: 0.000199 Logit Scale: 21.823 Contrastive_loss: 0.62005 (0.43353) Loss: 0.62005 (0.43353) 2025-03-18,20:42:17 | INFO | Train Epoch: 1 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.220, 143.819/s, 143.819/s/gpu LR: 0.000199 Logit Scale: 21.820 Contrastive_loss: 0.55670 (0.43466) Loss: 0.55670 (0.43466) 2025-03-18,20:42:39 | INFO | Train Epoch: 1 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.223, 145.316/s, 145.316/s/gpu LR: 0.000199 Logit Scale: 21.852 Contrastive_loss: 0.039087 (0.43106) Loss: 0.039087 (0.43106) 2025-03-18,20:43:01 | INFO | Train Epoch: 1 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.222, 145.244/s, 145.244/s/gpu LR: 0.000199 Logit Scale: 21.893 Contrastive_loss: 0.63259 (0.43288) Loss: 0.63259 (0.43288) 2025-03-18,20:43:24 | INFO | Train Epoch: 1 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.221, 145.729/s, 145.729/s/gpu LR: 0.000199 Logit Scale: 21.896 Contrastive_loss: 0.37589 (0.43237) Loss: 0.37589 (0.43237) 2025-03-18,20:43:46 | INFO | Train Epoch: 1 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.220, 146.196/s, 146.196/s/gpu LR: 0.000199 Logit Scale: 21.900 Contrastive_loss: 0.25771 (0.43082) Loss: 0.25771 (0.43082) 2025-03-18,20:44:07 | INFO | Train Epoch: 1 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 145.876/s, 145.876/s/gpu LR: 0.000199 Logit Scale: 21.906 Contrastive_loss: 0.19288 (0.42874) Loss: 0.19288 (0.42874) 2025-03-18,20:44:30 | INFO | Train Epoch: 1 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.221, 143.691/s, 143.691/s/gpu LR: 0.000199 Logit Scale: 21.951 Contrastive_loss: 0.23579 (0.42706) Loss: 0.23579 (0.42706) 2025-03-18,20:44:52 | INFO | Train Epoch: 1 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.221, 144.037/s, 144.037/s/gpu LR: 0.000199 Logit Scale: 21.911 Contrastive_loss: 0.28364 (0.42582) Loss: 0.28364 (0.42582) 2025-03-18,20:45:14 | INFO | Train Epoch: 1 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.221, 145.087/s, 145.087/s/gpu LR: 0.000199 Logit Scale: 21.924 Contrastive_loss: 0.23561 (0.42420) Loss: 0.23561 (0.42420) 2025-03-18,20:45:36 | INFO | Train Epoch: 1 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.222, 146.787/s, 146.787/s/gpu LR: 0.000199 Logit Scale: 21.922 Contrastive_loss: 0.43594 (0.42430) Loss: 0.43594 (0.42430) 2025-03-18,20:45:58 | INFO | Train Epoch: 1 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 147.847/s, 147.847/s/gpu LR: 0.000199 Logit Scale: 21.948 Contrastive_loss: 0.46588 (0.42465) Loss: 0.46588 (0.42465) 2025-03-18,20:46:19 | INFO | Train Epoch: 1 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.218, 144.418/s, 144.418/s/gpu LR: 0.000199 Logit Scale: 21.965 Contrastive_loss: 0.37091 (0.42420) Loss: 0.37091 (0.42420) 2025-03-18,20:46:42 | INFO | Train Epoch: 1 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.222, 143.959/s, 143.959/s/gpu LR: 0.000199 Logit Scale: 21.916 Contrastive_loss: 0.30743 (0.42323) Loss: 0.30743 (0.42323) 2025-03-18,20:47:04 | INFO | Train Epoch: 1 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.221, 146.384/s, 146.384/s/gpu LR: 0.000199 Logit Scale: 21.933 Contrastive_loss: 0.37009 (0.42280) Loss: 0.37009 (0.42280) 2025-03-18,20:47:26 | INFO | Train Epoch: 1 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.221, 145.895/s, 145.895/s/gpu LR: 0.000199 Logit Scale: 21.938 Contrastive_loss: 0.30562 (0.42184) Loss: 0.30562 (0.42184) 2025-03-18,20:47:48 | INFO | Train Epoch: 1 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.221, 145.212/s, 145.212/s/gpu LR: 0.000199 Logit Scale: 21.950 Contrastive_loss: 0.72410 (0.42428) Loss: 0.72410 (0.42428) 2025-03-18,20:48:10 | INFO | Train Epoch: 1 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.221, 145.682/s, 145.682/s/gpu LR: 0.000199 Logit Scale: 21.988 Contrastive_loss: 0.98357 (0.42876) Loss: 0.98357 (0.42876) 2025-03-18,20:48:32 | INFO | Train Epoch: 1 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.220, 142.197/s, 142.197/s/gpu LR: 0.000199 Logit Scale: 21.988 Contrastive_loss: 0.38782 (0.42843) Loss: 0.38782 (0.42843) 2025-03-18,20:48:54 | INFO | Train Epoch: 1 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 147.472/s, 147.472/s/gpu LR: 0.000199 Logit Scale: 21.976 Contrastive_loss: 0.36711 (0.42795) Loss: 0.36711 (0.42795) 2025-03-18,20:49:15 | INFO | Train Epoch: 1 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 141.962/s, 141.962/s/gpu LR: 0.000199 Logit Scale: 22.000 Contrastive_loss: 0.30244 (0.42697) Loss: 0.30244 (0.42697) 2025-03-18,20:49:37 | INFO | Train Epoch: 1 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 149.823/s, 149.823/s/gpu LR: 0.000199 Logit Scale: 21.989 Contrastive_loss: 0.26569 (0.42572) Loss: 0.26569 (0.42572) 2025-03-18,20:49:59 | INFO | Train Epoch: 1 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 147.597/s, 147.597/s/gpu LR: 0.000199 Logit Scale: 21.989 Contrastive_loss: 0.37440 (0.42532) Loss: 0.37440 (0.42532) 2025-03-18,20:50:20 | INFO | Train Epoch: 1 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.217, 147.449/s, 147.449/s/gpu LR: 0.000199 Logit Scale: 21.996 Contrastive_loss: 0.57559 (0.42647) Loss: 0.57559 (0.42647) 2025-03-18,20:50:42 | INFO | Train Epoch: 1 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 144.395/s, 144.395/s/gpu LR: 0.000199 Logit Scale: 22.009 Contrastive_loss: 0.41030 (0.42635) Loss: 0.41030 (0.42635) 2025-03-18,20:51:04 | INFO | Train Epoch: 1 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.221, 146.409/s, 146.409/s/gpu LR: 0.000199 Logit Scale: 22.015 Contrastive_loss: 0.49012 (0.42683) Loss: 0.49012 (0.42683) 2025-03-18,20:51:26 | INFO | Train Epoch: 1 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.220, 146.021/s, 146.021/s/gpu LR: 0.000199 Logit Scale: 22.040 Contrastive_loss: 0.53011 (0.42760) Loss: 0.53011 (0.42760) 2025-03-18,20:51:48 | INFO | Train Epoch: 1 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 150.955/s, 150.955/s/gpu LR: 0.000199 Logit Scale: 21.970 Contrastive_loss: 0.24247 (0.42623) Loss: 0.24247 (0.42623) 2025-03-18,20:52:09 | INFO | Train Epoch: 1 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 149.440/s, 149.440/s/gpu LR: 0.000199 Logit Scale: 22.030 Contrastive_loss: 0.75202 (0.42862) Loss: 0.75202 (0.42862) 2025-03-18,20:52:31 | INFO | Train Epoch: 1 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.217, 148.372/s, 148.372/s/gpu LR: 0.000199 Logit Scale: 22.063 Contrastive_loss: 0.58578 (0.42977) Loss: 0.58578 (0.42977) 2025-03-18,20:52:52 | INFO | Train Epoch: 1 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 148.400/s, 148.400/s/gpu LR: 0.000199 Logit Scale: 22.064 Contrastive_loss: 0.12213 (0.42754) Loss: 0.12213 (0.42754) 2025-03-18,20:53:14 | INFO | Train Epoch: 1 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 150.595/s, 150.595/s/gpu LR: 0.000199 Logit Scale: 22.061 Contrastive_loss: 0.44231 (0.42765) Loss: 0.44231 (0.42765) 2025-03-18,20:53:36 | INFO | Train Epoch: 1 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 146.900/s, 146.900/s/gpu LR: 0.000199 Logit Scale: 22.027 Contrastive_loss: 0.84102 (0.43060) Loss: 0.84102 (0.43060) 2025-03-18,20:53:58 | INFO | Train Epoch: 1 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.219, 146.163/s, 146.163/s/gpu LR: 0.000199 Logit Scale: 22.046 Contrastive_loss: 0.72780 (0.43271) Loss: 0.72780 (0.43271) 2025-03-18,20:54:20 | INFO | Train Epoch: 1 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.220, 144.663/s, 144.663/s/gpu LR: 0.000199 Logit Scale: 22.059 Contrastive_loss: 0.63719 (0.43415) Loss: 0.63719 (0.43415) 2025-03-18,20:54:42 | INFO | Train Epoch: 1 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.219, 145.838/s, 145.838/s/gpu LR: 0.000199 Logit Scale: 22.062 Contrastive_loss: 0.35237 (0.43358) Loss: 0.35237 (0.43358) 2025-03-18,20:55:03 | INFO | Train Epoch: 1 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.219, 146.367/s, 146.367/s/gpu LR: 0.000199 Logit Scale: 22.105 Contrastive_loss: 0.36650 (0.43311) Loss: 0.36650 (0.43311) 2025-03-18,20:55:25 | INFO | Train Epoch: 1 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.219, 146.065/s, 146.065/s/gpu LR: 0.000199 Logit Scale: 22.125 Contrastive_loss: 0.21695 (0.43162) Loss: 0.21695 (0.43162) 2025-03-18,20:55:47 | INFO | Train Epoch: 1 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.218, 149.830/s, 149.830/s/gpu LR: 0.000199 Logit Scale: 22.090 Contrastive_loss: 0.35177 (0.43107) Loss: 0.35177 (0.43107) 2025-03-18,20:56:09 | INFO | Train Epoch: 1 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.547/s, 148.547/s/gpu LR: 0.000199 Logit Scale: 22.124 Contrastive_loss: 0.53784 (0.43180) Loss: 0.53784 (0.43180) 2025-03-18,20:56:30 | INFO | Train Epoch: 1 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 151.110/s, 151.110/s/gpu LR: 0.000199 Logit Scale: 22.134 Contrastive_loss: 0.30830 (0.43096) Loss: 0.30830 (0.43096) 2025-03-18,20:56:51 | INFO | Train Epoch: 1 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.213, 149.581/s, 149.581/s/gpu LR: 0.000199 Logit Scale: 22.176 Contrastive_loss: 0.33043 (0.43029) Loss: 0.33043 (0.43029) 2025-03-18,20:57:13 | INFO | Train Epoch: 1 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 144.575/s, 144.575/s/gpu LR: 0.000199 Logit Scale: 22.185 Contrastive_loss: 0.48990 (0.43069) Loss: 0.48990 (0.43069) 2025-03-18,20:57:35 | INFO | Train Epoch: 1 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.217, 148.193/s, 148.193/s/gpu LR: 0.000199 Logit Scale: 22.199 Contrastive_loss: 0.69765 (0.43245) Loss: 0.69765 (0.43245) 2025-03-18,20:57:56 | INFO | Train Epoch: 1 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 152.047/s, 152.047/s/gpu LR: 0.000199 Logit Scale: 22.193 Contrastive_loss: 0.46883 (0.43269) Loss: 0.46883 (0.43269) 2025-03-18,20:58:17 | INFO | Train Epoch: 1 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 148.046/s, 148.046/s/gpu LR: 0.000199 Logit Scale: 22.219 Contrastive_loss: 0.34771 (0.43214) Loss: 0.34771 (0.43214) 2025-03-18,20:58:39 | INFO | Train Epoch: 1 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 148.669/s, 148.669/s/gpu LR: 0.000199 Logit Scale: 22.222 Contrastive_loss: 0.26555 (0.43106) Loss: 0.26555 (0.43106) 2025-03-18,20:59:01 | INFO | Train Epoch: 1 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 147.886/s, 147.886/s/gpu LR: 0.000199 Logit Scale: 22.229 Contrastive_loss: 0.18375 (0.42946) Loss: 0.18375 (0.42946) 2025-03-18,20:59:22 | INFO | Train Epoch: 1 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.454/s, 149.454/s/gpu LR: 0.000199 Logit Scale: 22.192 Contrastive_loss: 0.32390 (0.42878) Loss: 0.32390 (0.42878) 2025-03-18,20:59:44 | INFO | Train Epoch: 1 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 149.170/s, 149.170/s/gpu LR: 0.000199 Logit Scale: 22.153 Contrastive_loss: 0.45177 (0.42893) Loss: 0.45177 (0.42893) 2025-03-18,21:00:05 | INFO | Train Epoch: 1 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.593/s, 149.593/s/gpu LR: 0.000199 Logit Scale: 22.179 Contrastive_loss: 0.40070 (0.42875) Loss: 0.40070 (0.42875) 2025-03-18,21:00:26 | INFO | Train Epoch: 1 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.213, 151.910/s, 151.910/s/gpu LR: 0.000199 Logit Scale: 22.176 Contrastive_loss: 0.28426 (0.42784) Loss: 0.28426 (0.42784) 2025-03-18,21:00:47 | INFO | Train Epoch: 1 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.211, 149.014/s, 149.014/s/gpu LR: 0.000199 Logit Scale: 22.185 Contrastive_loss: 0.71946 (0.42967) Loss: 0.71946 (0.42967) 2025-03-18,21:01:09 | INFO | Train Epoch: 1 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 148.635/s, 148.635/s/gpu LR: 0.000199 Logit Scale: 22.161 Contrastive_loss: 0.41789 (0.42959) Loss: 0.41789 (0.42959) 2025-03-18,21:01:31 | INFO | Train Epoch: 1 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 145.309/s, 145.309/s/gpu LR: 0.000199 Logit Scale: 22.185 Contrastive_loss: 0.41334 (0.42949) Loss: 0.41334 (0.42949) 2025-03-18,21:01:53 | INFO | Train Epoch: 1 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.220, 145.054/s, 145.054/s/gpu LR: 0.000199 Logit Scale: 22.165 Contrastive_loss: 1.2427 (0.43448) Loss: 1.2427 (0.43448) 2025-03-18,21:02:14 | INFO | Train Epoch: 1 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 149.381/s, 149.381/s/gpu LR: 0.000199 Logit Scale: 22.126 Contrastive_loss: 0.26335 (0.43344) Loss: 0.26335 (0.43344) 2025-03-18,21:02:36 | INFO | Train Epoch: 1 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.213, 149.968/s, 149.968/s/gpu LR: 0.000199 Logit Scale: 22.174 Contrastive_loss: 0.60671 (0.43449) Loss: 0.60671 (0.43449) 2025-03-18,21:02:57 | INFO | Train Epoch: 1 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 149.256/s, 149.256/s/gpu LR: 0.000199 Logit Scale: 22.165 Contrastive_loss: 0.44509 (0.43455) Loss: 0.44509 (0.43455) 2025-03-18,21:03:19 | INFO | Train Epoch: 1 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 148.134/s, 148.134/s/gpu LR: 0.000199 Logit Scale: 22.191 Contrastive_loss: 0.83247 (0.43693) Loss: 0.83247 (0.43693) 2025-03-18,21:03:40 | INFO | Train Epoch: 1 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 151.958/s, 151.958/s/gpu LR: 0.000199 Logit Scale: 22.233 Contrastive_loss: 0.13388 (0.43513) Loss: 0.13388 (0.43513) 2025-03-18,21:04:01 | INFO | Train Epoch: 1 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.213, 149.252/s, 149.252/s/gpu LR: 0.000199 Logit Scale: 22.206 Contrastive_loss: 0.29193 (0.43428) Loss: 0.29193 (0.43428) 2025-03-18,21:04:23 | INFO | Train Epoch: 1 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 149.587/s, 149.587/s/gpu LR: 0.000199 Logit Scale: 22.230 Contrastive_loss: 0.66685 (0.43565) Loss: 0.66685 (0.43565) 2025-03-18,21:04:45 | INFO | Train Epoch: 1 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 146.348/s, 146.348/s/gpu LR: 0.000199 Logit Scale: 22.263 Contrastive_loss: 0.12774 (0.43385) Loss: 0.12774 (0.43385) 2025-03-18,21:05:07 | INFO | Train Epoch: 1 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 150.135/s, 150.135/s/gpu LR: 0.000199 Logit Scale: 22.248 Contrastive_loss: 0.54024 (0.43447) Loss: 0.54024 (0.43447) 2025-03-18,21:05:28 | INFO | Train Epoch: 1 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 149.975/s, 149.975/s/gpu LR: 0.000199 Logit Scale: 22.222 Contrastive_loss: 0.14237 (0.43278) Loss: 0.14237 (0.43278) 2025-03-18,21:05:50 | INFO | Train Epoch: 1 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 148.079/s, 148.079/s/gpu LR: 0.000199 Logit Scale: 22.182 Contrastive_loss: 0.53742 (0.43338) Loss: 0.53742 (0.43338) 2025-03-18,21:06:11 | INFO | Train Epoch: 1 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 148.738/s, 148.738/s/gpu LR: 0.000199 Logit Scale: 22.229 Contrastive_loss: 0.50259 (0.43378) Loss: 0.50259 (0.43378) 2025-03-18,21:06:33 | INFO | Train Epoch: 1 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.214, 151.756/s, 151.756/s/gpu LR: 0.000199 Logit Scale: 22.211 Contrastive_loss: 0.49171 (0.43411) Loss: 0.49171 (0.43411) 2025-03-18,21:06:54 | INFO | Train Epoch: 1 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.549/s, 149.549/s/gpu LR: 0.000199 Logit Scale: 22.261 Contrastive_loss: 0.65574 (0.43536) Loss: 0.65574 (0.43536) 2025-03-18,21:07:16 | INFO | Train Epoch: 1 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 148.167/s, 148.167/s/gpu LR: 0.000199 Logit Scale: 22.229 Contrastive_loss: 0.21648 (0.43413) Loss: 0.21648 (0.43413) 2025-03-18,21:07:37 | INFO | Train Epoch: 1 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 151.236/s, 151.236/s/gpu LR: 0.000199 Logit Scale: 22.246 Contrastive_loss: 0.46044 (0.43428) Loss: 0.46044 (0.43428) 2025-03-18,21:07:58 | INFO | Train Epoch: 1 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.213, 148.541/s, 148.541/s/gpu LR: 0.000199 Logit Scale: 22.269 Contrastive_loss: 0.43055 (0.43426) Loss: 0.43055 (0.43426) 2025-03-18,21:08:20 | INFO | Train Epoch: 1 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 149.682/s, 149.682/s/gpu LR: 0.000199 Logit Scale: 22.263 Contrastive_loss: 0.70310 (0.43574) Loss: 0.70310 (0.43574) 2025-03-18,21:08:41 | INFO | Train Epoch: 1 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 151.281/s, 151.281/s/gpu LR: 0.000199 Logit Scale: 22.272 Contrastive_loss: 0.42090 (0.43566) Loss: 0.42090 (0.43566) 2025-03-18,21:09:03 | INFO | Train Epoch: 1 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 143.900/s, 143.900/s/gpu LR: 0.000199 Logit Scale: 22.234 Contrastive_loss: 0.26261 (0.43471) Loss: 0.26261 (0.43471) 2025-03-18,21:09:25 | INFO | Train Epoch: 1 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 150.323/s, 150.323/s/gpu LR: 0.000199 Logit Scale: 22.243 Contrastive_loss: 0.46860 (0.43490) Loss: 0.46860 (0.43490) 2025-03-18,21:09:46 | INFO | Train Epoch: 1 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 149.802/s, 149.802/s/gpu LR: 0.000199 Logit Scale: 22.287 Contrastive_loss: 0.45000 (0.43498) Loss: 0.45000 (0.43498) 2025-03-18,21:10:08 | INFO | Train Epoch: 1 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 148.773/s, 148.773/s/gpu LR: 0.000199 Logit Scale: 22.288 Contrastive_loss: 0.40444 (0.43482) Loss: 0.40444 (0.43482) 2025-03-18,21:10:29 | INFO | Train Epoch: 1 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.834/s, 149.834/s/gpu LR: 0.000199 Logit Scale: 22.311 Contrastive_loss: 0.38761 (0.43456) Loss: 0.38761 (0.43456) 2025-03-18,21:10:51 | INFO | Train Epoch: 1 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 149.473/s, 149.473/s/gpu LR: 0.000199 Logit Scale: 22.295 Contrastive_loss: 0.48071 (0.43481) Loss: 0.48071 (0.43481) 2025-03-18,21:11:12 | INFO | Train Epoch: 1 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.212, 152.591/s, 152.591/s/gpu LR: 0.000199 Logit Scale: 22.334 Contrastive_loss: 0.74993 (0.43648) Loss: 0.74993 (0.43648) 2025-03-18,21:11:33 | INFO | Train Epoch: 1 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 147.489/s, 147.489/s/gpu LR: 0.000199 Logit Scale: 22.297 Contrastive_loss: 0.20049 (0.43523) Loss: 0.20049 (0.43523) 2025-03-18,21:11:55 | INFO | Train Epoch: 1 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.265/s, 148.265/s/gpu LR: 0.000199 Logit Scale: 22.378 Contrastive_loss: 0.57890 (0.43599) Loss: 0.57890 (0.43599) 2025-03-18,21:12:16 | INFO | Train Epoch: 1 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 151.228/s, 151.228/s/gpu LR: 0.000199 Logit Scale: 22.394 Contrastive_loss: 0.44790 (0.43605) Loss: 0.44790 (0.43605) 2025-03-18,21:12:38 | INFO | Train Epoch: 1 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 146.560/s, 146.560/s/gpu LR: 0.000199 Logit Scale: 22.399 Contrastive_loss: 0.50539 (0.43641) Loss: 0.50539 (0.43641) 2025-03-18,21:13:00 | INFO | Train Epoch: 1 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 134.146/s, 134.146/s/gpu LR: 0.000199 Logit Scale: 22.382 Contrastive_loss: 0.35646 (0.43599) Loss: 0.35646 (0.43599) 2025-03-18,21:13:22 | INFO | Train Epoch: 1 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.225, 144.520/s, 144.520/s/gpu LR: 0.000199 Logit Scale: 22.409 Contrastive_loss: 0.89103 (0.43833) Loss: 0.89103 (0.43833) 2025-03-18,21:13:44 | INFO | Train Epoch: 1 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 149.873/s, 149.873/s/gpu LR: 0.000199 Logit Scale: 22.368 Contrastive_loss: 0.26079 (0.43742) Loss: 0.26079 (0.43742) 2025-03-18,21:14:06 | INFO | Train Epoch: 1 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.220, 146.458/s, 146.458/s/gpu LR: 0.000199 Logit Scale: 22.409 Contrastive_loss: 0.15712 (0.43600) Loss: 0.15712 (0.43600) 2025-03-18,21:14:28 | INFO | Train Epoch: 1 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 150.038/s, 150.038/s/gpu LR: 0.000199 Logit Scale: 22.412 Contrastive_loss: 0.83932 (0.43804) Loss: 0.83932 (0.43804) 2025-03-18,21:14:50 | INFO | Train Epoch: 1 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.221, 146.723/s, 146.723/s/gpu LR: 0.000199 Logit Scale: 22.402 Contrastive_loss: 0.38614 (0.43778) Loss: 0.38614 (0.43778) 2025-03-18,21:15:12 | INFO | Train Epoch: 1 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.219, 144.503/s, 144.503/s/gpu LR: 0.000199 Logit Scale: 22.421 Contrastive_loss: 0.61340 (0.43865) Loss: 0.61340 (0.43865) 2025-03-18,21:15:34 | INFO | Train Epoch: 1 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.220, 145.896/s, 145.896/s/gpu LR: 0.000199 Logit Scale: 22.437 Contrastive_loss: 0.45094 (0.43872) Loss: 0.45094 (0.43872) 2025-03-18,21:15:56 | INFO | Train Epoch: 1 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.221, 145.393/s, 145.393/s/gpu LR: 0.000199 Logit Scale: 22.419 Contrastive_loss: 0.42968 (0.43867) Loss: 0.42968 (0.43867) 2025-03-18,21:16:18 | INFO | Train Epoch: 1 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.221, 145.606/s, 145.606/s/gpu LR: 0.000199 Logit Scale: 22.408 Contrastive_loss: 0.28655 (0.43792) Loss: 0.28655 (0.43792) 2025-03-18,21:16:40 | INFO | Train Epoch: 1 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.222, 144.584/s, 144.584/s/gpu LR: 0.000199 Logit Scale: 22.459 Contrastive_loss: 0.50079 (0.43823) Loss: 0.50079 (0.43823) 2025-03-18,21:17:02 | INFO | Train Epoch: 1 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.220, 145.888/s, 145.888/s/gpu LR: 0.000199 Logit Scale: 22.470 Contrastive_loss: 0.46083 (0.43834) Loss: 0.46083 (0.43834) 2025-03-18,21:17:24 | INFO | Train Epoch: 1 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.220, 144.954/s, 144.954/s/gpu LR: 0.000199 Logit Scale: 22.498 Contrastive_loss: 0.70526 (0.43964) Loss: 0.70526 (0.43964) 2025-03-18,21:17:46 | INFO | Train Epoch: 1 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.221, 144.435/s, 144.435/s/gpu LR: 0.000199 Logit Scale: 22.509 Contrastive_loss: 0.50270 (0.43994) Loss: 0.50270 (0.43994) 2025-03-18,21:18:08 | INFO | Train Epoch: 1 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 151.193/s, 151.193/s/gpu LR: 0.000199 Logit Scale: 22.488 Contrastive_loss: 0.36701 (0.43959) Loss: 0.36701 (0.43959) 2025-03-18,21:18:29 | INFO | Train Epoch: 1 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 147.698/s, 147.698/s/gpu LR: 0.000199 Logit Scale: 22.450 Contrastive_loss: 0.22407 (0.43856) Loss: 0.22407 (0.43856) 2025-03-18,21:18:51 | INFO | Train Epoch: 1 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 149.003/s, 149.003/s/gpu LR: 0.000199 Logit Scale: 22.517 Contrastive_loss: 0.20554 (0.43745) Loss: 0.20554 (0.43745) 2025-03-18,21:19:12 | INFO | Train Epoch: 1 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.586/s, 149.586/s/gpu LR: 0.000199 Logit Scale: 22.533 Contrastive_loss: 0.47745 (0.43764) Loss: 0.47745 (0.43764) 2025-03-18,21:19:34 | INFO | Train Epoch: 1 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 148.614/s, 148.614/s/gpu LR: 0.000199 Logit Scale: 22.598 Contrastive_loss: 0.16808 (0.43637) Loss: 0.16808 (0.43637) 2025-03-18,21:19:55 | INFO | Train Epoch: 1 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.572/s, 149.572/s/gpu LR: 0.000199 Logit Scale: 22.558 Contrastive_loss: 0.57544 (0.43702) Loss: 0.57544 (0.43702) 2025-03-18,21:20:17 | INFO | Train Epoch: 1 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 148.896/s, 148.896/s/gpu LR: 0.000199 Logit Scale: 22.563 Contrastive_loss: 0.51441 (0.43738) Loss: 0.51441 (0.43738) 2025-03-18,21:20:38 | INFO | Train Epoch: 1 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 149.925/s, 149.925/s/gpu LR: 0.000199 Logit Scale: 22.550 Contrastive_loss: 0.21007 (0.43632) Loss: 0.21007 (0.43632) 2025-03-18,21:21:00 | INFO | Train Epoch: 1 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 149.817/s, 149.817/s/gpu LR: 0.000199 Logit Scale: 22.582 Contrastive_loss: 0.17301 (0.43510) Loss: 0.17301 (0.43510) 2025-03-18,21:21:21 | INFO | Train Epoch: 1 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.211, 152.219/s, 152.219/s/gpu LR: 0.000199 Logit Scale: 22.566 Contrastive_loss: 0.21878 (0.43411) Loss: 0.21878 (0.43411) 2025-03-18,21:21:42 | INFO | Train Epoch: 1 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 147.195/s, 147.195/s/gpu LR: 0.000199 Logit Scale: 22.557 Contrastive_loss: 0.16242 (0.43286) Loss: 0.16242 (0.43286) 2025-03-18,21:22:04 | INFO | Train Epoch: 1 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 147.198/s, 147.198/s/gpu LR: 0.000199 Logit Scale: 22.550 Contrastive_loss: 0.27874 (0.43216) Loss: 0.27874 (0.43216) 2025-03-18,21:22:26 | INFO | Train Epoch: 1 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.218, 147.415/s, 147.415/s/gpu LR: 0.000199 Logit Scale: 22.604 Contrastive_loss: 0.61675 (0.43300) Loss: 0.61675 (0.43300) 2025-03-18,21:22:47 | INFO | Train Epoch: 1 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.213, 151.995/s, 151.995/s/gpu LR: 0.000199 Logit Scale: 22.593 Contrastive_loss: 0.21954 (0.43203) Loss: 0.21954 (0.43203) 2025-03-18,21:23:08 | INFO | Train Epoch: 1 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 147.241/s, 147.241/s/gpu LR: 0.000199 Logit Scale: 22.587 Contrastive_loss: 0.26613 (0.43128) Loss: 0.26613 (0.43128) 2025-03-18,21:23:30 | INFO | Train Epoch: 1 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.217, 147.973/s, 147.973/s/gpu LR: 0.000199 Logit Scale: 22.594 Contrastive_loss: 0.24428 (0.43045) Loss: 0.24428 (0.43045) 2025-03-18,21:23:52 | INFO | Train Epoch: 1 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.774/s, 149.774/s/gpu LR: 0.000199 Logit Scale: 22.599 Contrastive_loss: 0.35576 (0.43011) Loss: 0.35576 (0.43011) 2025-03-18,21:24:13 | INFO | Train Epoch: 1 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 147.084/s, 147.084/s/gpu LR: 0.000199 Logit Scale: 22.610 Contrastive_loss: 0.25200 (0.42932) Loss: 0.25200 (0.42932) 2025-03-18,21:24:35 | INFO | Train Epoch: 1 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.218, 145.321/s, 145.321/s/gpu LR: 0.000199 Logit Scale: 22.611 Contrastive_loss: 0.095351 (0.42784) Loss: 0.095351 (0.42784) 2025-03-18,21:24:57 | INFO | Train Epoch: 1 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.218, 146.987/s, 146.987/s/gpu LR: 0.000199 Logit Scale: 22.651 Contrastive_loss: 0.78068 (0.42940) Loss: 0.78068 (0.42940) 2025-03-18,21:25:19 | INFO | Train Epoch: 1 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.217, 148.381/s, 148.381/s/gpu LR: 0.000199 Logit Scale: 22.636 Contrastive_loss: 0.46827 (0.42957) Loss: 0.46827 (0.42957) 2025-03-18,21:25:40 | INFO | Train Epoch: 1 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.217, 146.767/s, 146.767/s/gpu LR: 0.000199 Logit Scale: 22.634 Contrastive_loss: 0.54502 (0.43007) Loss: 0.54502 (0.43007) 2025-03-18,21:26:02 | INFO | Train Epoch: 1 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.218, 147.601/s, 147.601/s/gpu LR: 0.000199 Logit Scale: 22.631 Contrastive_loss: 0.41457 (0.43000) Loss: 0.41457 (0.43000) 2025-03-18,21:26:24 | INFO | Train Epoch: 1 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 149.245/s, 149.245/s/gpu LR: 0.000199 Logit Scale: 22.649 Contrastive_loss: 0.63821 (0.43091) Loss: 0.63821 (0.43091) 2025-03-18,21:26:45 | INFO | Train Epoch: 1 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 152.028/s, 152.028/s/gpu LR: 0.000199 Logit Scale: 22.640 Contrastive_loss: 0.70627 (0.43209) Loss: 0.70627 (0.43209) 2025-03-18,21:27:07 | INFO | Train Epoch: 1 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.385/s, 149.385/s/gpu LR: 0.000199 Logit Scale: 22.693 Contrastive_loss: 0.36211 (0.43179) Loss: 0.36211 (0.43179) 2025-03-18,21:27:28 | INFO | Train Epoch: 1 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 147.980/s, 147.980/s/gpu LR: 0.000199 Logit Scale: 22.669 Contrastive_loss: 0.38897 (0.43161) Loss: 0.38897 (0.43161) 2025-03-18,21:27:50 | INFO | Train Epoch: 1 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.639/s, 147.639/s/gpu LR: 0.000199 Logit Scale: 22.686 Contrastive_loss: 0.37986 (0.43139) Loss: 0.37986 (0.43139) 2025-03-18,21:28:12 | INFO | Train Epoch: 1 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.905/s, 147.905/s/gpu LR: 0.000199 Logit Scale: 22.659 Contrastive_loss: 0.41933 (0.43134) Loss: 0.41933 (0.43134) 2025-03-18,21:28:33 | INFO | Train Epoch: 1 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 149.579/s, 149.579/s/gpu LR: 0.000199 Logit Scale: 22.663 Contrastive_loss: 0.94840 (0.43352) Loss: 0.94840 (0.43352) 2025-03-18,21:28:55 | INFO | Train Epoch: 1 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 148.052/s, 148.052/s/gpu LR: 0.000199 Logit Scale: 22.662 Contrastive_loss: 0.47266 (0.43368) Loss: 0.47266 (0.43368) 2025-03-18,21:29:16 | INFO | Train Epoch: 1 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 149.084/s, 149.084/s/gpu LR: 0.000199 Logit Scale: 22.681 Contrastive_loss: 0.20101 (0.43271) Loss: 0.20101 (0.43271) 2025-03-18,21:29:38 | INFO | Train Epoch: 1 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.215, 147.498/s, 147.498/s/gpu LR: 0.000199 Logit Scale: 22.674 Contrastive_loss: 0.23756 (0.43190) Loss: 0.23756 (0.43190) 2025-03-18,21:29:46 | INFO | Train Epoch: 1 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.219, 146.908/s, 146.908/s/gpu LR: 0.000199 Logit Scale: 22.689 Contrastive_loss: 0.47794 (0.43209) Loss: 0.47794 (0.43209) 2025-03-18,21:29:46 | INFO | Eval Epoch: 2 [32 / 7443] Clip Loss: 3.182167 2025-03-18,21:29:52 | INFO | Eval Epoch: 2 [3232 / 7443] Clip Loss: 1.047611 2025-03-18,21:29:57 | INFO | Eval Epoch: 2 [6432 / 7443] Clip Loss: 0.814681 2025-03-18,21:30:00 | INFO | Eval Epoch: 2 image_to_text_mean_rank: 123.4013 image_to_text_median_rank: 10.0000 image_to_text_R@1: 0.1051 image_to_text_R@5: 0.3556 image_to_text_R@10: 0.5230 text_to_image_mean_rank: 89.4690 text_to_image_median_rank: 9.0000 text_to_image_R@1: 0.1009 text_to_image_R@5: 0.3583 text_to_image_R@10: 0.5253 clip_val_loss: 0.7709 epoch: 2.0000 num_samples: 7443.0000 2025-03-18,21:30:33 | INFO | Start epoch 2 2025-03-18,21:30:34 | INFO | Train Epoch: 2 [ 32/766009 (0%)] Data (t): 0.177 Batch (t): 0.388, 82.5583/s, 82.5583/s/gpu LR: 0.000199 Logit Scale: 22.689 Contrastive_loss: 0.53908 (0.53908) Loss: 0.53908 (0.53908) 2025-03-18,21:30:56 | INFO | Train Epoch: 2 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.221, 146.648/s, 146.648/s/gpu LR: 0.000199 Logit Scale: 22.722 Contrastive_loss: 0.051838 (0.29546) Loss: 0.051838 (0.29546) 2025-03-18,21:31:18 | INFO | Train Epoch: 2 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 146.950/s, 146.950/s/gpu LR: 0.000199 Logit Scale: 22.707 Contrastive_loss: 0.73448 (0.44180) Loss: 0.73448 (0.44180) 2025-03-18,21:31:39 | INFO | Train Epoch: 2 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.213, 151.108/s, 151.108/s/gpu LR: 0.000199 Logit Scale: 22.729 Contrastive_loss: 0.27978 (0.40129) Loss: 0.27978 (0.40129) 2025-03-18,21:32:00 | INFO | Train Epoch: 2 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 148.570/s, 148.570/s/gpu LR: 0.000199 Logit Scale: 22.739 Contrastive_loss: 0.29939 (0.38091) Loss: 0.29939 (0.38091) 2025-03-18,21:32:22 | INFO | Train Epoch: 2 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 149.264/s, 149.264/s/gpu LR: 0.000199 Logit Scale: 22.762 Contrastive_loss: 0.48089 (0.39758) Loss: 0.48089 (0.39758) 2025-03-18,21:32:43 | INFO | Train Epoch: 2 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 151.191/s, 151.191/s/gpu LR: 0.000199 Logit Scale: 22.746 Contrastive_loss: 0.25275 (0.37689) Loss: 0.25275 (0.37689) 2025-03-18,21:33:05 | INFO | Train Epoch: 2 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 151.300/s, 151.300/s/gpu LR: 0.000199 Logit Scale: 22.733 Contrastive_loss: 0.51793 (0.39452) Loss: 0.51793 (0.39452) 2025-03-18,21:33:26 | INFO | Train Epoch: 2 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 151.179/s, 151.179/s/gpu LR: 0.000199 Logit Scale: 22.713 Contrastive_loss: 0.38453 (0.39341) Loss: 0.38453 (0.39341) 2025-03-18,21:33:47 | INFO | Train Epoch: 2 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 151.084/s, 151.084/s/gpu LR: 0.000199 Logit Scale: 22.749 Contrastive_loss: 0.76032 (0.43010) Loss: 0.76032 (0.43010) 2025-03-18,21:34:09 | INFO | Train Epoch: 2 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.213, 149.716/s, 149.716/s/gpu LR: 0.000199 Logit Scale: 22.757 Contrastive_loss: 0.30105 (0.41837) Loss: 0.30105 (0.41837) 2025-03-18,21:34:30 | INFO | Train Epoch: 2 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 146.563/s, 146.563/s/gpu LR: 0.000199 Logit Scale: 22.712 Contrastive_loss: 0.46669 (0.42239) Loss: 0.46669 (0.42239) 2025-03-18,21:34:52 | INFO | Train Epoch: 2 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 149.489/s, 149.489/s/gpu LR: 0.000199 Logit Scale: 22.752 Contrastive_loss: 0.26409 (0.41022) Loss: 0.26409 (0.41022) 2025-03-18,21:35:14 | INFO | Train Epoch: 2 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.219, 146.706/s, 146.706/s/gpu LR: 0.000198 Logit Scale: 22.804 Contrastive_loss: 0.44047 (0.41238) Loss: 0.44047 (0.41238) 2025-03-18,21:35:35 | INFO | Train Epoch: 2 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 147.819/s, 147.819/s/gpu LR: 0.000198 Logit Scale: 22.815 Contrastive_loss: 0.28680 (0.40401) Loss: 0.28680 (0.40401) 2025-03-18,21:35:57 | INFO | Train Epoch: 2 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 146.498/s, 146.498/s/gpu LR: 0.000198 Logit Scale: 22.856 Contrastive_loss: 0.28284 (0.39643) Loss: 0.28284 (0.39643) 2025-03-18,21:36:18 | INFO | Train Epoch: 2 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.668/s, 149.668/s/gpu LR: 0.000198 Logit Scale: 22.911 Contrastive_loss: 0.087538 (0.37826) Loss: 0.087538 (0.37826) 2025-03-18,21:36:40 | INFO | Train Epoch: 2 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 148.995/s, 148.995/s/gpu LR: 0.000198 Logit Scale: 22.905 Contrastive_loss: 0.13914 (0.36498) Loss: 0.13914 (0.36498) 2025-03-18,21:37:01 | INFO | Train Epoch: 2 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 151.494/s, 151.494/s/gpu LR: 0.000198 Logit Scale: 22.833 Contrastive_loss: 0.37691 (0.36561) Loss: 0.37691 (0.36561) 2025-03-18,21:37:23 | INFO | Train Epoch: 2 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 149.192/s, 149.192/s/gpu LR: 0.000198 Logit Scale: 22.839 Contrastive_loss: 0.39264 (0.36696) Loss: 0.39264 (0.36696) 2025-03-18,21:37:44 | INFO | Train Epoch: 2 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 150.087/s, 150.087/s/gpu LR: 0.000198 Logit Scale: 22.862 Contrastive_loss: 0.33563 (0.36547) Loss: 0.33563 (0.36547) 2025-03-18,21:38:06 | INFO | Train Epoch: 2 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 149.731/s, 149.731/s/gpu LR: 0.000198 Logit Scale: 22.881 Contrastive_loss: 0.22932 (0.35928) Loss: 0.22932 (0.35928) 2025-03-18,21:38:27 | INFO | Train Epoch: 2 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 146.653/s, 146.653/s/gpu LR: 0.000198 Logit Scale: 22.907 Contrastive_loss: 0.52884 (0.36665) Loss: 0.52884 (0.36665) 2025-03-18,21:38:49 | INFO | Train Epoch: 2 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 148.564/s, 148.564/s/gpu LR: 0.000198 Logit Scale: 22.915 Contrastive_loss: 0.28343 (0.36318) Loss: 0.28343 (0.36318) 2025-03-18,21:39:10 | INFO | Train Epoch: 2 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 148.539/s, 148.539/s/gpu LR: 0.000198 Logit Scale: 22.933 Contrastive_loss: 0.39214 (0.36434) Loss: 0.39214 (0.36434) 2025-03-18,21:39:32 | INFO | Train Epoch: 2 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 149.741/s, 149.741/s/gpu LR: 0.000198 Logit Scale: 22.919 Contrastive_loss: 0.41358 (0.36623) Loss: 0.41358 (0.36623) 2025-03-18,21:39:53 | INFO | Train Epoch: 2 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 149.498/s, 149.498/s/gpu LR: 0.000198 Logit Scale: 22.880 Contrastive_loss: 0.12983 (0.35748) Loss: 0.12983 (0.35748) 2025-03-18,21:40:15 | INFO | Train Epoch: 2 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 148.681/s, 148.681/s/gpu LR: 0.000198 Logit Scale: 22.846 Contrastive_loss: 0.63618 (0.36743) Loss: 0.63618 (0.36743) 2025-03-18,21:40:37 | INFO | Train Epoch: 2 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 147.223/s, 147.223/s/gpu LR: 0.000198 Logit Scale: 22.915 Contrastive_loss: 0.15669 (0.36017) Loss: 0.15669 (0.36017) 2025-03-18,21:40:58 | INFO | Train Epoch: 2 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 147.900/s, 147.900/s/gpu LR: 0.000198 Logit Scale: 22.907 Contrastive_loss: 0.45568 (0.36335) Loss: 0.45568 (0.36335) 2025-03-18,21:41:20 | INFO | Train Epoch: 2 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.822/s, 148.822/s/gpu LR: 0.000198 Logit Scale: 22.934 Contrastive_loss: 0.24187 (0.35943) Loss: 0.24187 (0.35943) 2025-03-18,21:41:41 | INFO | Train Epoch: 2 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 148.431/s, 148.431/s/gpu LR: 0.000198 Logit Scale: 22.906 Contrastive_loss: 0.32381 (0.35832) Loss: 0.32381 (0.35832) 2025-03-18,21:42:03 | INFO | Train Epoch: 2 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.478/s, 149.478/s/gpu LR: 0.000198 Logit Scale: 22.936 Contrastive_loss: 0.61223 (0.36601) Loss: 0.61223 (0.36601) 2025-03-18,21:42:25 | INFO | Train Epoch: 2 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.527/s, 148.527/s/gpu LR: 0.000198 Logit Scale: 22.949 Contrastive_loss: 0.53802 (0.37107) Loss: 0.53802 (0.37107) 2025-03-18,21:42:46 | INFO | Train Epoch: 2 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 148.356/s, 148.356/s/gpu LR: 0.000198 Logit Scale: 22.958 Contrastive_loss: 0.49298 (0.37455) Loss: 0.49298 (0.37455) 2025-03-18,21:43:08 | INFO | Train Epoch: 2 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.354/s, 148.354/s/gpu LR: 0.000198 Logit Scale: 22.975 Contrastive_loss: 0.37051 (0.37444) Loss: 0.37051 (0.37444) 2025-03-18,21:43:29 | INFO | Train Epoch: 2 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.794/s, 149.794/s/gpu LR: 0.000198 Logit Scale: 22.944 Contrastive_loss: 0.34283 (0.37359) Loss: 0.34283 (0.37359) 2025-03-18,21:43:51 | INFO | Train Epoch: 2 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 149.147/s, 149.147/s/gpu LR: 0.000198 Logit Scale: 22.955 Contrastive_loss: 0.67309 (0.38147) Loss: 0.67309 (0.38147) 2025-03-18,21:44:12 | INFO | Train Epoch: 2 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 148.909/s, 148.909/s/gpu LR: 0.000198 Logit Scale: 22.953 Contrastive_loss: 0.40768 (0.38214) Loss: 0.40768 (0.38214) 2025-03-18,21:44:33 | INFO | Train Epoch: 2 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 149.983/s, 149.983/s/gpu LR: 0.000198 Logit Scale: 23.011 Contrastive_loss: 0.42169 (0.38313) Loss: 0.42169 (0.38313) 2025-03-18,21:44:55 | INFO | Train Epoch: 2 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 143.788/s, 143.788/s/gpu LR: 0.000198 Logit Scale: 23.021 Contrastive_loss: 0.22939 (0.37938) Loss: 0.22939 (0.37938) 2025-03-18,21:45:17 | INFO | Train Epoch: 2 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.223, 143.176/s, 143.176/s/gpu LR: 0.000198 Logit Scale: 23.000 Contrastive_loss: 0.32717 (0.37814) Loss: 0.32717 (0.37814) 2025-03-18,21:45:39 | INFO | Train Epoch: 2 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.947/s, 149.947/s/gpu LR: 0.000198 Logit Scale: 22.997 Contrastive_loss: 0.25737 (0.37533) Loss: 0.25737 (0.37533) 2025-03-18,21:46:00 | INFO | Train Epoch: 2 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 150.346/s, 150.346/s/gpu LR: 0.000198 Logit Scale: 23.063 Contrastive_loss: 0.56540 (0.37965) Loss: 0.56540 (0.37965) 2025-03-18,21:46:22 | INFO | Train Epoch: 2 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 141.644/s, 141.644/s/gpu LR: 0.000198 Logit Scale: 23.055 Contrastive_loss: 0.19021 (0.37544) Loss: 0.19021 (0.37544) 2025-03-18,21:46:43 | INFO | Train Epoch: 2 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 147.967/s, 147.967/s/gpu LR: 0.000198 Logit Scale: 22.998 Contrastive_loss: 0.18030 (0.37120) Loss: 0.18030 (0.37120) 2025-03-18,21:47:05 | INFO | Train Epoch: 2 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 144.936/s, 144.936/s/gpu LR: 0.000198 Logit Scale: 23.024 Contrastive_loss: 0.37953 (0.37137) Loss: 0.37953 (0.37137) 2025-03-18,21:47:27 | INFO | Train Epoch: 2 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.222, 144.311/s, 144.311/s/gpu LR: 0.000198 Logit Scale: 23.032 Contrastive_loss: 0.50746 (0.37421) Loss: 0.50746 (0.37421) 2025-03-18,21:47:49 | INFO | Train Epoch: 2 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.220, 145.942/s, 145.942/s/gpu LR: 0.000198 Logit Scale: 23.010 Contrastive_loss: 0.37483 (0.37422) Loss: 0.37483 (0.37422) 2025-03-18,21:48:11 | INFO | Train Epoch: 2 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 143.365/s, 143.365/s/gpu LR: 0.000198 Logit Scale: 22.988 Contrastive_loss: 0.48471 (0.37643) Loss: 0.48471 (0.37643) 2025-03-18,21:48:33 | INFO | Train Epoch: 2 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.221, 145.143/s, 145.143/s/gpu LR: 0.000198 Logit Scale: 23.012 Contrastive_loss: 0.053422 (0.37010) Loss: 0.053422 (0.37010) 2025-03-18,21:48:55 | INFO | Train Epoch: 2 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.219, 147.812/s, 147.812/s/gpu LR: 0.000198 Logit Scale: 22.995 Contrastive_loss: 0.30743 (0.36889) Loss: 0.30743 (0.36889) 2025-03-18,21:49:17 | INFO | Train Epoch: 2 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.219, 145.848/s, 145.848/s/gpu LR: 0.000198 Logit Scale: 23.016 Contrastive_loss: 0.068243 (0.36322) Loss: 0.068243 (0.36322) 2025-03-18,21:49:39 | INFO | Train Epoch: 2 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.220, 145.704/s, 145.704/s/gpu LR: 0.000198 Logit Scale: 22.997 Contrastive_loss: 0.43683 (0.36458) Loss: 0.43683 (0.36458) 2025-03-18,21:50:01 | INFO | Train Epoch: 2 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.219, 146.628/s, 146.628/s/gpu LR: 0.000198 Logit Scale: 23.034 Contrastive_loss: 0.62159 (0.36926) Loss: 0.62159 (0.36926) 2025-03-18,21:50:23 | INFO | Train Epoch: 2 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.220, 147.038/s, 147.038/s/gpu LR: 0.000198 Logit Scale: 23.027 Contrastive_loss: 0.29255 (0.36789) Loss: 0.29255 (0.36789) 2025-03-18,21:50:45 | INFO | Train Epoch: 2 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 145.372/s, 145.372/s/gpu LR: 0.000198 Logit Scale: 23.001 Contrastive_loss: 0.18609 (0.36470) Loss: 0.18609 (0.36470) 2025-03-18,21:51:06 | INFO | Train Epoch: 2 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.219, 146.147/s, 146.147/s/gpu LR: 0.000198 Logit Scale: 23.047 Contrastive_loss: 0.28771 (0.36337) Loss: 0.28771 (0.36337) 2025-03-18,21:51:28 | INFO | Train Epoch: 2 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 148.805/s, 148.805/s/gpu LR: 0.000198 Logit Scale: 23.068 Contrastive_loss: 0.80480 (0.37085) Loss: 0.80480 (0.37085) 2025-03-18,21:51:50 | INFO | Train Epoch: 2 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 149.922/s, 149.922/s/gpu LR: 0.000198 Logit Scale: 23.037 Contrastive_loss: 0.36139 (0.37069) Loss: 0.36139 (0.37069) 2025-03-18,21:52:11 | INFO | Train Epoch: 2 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 148.295/s, 148.295/s/gpu LR: 0.000198 Logit Scale: 23.065 Contrastive_loss: 0.12302 (0.36663) Loss: 0.12302 (0.36663) 2025-03-18,21:52:33 | INFO | Train Epoch: 2 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 150.395/s, 150.395/s/gpu LR: 0.000198 Logit Scale: 23.050 Contrastive_loss: 0.54844 (0.36957) Loss: 0.54844 (0.36957) 2025-03-18,21:52:54 | INFO | Train Epoch: 2 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 138.584/s, 138.584/s/gpu LR: 0.000198 Logit Scale: 23.068 Contrastive_loss: 0.57099 (0.37276) Loss: 0.57099 (0.37276) 2025-03-18,21:53:16 | INFO | Train Epoch: 2 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.219, 145.937/s, 145.937/s/gpu LR: 0.000198 Logit Scale: 23.030 Contrastive_loss: 0.34609 (0.37235) Loss: 0.34609 (0.37235) 2025-03-18,21:53:38 | INFO | Train Epoch: 2 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.218, 145.772/s, 145.772/s/gpu LR: 0.000198 Logit Scale: 23.067 Contrastive_loss: 0.37692 (0.37242) Loss: 0.37692 (0.37242) 2025-03-18,21:54:00 | INFO | Train Epoch: 2 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 148.139/s, 148.139/s/gpu LR: 0.000198 Logit Scale: 23.129 Contrastive_loss: 0.53531 (0.37488) Loss: 0.53531 (0.37488) 2025-03-18,21:54:22 | INFO | Train Epoch: 2 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 147.170/s, 147.170/s/gpu LR: 0.000198 Logit Scale: 23.118 Contrastive_loss: 0.15688 (0.37163) Loss: 0.15688 (0.37163) 2025-03-18,21:54:43 | INFO | Train Epoch: 2 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 148.079/s, 148.079/s/gpu LR: 0.000198 Logit Scale: 23.128 Contrastive_loss: 0.30335 (0.37063) Loss: 0.30335 (0.37063) 2025-03-18,21:55:05 | INFO | Train Epoch: 2 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 149.121/s, 149.121/s/gpu LR: 0.000198 Logit Scale: 23.135 Contrastive_loss: 0.57135 (0.37354) Loss: 0.57135 (0.37354) 2025-03-18,21:55:26 | INFO | Train Epoch: 2 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.162/s, 148.162/s/gpu LR: 0.000198 Logit Scale: 23.177 Contrastive_loss: 0.67131 (0.37779) Loss: 0.67131 (0.37779) 2025-03-18,21:55:48 | INFO | Train Epoch: 2 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.218, 153.523/s, 153.523/s/gpu LR: 0.000198 Logit Scale: 23.187 Contrastive_loss: 0.26008 (0.37613) Loss: 0.26008 (0.37613) 2025-03-18,21:56:10 | INFO | Train Epoch: 2 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 135.551/s, 135.551/s/gpu LR: 0.000198 Logit Scale: 23.203 Contrastive_loss: 0.35437 (0.37583) Loss: 0.35437 (0.37583) 2025-03-18,21:56:31 | INFO | Train Epoch: 2 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 149.030/s, 149.030/s/gpu LR: 0.000198 Logit Scale: 23.199 Contrastive_loss: 0.55660 (0.37831) Loss: 0.55660 (0.37831) 2025-03-18,21:56:53 | INFO | Train Epoch: 2 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.407/s, 149.407/s/gpu LR: 0.000198 Logit Scale: 23.152 Contrastive_loss: 0.65589 (0.38206) Loss: 0.65589 (0.38206) 2025-03-18,21:57:14 | INFO | Train Epoch: 2 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 151.322/s, 151.322/s/gpu LR: 0.000198 Logit Scale: 23.184 Contrastive_loss: 0.64888 (0.38561) Loss: 0.64888 (0.38561) 2025-03-18,21:57:35 | INFO | Train Epoch: 2 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 148.629/s, 148.629/s/gpu LR: 0.000198 Logit Scale: 23.162 Contrastive_loss: 0.66776 (0.38933) Loss: 0.66776 (0.38933) 2025-03-18,21:57:57 | INFO | Train Epoch: 2 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 148.651/s, 148.651/s/gpu LR: 0.000198 Logit Scale: 23.190 Contrastive_loss: 0.57518 (0.39174) Loss: 0.57518 (0.39174) 2025-03-18,21:58:19 | INFO | Train Epoch: 2 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 148.172/s, 148.172/s/gpu LR: 0.000198 Logit Scale: 23.181 Contrastive_loss: 0.26007 (0.39005) Loss: 0.26007 (0.39005) 2025-03-18,21:58:40 | INFO | Train Epoch: 2 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 149.812/s, 149.812/s/gpu LR: 0.000198 Logit Scale: 23.158 Contrastive_loss: 0.53948 (0.39194) Loss: 0.53948 (0.39194) 2025-03-18,21:59:02 | INFO | Train Epoch: 2 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 148.026/s, 148.026/s/gpu LR: 0.000198 Logit Scale: 23.167 Contrastive_loss: 0.28904 (0.39066) Loss: 0.28904 (0.39066) 2025-03-18,21:59:23 | INFO | Train Epoch: 2 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.212, 151.780/s, 151.780/s/gpu LR: 0.000198 Logit Scale: 23.185 Contrastive_loss: 0.72861 (0.39483) Loss: 0.72861 (0.39483) 2025-03-18,21:59:44 | INFO | Train Epoch: 2 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.213, 148.974/s, 148.974/s/gpu LR: 0.000198 Logit Scale: 23.163 Contrastive_loss: 0.59469 (0.39727) Loss: 0.59469 (0.39727) 2025-03-18,22:00:06 | INFO | Train Epoch: 2 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.866/s, 149.866/s/gpu LR: 0.000198 Logit Scale: 23.203 Contrastive_loss: 0.55766 (0.39920) Loss: 0.55766 (0.39920) 2025-03-18,22:00:27 | INFO | Train Epoch: 2 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 151.001/s, 151.001/s/gpu LR: 0.000198 Logit Scale: 23.170 Contrastive_loss: 0.15858 (0.39634) Loss: 0.15858 (0.39634) 2025-03-18,22:00:48 | INFO | Train Epoch: 2 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.213, 149.874/s, 149.874/s/gpu LR: 0.000198 Logit Scale: 23.194 Contrastive_loss: 0.11324 (0.39300) Loss: 0.11324 (0.39300) 2025-03-18,22:01:10 | INFO | Train Epoch: 2 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 150.462/s, 150.462/s/gpu LR: 0.000198 Logit Scale: 23.216 Contrastive_loss: 0.37761 (0.39283) Loss: 0.37761 (0.39283) 2025-03-18,22:01:31 | INFO | Train Epoch: 2 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 150.214/s, 150.214/s/gpu LR: 0.000198 Logit Scale: 23.230 Contrastive_loss: 0.38023 (0.39268) Loss: 0.38023 (0.39268) 2025-03-18,22:01:52 | INFO | Train Epoch: 2 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.581/s, 149.581/s/gpu LR: 0.000198 Logit Scale: 23.226 Contrastive_loss: 0.47747 (0.39364) Loss: 0.47747 (0.39364) 2025-03-18,22:02:14 | INFO | Train Epoch: 2 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 148.305/s, 148.305/s/gpu LR: 0.000198 Logit Scale: 23.257 Contrastive_loss: 0.28418 (0.39241) Loss: 0.28418 (0.39241) 2025-03-18,22:02:36 | INFO | Train Epoch: 2 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 146.277/s, 146.277/s/gpu LR: 0.000198 Logit Scale: 23.201 Contrastive_loss: 0.65931 (0.39538) Loss: 0.65931 (0.39538) 2025-03-18,22:02:57 | INFO | Train Epoch: 2 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.217, 149.543/s, 149.543/s/gpu LR: 0.000198 Logit Scale: 23.196 Contrastive_loss: 0.088165 (0.39200) Loss: 0.088165 (0.39200) 2025-03-18,22:03:19 | INFO | Train Epoch: 2 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.159/s, 149.159/s/gpu LR: 0.000198 Logit Scale: 23.167 Contrastive_loss: 0.20623 (0.38998) Loss: 0.20623 (0.38998) 2025-03-18,22:03:40 | INFO | Train Epoch: 2 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 146.431/s, 146.431/s/gpu LR: 0.000198 Logit Scale: 23.148 Contrastive_loss: 0.27701 (0.38877) Loss: 0.27701 (0.38877) 2025-03-18,22:04:02 | INFO | Train Epoch: 2 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.219, 149.265/s, 149.265/s/gpu LR: 0.000198 Logit Scale: 23.159 Contrastive_loss: 0.22616 (0.38704) Loss: 0.22616 (0.38704) 2025-03-18,22:04:24 | INFO | Train Epoch: 2 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 146.737/s, 146.737/s/gpu LR: 0.000198 Logit Scale: 23.146 Contrastive_loss: 0.40010 (0.38718) Loss: 0.40010 (0.38718) 2025-03-18,22:04:45 | INFO | Train Epoch: 2 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 151.650/s, 151.650/s/gpu LR: 0.000198 Logit Scale: 23.212 Contrastive_loss: 0.67606 (0.39019) Loss: 0.67606 (0.39019) 2025-03-18,22:05:07 | INFO | Train Epoch: 2 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.221, 145.143/s, 145.143/s/gpu LR: 0.000198 Logit Scale: 23.234 Contrastive_loss: 0.82709 (0.39469) Loss: 0.82709 (0.39469) 2025-03-18,22:05:29 | INFO | Train Epoch: 2 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.219, 149.423/s, 149.423/s/gpu LR: 0.000198 Logit Scale: 23.228 Contrastive_loss: 0.41369 (0.39488) Loss: 0.41369 (0.39488) 2025-03-18,22:05:51 | INFO | Train Epoch: 2 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 150.912/s, 150.912/s/gpu LR: 0.000198 Logit Scale: 23.219 Contrastive_loss: 0.61017 (0.39706) Loss: 0.61017 (0.39706) 2025-03-18,22:06:12 | INFO | Train Epoch: 2 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 148.668/s, 148.668/s/gpu LR: 0.000198 Logit Scale: 23.227 Contrastive_loss: 0.52857 (0.39837) Loss: 0.52857 (0.39837) 2025-03-18,22:06:34 | INFO | Train Epoch: 2 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.571/s, 149.571/s/gpu LR: 0.000198 Logit Scale: 23.242 Contrastive_loss: 0.40410 (0.39843) Loss: 0.40410 (0.39843) 2025-03-18,22:06:55 | INFO | Train Epoch: 2 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 152.560/s, 152.560/s/gpu LR: 0.000198 Logit Scale: 23.251 Contrastive_loss: 0.39043 (0.39835) Loss: 0.39043 (0.39835) 2025-03-18,22:07:16 | INFO | Train Epoch: 2 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 134.174/s, 134.174/s/gpu LR: 0.000198 Logit Scale: 23.242 Contrastive_loss: 0.25159 (0.39693) Loss: 0.25159 (0.39693) 2025-03-18,22:07:38 | INFO | Train Epoch: 2 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 149.412/s, 149.412/s/gpu LR: 0.000198 Logit Scale: 23.259 Contrastive_loss: 0.51180 (0.39803) Loss: 0.51180 (0.39803) 2025-03-18,22:07:59 | INFO | Train Epoch: 2 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.386/s, 149.386/s/gpu LR: 0.000198 Logit Scale: 23.250 Contrastive_loss: 0.16773 (0.39584) Loss: 0.16773 (0.39584) 2025-03-18,22:08:21 | INFO | Train Epoch: 2 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.217, 149.260/s, 149.260/s/gpu LR: 0.000198 Logit Scale: 23.253 Contrastive_loss: 0.57017 (0.39748) Loss: 0.57017 (0.39748) 2025-03-18,22:08:43 | INFO | Train Epoch: 2 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.394/s, 148.394/s/gpu LR: 0.000198 Logit Scale: 23.273 Contrastive_loss: 0.46257 (0.39809) Loss: 0.46257 (0.39809) 2025-03-18,22:09:04 | INFO | Train Epoch: 2 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 148.147/s, 148.147/s/gpu LR: 0.000198 Logit Scale: 23.282 Contrastive_loss: 0.52415 (0.39926) Loss: 0.52415 (0.39926) 2025-03-18,22:09:26 | INFO | Train Epoch: 2 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.217, 145.522/s, 145.522/s/gpu LR: 0.000198 Logit Scale: 23.325 Contrastive_loss: 0.37465 (0.39903) Loss: 0.37465 (0.39903) 2025-03-18,22:09:48 | INFO | Train Epoch: 2 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.222, 141.316/s, 141.316/s/gpu LR: 0.000198 Logit Scale: 23.343 Contrastive_loss: 0.37562 (0.39882) Loss: 0.37562 (0.39882) 2025-03-18,22:10:10 | INFO | Train Epoch: 2 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.221, 145.087/s, 145.087/s/gpu LR: 0.000198 Logit Scale: 23.334 Contrastive_loss: 0.21090 (0.39713) Loss: 0.21090 (0.39713) 2025-03-18,22:10:32 | INFO | Train Epoch: 2 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.220, 146.704/s, 146.704/s/gpu LR: 0.000198 Logit Scale: 23.366 Contrastive_loss: 0.22213 (0.39557) Loss: 0.22213 (0.39557) 2025-03-18,22:10:54 | INFO | Train Epoch: 2 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 145.370/s, 145.370/s/gpu LR: 0.000198 Logit Scale: 23.361 Contrastive_loss: 0.12704 (0.39319) Loss: 0.12704 (0.39319) 2025-03-18,22:11:16 | INFO | Train Epoch: 2 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.220, 147.585/s, 147.585/s/gpu LR: 0.000198 Logit Scale: 23.413 Contrastive_loss: 0.75925 (0.39640) Loss: 0.75925 (0.39640) 2025-03-18,22:11:38 | INFO | Train Epoch: 2 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 148.038/s, 148.038/s/gpu LR: 0.000198 Logit Scale: 23.410 Contrastive_loss: 0.28253 (0.39541) Loss: 0.28253 (0.39541) 2025-03-18,22:12:00 | INFO | Train Epoch: 2 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 141.536/s, 141.536/s/gpu LR: 0.000198 Logit Scale: 23.377 Contrastive_loss: 0.52360 (0.39651) Loss: 0.52360 (0.39651) 2025-03-18,22:12:22 | INFO | Train Epoch: 2 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 143.281/s, 143.281/s/gpu LR: 0.000198 Logit Scale: 23.402 Contrastive_loss: 0.89384 (0.40077) Loss: 0.89384 (0.40077) 2025-03-18,22:12:43 | INFO | Train Epoch: 2 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 150.577/s, 150.577/s/gpu LR: 0.000198 Logit Scale: 23.401 Contrastive_loss: 0.64686 (0.40285) Loss: 0.64686 (0.40285) 2025-03-18,22:13:05 | INFO | Train Epoch: 2 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.213, 151.906/s, 151.906/s/gpu LR: 0.000198 Logit Scale: 23.376 Contrastive_loss: 0.36698 (0.40255) Loss: 0.36698 (0.40255) 2025-03-18,22:13:26 | INFO | Train Epoch: 2 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.218, 145.948/s, 145.948/s/gpu LR: 0.000198 Logit Scale: 23.381 Contrastive_loss: 0.74054 (0.40537) Loss: 0.74054 (0.40537) 2025-03-18,22:13:48 | INFO | Train Epoch: 2 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 149.209/s, 149.209/s/gpu LR: 0.000198 Logit Scale: 23.351 Contrastive_loss: 0.47712 (0.40596) Loss: 0.47712 (0.40596) 2025-03-18,22:14:09 | INFO | Train Epoch: 2 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.213, 146.576/s, 146.576/s/gpu LR: 0.000198 Logit Scale: 23.369 Contrastive_loss: 0.31225 (0.40519) Loss: 0.31225 (0.40519) 2025-03-18,22:14:31 | INFO | Train Epoch: 2 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 151.537/s, 151.537/s/gpu LR: 0.000198 Logit Scale: 23.431 Contrastive_loss: 0.29790 (0.40432) Loss: 0.29790 (0.40432) 2025-03-18,22:14:52 | INFO | Train Epoch: 2 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 149.718/s, 149.718/s/gpu LR: 0.000198 Logit Scale: 23.453 Contrastive_loss: 0.33225 (0.40374) Loss: 0.33225 (0.40374) 2025-03-18,22:15:14 | INFO | Train Epoch: 2 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 149.902/s, 149.902/s/gpu LR: 0.000198 Logit Scale: 23.420 Contrastive_loss: 0.29589 (0.40287) Loss: 0.29589 (0.40287) 2025-03-18,22:15:35 | INFO | Train Epoch: 2 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 148.980/s, 148.980/s/gpu LR: 0.000198 Logit Scale: 23.439 Contrastive_loss: 0.11116 (0.40056) Loss: 0.11116 (0.40056) 2025-03-18,22:15:57 | INFO | Train Epoch: 2 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 149.565/s, 149.565/s/gpu LR: 0.000198 Logit Scale: 23.373 Contrastive_loss: 0.19245 (0.39892) Loss: 0.19245 (0.39892) 2025-03-18,22:16:18 | INFO | Train Epoch: 2 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 148.796/s, 148.796/s/gpu LR: 0.000197 Logit Scale: 23.410 Contrastive_loss: 0.068223 (0.39634) Loss: 0.068223 (0.39634) 2025-03-18,22:16:40 | INFO | Train Epoch: 2 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 148.426/s, 148.426/s/gpu LR: 0.000197 Logit Scale: 23.430 Contrastive_loss: 0.17918 (0.39465) Loss: 0.17918 (0.39465) 2025-03-18,22:17:01 | INFO | Train Epoch: 2 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 149.138/s, 149.138/s/gpu LR: 0.000197 Logit Scale: 23.437 Contrastive_loss: 0.36377 (0.39442) Loss: 0.36377 (0.39442) 2025-03-18,22:17:23 | INFO | Train Epoch: 2 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.218, 145.533/s, 145.533/s/gpu LR: 0.000197 Logit Scale: 23.436 Contrastive_loss: 0.41910 (0.39460) Loss: 0.41910 (0.39460) 2025-03-18,22:17:45 | INFO | Train Epoch: 2 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 148.459/s, 148.459/s/gpu LR: 0.000197 Logit Scale: 23.430 Contrastive_loss: 0.14293 (0.39270) Loss: 0.14293 (0.39270) 2025-03-18,22:18:06 | INFO | Train Epoch: 2 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 150.417/s, 150.417/s/gpu LR: 0.000197 Logit Scale: 23.382 Contrastive_loss: 0.56524 (0.39400) Loss: 0.56524 (0.39400) 2025-03-18,22:18:27 | INFO | Train Epoch: 2 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 148.749/s, 148.749/s/gpu LR: 0.000197 Logit Scale: 23.394 Contrastive_loss: 0.35052 (0.39367) Loss: 0.35052 (0.39367) 2025-03-18,22:18:49 | INFO | Train Epoch: 2 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 148.135/s, 148.135/s/gpu LR: 0.000197 Logit Scale: 23.389 Contrastive_loss: 0.49287 (0.39441) Loss: 0.49287 (0.39441) 2025-03-18,22:19:10 | INFO | Train Epoch: 2 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 150.937/s, 150.937/s/gpu LR: 0.000197 Logit Scale: 23.425 Contrastive_loss: 0.47122 (0.39497) Loss: 0.47122 (0.39497) 2025-03-18,22:19:32 | INFO | Train Epoch: 2 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 148.495/s, 148.495/s/gpu LR: 0.000197 Logit Scale: 23.406 Contrastive_loss: 0.32599 (0.39447) Loss: 0.32599 (0.39447) 2025-03-18,22:19:53 | INFO | Train Epoch: 2 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.213, 152.918/s, 152.918/s/gpu LR: 0.000197 Logit Scale: 23.427 Contrastive_loss: 0.45291 (0.39489) Loss: 0.45291 (0.39489) 2025-03-18,22:20:14 | INFO | Train Epoch: 2 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.295/s, 149.295/s/gpu LR: 0.000197 Logit Scale: 23.410 Contrastive_loss: 0.21475 (0.39359) Loss: 0.21475 (0.39359) 2025-03-18,22:20:36 | INFO | Train Epoch: 2 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 148.936/s, 148.936/s/gpu LR: 0.000197 Logit Scale: 23.399 Contrastive_loss: 0.17649 (0.39204) Loss: 0.17649 (0.39204) 2025-03-18,22:20:57 | INFO | Train Epoch: 2 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.336/s, 147.336/s/gpu LR: 0.000197 Logit Scale: 23.413 Contrastive_loss: 0.38631 (0.39200) Loss: 0.38631 (0.39200) 2025-03-18,22:21:19 | INFO | Train Epoch: 2 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 149.384/s, 149.384/s/gpu LR: 0.000197 Logit Scale: 23.443 Contrastive_loss: 0.66955 (0.39396) Loss: 0.66955 (0.39396) 2025-03-18,22:21:40 | INFO | Train Epoch: 2 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.141/s, 149.141/s/gpu LR: 0.000197 Logit Scale: 23.474 Contrastive_loss: 0.37782 (0.39385) Loss: 0.37782 (0.39385) 2025-03-18,22:22:02 | INFO | Train Epoch: 2 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 151.620/s, 151.620/s/gpu LR: 0.000197 Logit Scale: 23.487 Contrastive_loss: 0.23731 (0.39276) Loss: 0.23731 (0.39276) 2025-03-18,22:22:23 | INFO | Train Epoch: 2 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.415/s, 149.415/s/gpu LR: 0.000197 Logit Scale: 23.410 Contrastive_loss: 0.27890 (0.39197) Loss: 0.27890 (0.39197) 2025-03-18,22:22:45 | INFO | Train Epoch: 2 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 149.447/s, 149.447/s/gpu LR: 0.000197 Logit Scale: 23.411 Contrastive_loss: 0.36106 (0.39176) Loss: 0.36106 (0.39176) 2025-03-18,22:23:06 | INFO | Train Epoch: 2 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.853/s, 148.853/s/gpu LR: 0.000197 Logit Scale: 23.452 Contrastive_loss: 0.29631 (0.39111) Loss: 0.29631 (0.39111) 2025-03-18,22:23:28 | INFO | Train Epoch: 2 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.438/s, 149.438/s/gpu LR: 0.000197 Logit Scale: 23.414 Contrastive_loss: 0.23005 (0.39002) Loss: 0.23005 (0.39002) 2025-03-18,22:23:49 | INFO | Train Epoch: 2 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 148.918/s, 148.918/s/gpu LR: 0.000197 Logit Scale: 23.368 Contrastive_loss: 0.92011 (0.39358) Loss: 0.92011 (0.39358) 2025-03-18,22:24:11 | INFO | Train Epoch: 2 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 149.768/s, 149.768/s/gpu LR: 0.000197 Logit Scale: 23.388 Contrastive_loss: 0.21986 (0.39242) Loss: 0.21986 (0.39242) 2025-03-18,22:24:32 | INFO | Train Epoch: 2 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.377/s, 148.377/s/gpu LR: 0.000197 Logit Scale: 23.405 Contrastive_loss: 0.33528 (0.39204) Loss: 0.33528 (0.39204) 2025-03-18,22:24:54 | INFO | Train Epoch: 2 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 150.028/s, 150.028/s/gpu LR: 0.000197 Logit Scale: 23.352 Contrastive_loss: 0.28770 (0.39136) Loss: 0.28770 (0.39136) 2025-03-18,22:25:15 | INFO | Train Epoch: 2 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.392/s, 149.392/s/gpu LR: 0.000197 Logit Scale: 23.387 Contrastive_loss: 0.33825 (0.39101) Loss: 0.33825 (0.39101) 2025-03-18,22:25:37 | INFO | Train Epoch: 2 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.272/s, 149.272/s/gpu LR: 0.000197 Logit Scale: 23.366 Contrastive_loss: 0.15458 (0.38948) Loss: 0.15458 (0.38948) 2025-03-18,22:25:58 | INFO | Train Epoch: 2 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.228/s, 149.228/s/gpu LR: 0.000197 Logit Scale: 23.375 Contrastive_loss: 0.29712 (0.38888) Loss: 0.29712 (0.38888) 2025-03-18,22:26:20 | INFO | Train Epoch: 2 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.531/s, 149.531/s/gpu LR: 0.000197 Logit Scale: 23.363 Contrastive_loss: 0.55921 (0.38997) Loss: 0.55921 (0.38997) 2025-03-18,22:26:41 | INFO | Train Epoch: 2 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 150.199/s, 150.199/s/gpu LR: 0.000197 Logit Scale: 23.381 Contrastive_loss: 0.35231 (0.38973) Loss: 0.35231 (0.38973) 2025-03-18,22:27:03 | INFO | Train Epoch: 2 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.341/s, 149.341/s/gpu LR: 0.000197 Logit Scale: 23.358 Contrastive_loss: 0.48605 (0.39034) Loss: 0.48605 (0.39034) 2025-03-18,22:27:24 | INFO | Train Epoch: 2 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 148.864/s, 148.864/s/gpu LR: 0.000197 Logit Scale: 23.383 Contrastive_loss: 0.30590 (0.38981) Loss: 0.30590 (0.38981) 2025-03-18,22:27:46 | INFO | Train Epoch: 2 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.105/s, 150.105/s/gpu LR: 0.000197 Logit Scale: 23.324 Contrastive_loss: 0.48445 (0.39040) Loss: 0.48445 (0.39040) 2025-03-18,22:28:07 | INFO | Train Epoch: 2 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 147.773/s, 147.773/s/gpu LR: 0.000197 Logit Scale: 23.325 Contrastive_loss: 0.33435 (0.39005) Loss: 0.33435 (0.39005) 2025-03-18,22:28:29 | INFO | Train Epoch: 2 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 149.848/s, 149.848/s/gpu LR: 0.000197 Logit Scale: 23.321 Contrastive_loss: 0.31648 (0.38960) Loss: 0.31648 (0.38960) 2025-03-18,22:28:50 | INFO | Train Epoch: 2 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 149.036/s, 149.036/s/gpu LR: 0.000197 Logit Scale: 23.301 Contrastive_loss: 0.80146 (0.39213) Loss: 0.80146 (0.39213) 2025-03-18,22:29:11 | INFO | Train Epoch: 2 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 149.449/s, 149.449/s/gpu LR: 0.000197 Logit Scale: 23.302 Contrastive_loss: 0.69376 (0.39397) Loss: 0.69376 (0.39397) 2025-03-18,22:29:33 | INFO | Train Epoch: 2 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 149.440/s, 149.440/s/gpu LR: 0.000197 Logit Scale: 23.325 Contrastive_loss: 0.35186 (0.39371) Loss: 0.35186 (0.39371) 2025-03-18,22:29:55 | INFO | Train Epoch: 2 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 148.365/s, 148.365/s/gpu LR: 0.000197 Logit Scale: 23.386 Contrastive_loss: 0.84576 (0.39643) Loss: 0.84576 (0.39643) 2025-03-18,22:30:16 | INFO | Train Epoch: 2 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 148.750/s, 148.750/s/gpu LR: 0.000197 Logit Scale: 23.365 Contrastive_loss: 0.39514 (0.39643) Loss: 0.39514 (0.39643) 2025-03-18,22:30:38 | INFO | Train Epoch: 2 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 150.197/s, 150.197/s/gpu LR: 0.000197 Logit Scale: 23.397 Contrastive_loss: 0.69077 (0.39818) Loss: 0.69077 (0.39818) 2025-03-18,22:30:59 | INFO | Train Epoch: 2 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 149.143/s, 149.143/s/gpu LR: 0.000197 Logit Scale: 23.362 Contrastive_loss: 0.24391 (0.39726) Loss: 0.24391 (0.39726) 2025-03-18,22:31:21 | INFO | Train Epoch: 2 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 148.387/s, 148.387/s/gpu LR: 0.000197 Logit Scale: 23.372 Contrastive_loss: 0.15021 (0.39581) Loss: 0.15021 (0.39581) 2025-03-18,22:31:43 | INFO | Train Epoch: 2 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 137.780/s, 137.780/s/gpu LR: 0.000197 Logit Scale: 23.380 Contrastive_loss: 0.37675 (0.39570) Loss: 0.37675 (0.39570) 2025-03-18,22:32:05 | INFO | Train Epoch: 2 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 145.102/s, 145.102/s/gpu LR: 0.000197 Logit Scale: 23.382 Contrastive_loss: 0.25961 (0.39491) Loss: 0.25961 (0.39491) 2025-03-18,22:32:27 | INFO | Train Epoch: 2 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.221, 145.147/s, 145.147/s/gpu LR: 0.000197 Logit Scale: 23.404 Contrastive_loss: 0.32417 (0.39450) Loss: 0.32417 (0.39450) 2025-03-18,22:32:48 | INFO | Train Epoch: 2 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 146.025/s, 146.025/s/gpu LR: 0.000197 Logit Scale: 23.439 Contrastive_loss: 0.27291 (0.39380) Loss: 0.27291 (0.39380) 2025-03-18,22:33:10 | INFO | Train Epoch: 2 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 151.215/s, 151.215/s/gpu LR: 0.000197 Logit Scale: 23.419 Contrastive_loss: 0.23742 (0.39291) Loss: 0.23742 (0.39291) 2025-03-18,22:33:31 | INFO | Train Epoch: 2 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.431/s, 149.431/s/gpu LR: 0.000197 Logit Scale: 23.413 Contrastive_loss: 0.55259 (0.39382) Loss: 0.55259 (0.39382) 2025-03-18,22:33:53 | INFO | Train Epoch: 2 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 145.667/s, 145.667/s/gpu LR: 0.000197 Logit Scale: 23.426 Contrastive_loss: 0.34869 (0.39356) Loss: 0.34869 (0.39356) 2025-03-18,22:34:14 | INFO | Train Epoch: 2 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.438/s, 149.438/s/gpu LR: 0.000197 Logit Scale: 23.417 Contrastive_loss: 0.47941 (0.39404) Loss: 0.47941 (0.39404) 2025-03-18,22:34:36 | INFO | Train Epoch: 2 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 148.808/s, 148.808/s/gpu LR: 0.000197 Logit Scale: 23.445 Contrastive_loss: 0.49115 (0.39458) Loss: 0.49115 (0.39458) 2025-03-18,22:34:58 | INFO | Train Epoch: 2 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 148.113/s, 148.113/s/gpu LR: 0.000197 Logit Scale: 23.407 Contrastive_loss: 0.57569 (0.39559) Loss: 0.57569 (0.39559) 2025-03-18,22:35:19 | INFO | Train Epoch: 2 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 151.266/s, 151.266/s/gpu LR: 0.000197 Logit Scale: 23.459 Contrastive_loss: 0.67345 (0.39713) Loss: 0.67345 (0.39713) 2025-03-18,22:35:41 | INFO | Train Epoch: 2 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.217, 145.623/s, 145.623/s/gpu LR: 0.000197 Logit Scale: 23.426 Contrastive_loss: 0.31094 (0.39665) Loss: 0.31094 (0.39665) 2025-03-18,22:36:03 | INFO | Train Epoch: 2 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 146.099/s, 146.099/s/gpu LR: 0.000197 Logit Scale: 23.452 Contrastive_loss: 0.51492 (0.39730) Loss: 0.51492 (0.39730) 2025-03-18,22:36:25 | INFO | Train Epoch: 2 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 146.927/s, 146.927/s/gpu LR: 0.000197 Logit Scale: 23.426 Contrastive_loss: 0.36150 (0.39710) Loss: 0.36150 (0.39710) 2025-03-18,22:36:47 | INFO | Train Epoch: 2 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.220, 146.116/s, 146.116/s/gpu LR: 0.000197 Logit Scale: 23.450 Contrastive_loss: 0.40087 (0.39712) Loss: 0.40087 (0.39712) 2025-03-18,22:37:09 | INFO | Train Epoch: 2 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.220, 144.812/s, 144.812/s/gpu LR: 0.000197 Logit Scale: 23.443 Contrastive_loss: 0.27404 (0.39646) Loss: 0.27404 (0.39646) 2025-03-18,22:37:31 | INFO | Train Epoch: 2 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.219, 144.740/s, 144.740/s/gpu LR: 0.000197 Logit Scale: 23.439 Contrastive_loss: 0.51934 (0.39712) Loss: 0.51934 (0.39712) 2025-03-18,22:37:52 | INFO | Train Epoch: 2 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.219, 145.017/s, 145.017/s/gpu LR: 0.000197 Logit Scale: 23.455 Contrastive_loss: 0.22213 (0.39619) Loss: 0.22213 (0.39619) 2025-03-18,22:38:14 | INFO | Train Epoch: 2 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.220, 145.051/s, 145.051/s/gpu LR: 0.000197 Logit Scale: 23.488 Contrastive_loss: 0.30665 (0.39572) Loss: 0.30665 (0.39572) 2025-03-18,22:38:37 | INFO | Train Epoch: 2 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.222, 146.610/s, 146.610/s/gpu LR: 0.000197 Logit Scale: 23.513 Contrastive_loss: 0.30084 (0.39522) Loss: 0.30084 (0.39522) 2025-03-18,22:38:59 | INFO | Train Epoch: 2 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.222, 141.244/s, 141.244/s/gpu LR: 0.000197 Logit Scale: 23.495 Contrastive_loss: 0.69632 (0.39679) Loss: 0.69632 (0.39679) 2025-03-18,22:39:21 | INFO | Train Epoch: 2 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.223, 145.107/s, 145.107/s/gpu LR: 0.000197 Logit Scale: 23.542 Contrastive_loss: 0.68287 (0.39828) Loss: 0.68287 (0.39828) 2025-03-18,22:39:43 | INFO | Train Epoch: 2 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.223, 146.607/s, 146.607/s/gpu LR: 0.000197 Logit Scale: 23.584 Contrastive_loss: 0.34190 (0.39799) Loss: 0.34190 (0.39799) 2025-03-18,22:40:05 | INFO | Train Epoch: 2 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 145.695/s, 145.695/s/gpu LR: 0.000197 Logit Scale: 23.618 Contrastive_loss: 0.32755 (0.39763) Loss: 0.32755 (0.39763) 2025-03-18,22:40:27 | INFO | Train Epoch: 2 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 144.858/s, 144.858/s/gpu LR: 0.000197 Logit Scale: 23.620 Contrastive_loss: 0.29868 (0.39712) Loss: 0.29868 (0.39712) 2025-03-18,22:40:50 | INFO | Train Epoch: 2 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.221, 142.149/s, 142.149/s/gpu LR: 0.000197 Logit Scale: 23.606 Contrastive_loss: 0.58240 (0.39807) Loss: 0.58240 (0.39807) 2025-03-18,22:41:11 | INFO | Train Epoch: 2 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 153.072/s, 153.072/s/gpu LR: 0.000197 Logit Scale: 23.610 Contrastive_loss: 0.43707 (0.39826) Loss: 0.43707 (0.39826) 2025-03-18,22:41:33 | INFO | Train Epoch: 2 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 141.355/s, 141.355/s/gpu LR: 0.000197 Logit Scale: 23.593 Contrastive_loss: 0.43630 (0.39846) Loss: 0.43630 (0.39846) 2025-03-18,22:41:54 | INFO | Train Epoch: 2 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 142.050/s, 142.050/s/gpu LR: 0.000197 Logit Scale: 23.597 Contrastive_loss: 0.33438 (0.39813) Loss: 0.33438 (0.39813) 2025-03-18,22:42:16 | INFO | Train Epoch: 2 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 149.155/s, 149.155/s/gpu LR: 0.000197 Logit Scale: 23.580 Contrastive_loss: 0.46167 (0.39845) Loss: 0.46167 (0.39845) 2025-03-18,22:42:37 | INFO | Train Epoch: 2 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.213, 148.721/s, 148.721/s/gpu LR: 0.000197 Logit Scale: 23.584 Contrastive_loss: 0.25567 (0.39774) Loss: 0.25567 (0.39774) 2025-03-18,22:42:59 | INFO | Train Epoch: 2 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 146.224/s, 146.224/s/gpu LR: 0.000197 Logit Scale: 23.592 Contrastive_loss: 0.41630 (0.39783) Loss: 0.41630 (0.39783) 2025-03-18,22:43:21 | INFO | Train Epoch: 2 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 144.930/s, 144.930/s/gpu LR: 0.000197 Logit Scale: 23.636 Contrastive_loss: 0.78356 (0.39973) Loss: 0.78356 (0.39973) 2025-03-18,22:43:42 | INFO | Train Epoch: 2 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 146.041/s, 146.041/s/gpu LR: 0.000197 Logit Scale: 23.607 Contrastive_loss: 0.47197 (0.40009) Loss: 0.47197 (0.40009) 2025-03-18,22:44:04 | INFO | Train Epoch: 2 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.219, 145.522/s, 145.522/s/gpu LR: 0.000197 Logit Scale: 23.612 Contrastive_loss: 0.60913 (0.40111) Loss: 0.60913 (0.40111) 2025-03-18,22:44:26 | INFO | Train Epoch: 2 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.218, 149.787/s, 149.787/s/gpu LR: 0.000197 Logit Scale: 23.642 Contrastive_loss: 0.48433 (0.40151) Loss: 0.48433 (0.40151) 2025-03-18,22:44:48 | INFO | Train Epoch: 2 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.324/s, 148.324/s/gpu LR: 0.000197 Logit Scale: 23.617 Contrastive_loss: 0.12149 (0.40016) Loss: 0.12149 (0.40016) 2025-03-18,22:45:09 | INFO | Train Epoch: 2 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 147.810/s, 147.810/s/gpu LR: 0.000197 Logit Scale: 23.653 Contrastive_loss: 0.48664 (0.40057) Loss: 0.48664 (0.40057) 2025-03-18,22:45:31 | INFO | Train Epoch: 2 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 149.871/s, 149.871/s/gpu LR: 0.000197 Logit Scale: 23.632 Contrastive_loss: 0.42628 (0.40070) Loss: 0.42628 (0.40070) 2025-03-18,22:45:53 | INFO | Train Epoch: 2 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.218, 147.066/s, 147.066/s/gpu LR: 0.000197 Logit Scale: 23.627 Contrastive_loss: 0.22859 (0.39988) Loss: 0.22859 (0.39988) 2025-03-18,22:46:15 | INFO | Train Epoch: 2 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.220, 145.425/s, 145.425/s/gpu LR: 0.000197 Logit Scale: 23.599 Contrastive_loss: 0.17243 (0.39880) Loss: 0.17243 (0.39880) 2025-03-18,22:46:37 | INFO | Train Epoch: 2 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.220, 145.985/s, 145.985/s/gpu LR: 0.000197 Logit Scale: 23.607 Contrastive_loss: 0.25864 (0.39814) Loss: 0.25864 (0.39814) 2025-03-18,22:46:59 | INFO | Train Epoch: 2 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.221, 146.324/s, 146.324/s/gpu LR: 0.000197 Logit Scale: 23.617 Contrastive_loss: 0.29979 (0.39768) Loss: 0.29979 (0.39768) 2025-03-18,22:47:20 | INFO | Train Epoch: 2 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 148.461/s, 148.461/s/gpu LR: 0.000197 Logit Scale: 23.561 Contrastive_loss: 0.38072 (0.39760) Loss: 0.38072 (0.39760) 2025-03-18,22:47:42 | INFO | Train Epoch: 2 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 149.472/s, 149.472/s/gpu LR: 0.000197 Logit Scale: 23.633 Contrastive_loss: 0.28984 (0.39710) Loss: 0.28984 (0.39710) 2025-03-18,22:48:03 | INFO | Train Epoch: 2 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 149.812/s, 149.812/s/gpu LR: 0.000197 Logit Scale: 23.625 Contrastive_loss: 0.43471 (0.39727) Loss: 0.43471 (0.39727) 2025-03-18,22:48:25 | INFO | Train Epoch: 2 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 147.752/s, 147.752/s/gpu LR: 0.000197 Logit Scale: 23.618 Contrastive_loss: 0.38349 (0.39721) Loss: 0.38349 (0.39721) 2025-03-18,22:48:47 | INFO | Train Epoch: 2 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 147.815/s, 147.815/s/gpu LR: 0.000197 Logit Scale: 23.627 Contrastive_loss: 0.15587 (0.39610) Loss: 0.15587 (0.39610) 2025-03-18,22:49:08 | INFO | Train Epoch: 2 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 144.638/s, 144.638/s/gpu LR: 0.000197 Logit Scale: 23.616 Contrastive_loss: 0.18760 (0.39515) Loss: 0.18760 (0.39515) 2025-03-18,22:49:31 | INFO | Train Epoch: 2 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.222, 145.500/s, 145.500/s/gpu LR: 0.000197 Logit Scale: 23.600 Contrastive_loss: 0.43913 (0.39535) Loss: 0.43913 (0.39535) 2025-03-18,22:49:53 | INFO | Train Epoch: 2 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.220, 145.969/s, 145.969/s/gpu LR: 0.000196 Logit Scale: 23.640 Contrastive_loss: 0.32064 (0.39501) Loss: 0.32064 (0.39501) 2025-03-18,22:50:14 | INFO | Train Epoch: 2 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.218, 150.010/s, 150.010/s/gpu LR: 0.000196 Logit Scale: 23.626 Contrastive_loss: 0.20581 (0.39416) Loss: 0.20581 (0.39416) 2025-03-18,22:50:36 | INFO | Train Epoch: 2 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.213, 150.440/s, 150.440/s/gpu LR: 0.000196 Logit Scale: 23.646 Contrastive_loss: 0.13069 (0.39298) Loss: 0.13069 (0.39298) 2025-03-18,22:50:57 | INFO | Train Epoch: 2 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 148.150/s, 148.150/s/gpu LR: 0.000196 Logit Scale: 23.656 Contrastive_loss: 0.74332 (0.39454) Loss: 0.74332 (0.39454) 2025-03-18,22:51:19 | INFO | Train Epoch: 2 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.108/s, 148.108/s/gpu LR: 0.000196 Logit Scale: 23.645 Contrastive_loss: 0.54396 (0.39520) Loss: 0.54396 (0.39520) 2025-03-18,22:51:40 | INFO | Train Epoch: 2 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.310/s, 148.310/s/gpu LR: 0.000196 Logit Scale: 23.665 Contrastive_loss: 0.16144 (0.39417) Loss: 0.16144 (0.39417) 2025-03-18,22:52:02 | INFO | Train Epoch: 2 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.268/s, 149.268/s/gpu LR: 0.000196 Logit Scale: 23.670 Contrastive_loss: 0.12019 (0.39296) Loss: 0.12019 (0.39296) 2025-03-18,22:52:24 | INFO | Train Epoch: 2 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.220, 147.298/s, 147.298/s/gpu LR: 0.000196 Logit Scale: 23.732 Contrastive_loss: 0.38816 (0.39294) Loss: 0.38816 (0.39294) 2025-03-18,22:52:46 | INFO | Train Epoch: 2 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.219, 147.166/s, 147.166/s/gpu LR: 0.000196 Logit Scale: 23.695 Contrastive_loss: 0.30901 (0.39257) Loss: 0.30901 (0.39257) 2025-03-18,22:53:08 | INFO | Train Epoch: 2 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.219, 145.728/s, 145.728/s/gpu LR: 0.000196 Logit Scale: 23.707 Contrastive_loss: 0.27834 (0.39208) Loss: 0.27834 (0.39208) 2025-03-18,22:53:30 | INFO | Train Epoch: 2 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.221, 143.020/s, 143.020/s/gpu LR: 0.000196 Logit Scale: 23.700 Contrastive_loss: 0.52746 (0.39266) Loss: 0.52746 (0.39266) 2025-03-18,22:53:52 | INFO | Train Epoch: 2 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.222, 143.269/s, 143.269/s/gpu LR: 0.000196 Logit Scale: 23.690 Contrastive_loss: 0.55368 (0.39336) Loss: 0.55368 (0.39336) 2025-03-18,22:54:14 | INFO | Train Epoch: 2 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.220, 146.914/s, 146.914/s/gpu LR: 0.000196 Logit Scale: 23.663 Contrastive_loss: 0.35394 (0.39319) Loss: 0.35394 (0.39319) 2025-03-18,22:54:36 | INFO | Train Epoch: 2 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.220, 146.429/s, 146.429/s/gpu LR: 0.000196 Logit Scale: 23.701 Contrastive_loss: 0.23816 (0.39253) Loss: 0.23816 (0.39253) 2025-03-18,22:54:58 | INFO | Train Epoch: 2 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.221, 143.776/s, 143.776/s/gpu LR: 0.000196 Logit Scale: 23.673 Contrastive_loss: 0.38702 (0.39250) Loss: 0.38702 (0.39250) 2025-03-18,22:55:20 | INFO | Train Epoch: 2 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.221, 145.467/s, 145.467/s/gpu LR: 0.000196 Logit Scale: 23.684 Contrastive_loss: 0.43808 (0.39270) Loss: 0.43808 (0.39270) 2025-03-18,22:55:42 | INFO | Train Epoch: 2 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.221, 147.038/s, 147.038/s/gpu LR: 0.000196 Logit Scale: 23.703 Contrastive_loss: 0.14305 (0.39164) Loss: 0.14305 (0.39164) 2025-03-18,22:56:04 | INFO | Train Epoch: 2 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.219, 145.456/s, 145.456/s/gpu LR: 0.000196 Logit Scale: 23.654 Contrastive_loss: 0.46292 (0.39194) Loss: 0.46292 (0.39194) 2025-03-18,22:56:26 | INFO | Train Epoch: 2 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.220, 147.111/s, 147.111/s/gpu LR: 0.000196 Logit Scale: 23.693 Contrastive_loss: 0.43293 (0.39211) Loss: 0.43293 (0.39211) 2025-03-18,22:56:48 | INFO | Train Epoch: 2 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.221, 147.167/s, 147.167/s/gpu LR: 0.000196 Logit Scale: 23.726 Contrastive_loss: 0.11722 (0.39097) Loss: 0.11722 (0.39097) 2025-03-18,22:56:56 | INFO | Train Epoch: 2 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.218, 148.740/s, 148.740/s/gpu LR: 0.000196 Logit Scale: 23.722 Contrastive_loss: 0.098732 (0.38976) Loss: 0.098732 (0.38976) 2025-03-18,22:56:57 | INFO | Eval Epoch: 3 [32 / 7443] Clip Loss: 3.612717 2025-03-18,22:57:02 | INFO | Eval Epoch: 3 [3232 / 7443] Clip Loss: 1.022874 2025-03-18,22:57:08 | INFO | Eval Epoch: 3 [6432 / 7443] Clip Loss: 0.778979 2025-03-18,22:57:10 | INFO | Eval Epoch: 3 image_to_text_mean_rank: 121.0059 image_to_text_median_rank: 9.0000 image_to_text_R@1: 0.1014 image_to_text_R@5: 0.3671 image_to_text_R@10: 0.5406 text_to_image_mean_rank: 80.4938 text_to_image_median_rank: 9.0000 text_to_image_R@1: 0.1014 text_to_image_R@5: 0.3667 text_to_image_R@10: 0.5419 clip_val_loss: 0.7330 epoch: 3.0000 num_samples: 7443.0000 2025-03-18,22:57:44 | INFO | Start epoch 3 2025-03-18,22:57:44 | INFO | Train Epoch: 3 [ 32/766009 (0%)] Data (t): 0.161 Batch (t): 0.372, 85.9570/s, 85.9570/s/gpu LR: 0.000196 Logit Scale: 23.723 Contrastive_loss: 0.32410 (0.32410) Loss: 0.32410 (0.32410) 2025-03-18,22:58:06 | INFO | Train Epoch: 3 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 144.556/s, 144.556/s/gpu LR: 0.000196 Logit Scale: 23.786 Contrastive_loss: 0.59476 (0.45943) Loss: 0.59476 (0.45943) 2025-03-18,22:58:28 | INFO | Train Epoch: 3 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.221, 145.396/s, 145.396/s/gpu LR: 0.000196 Logit Scale: 23.738 Contrastive_loss: 0.44356 (0.45414) Loss: 0.44356 (0.45414) 2025-03-18,22:58:50 | INFO | Train Epoch: 3 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.222, 146.457/s, 146.457/s/gpu LR: 0.000196 Logit Scale: 23.763 Contrastive_loss: 0.42362 (0.44651) Loss: 0.42362 (0.44651) 2025-03-18,22:59:12 | INFO | Train Epoch: 3 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 150.388/s, 150.388/s/gpu LR: 0.000196 Logit Scale: 23.806 Contrastive_loss: 0.32393 (0.42199) Loss: 0.32393 (0.42199) 2025-03-18,22:59:33 | INFO | Train Epoch: 3 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 148.744/s, 148.744/s/gpu LR: 0.000196 Logit Scale: 23.831 Contrastive_loss: 0.10578 (0.36929) Loss: 0.10578 (0.36929) 2025-03-18,22:59:55 | INFO | Train Epoch: 3 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.218, 146.654/s, 146.654/s/gpu LR: 0.000196 Logit Scale: 23.839 Contrastive_loss: 0.25665 (0.35320) Loss: 0.25665 (0.35320) 2025-03-18,23:00:17 | INFO | Train Epoch: 3 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 147.837/s, 147.837/s/gpu LR: 0.000196 Logit Scale: 23.818 Contrastive_loss: 0.43147 (0.36298) Loss: 0.43147 (0.36298) 2025-03-18,23:00:38 | INFO | Train Epoch: 3 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.218, 149.427/s, 149.427/s/gpu LR: 0.000196 Logit Scale: 23.754 Contrastive_loss: 0.48151 (0.37615) Loss: 0.48151 (0.37615) 2025-03-18,23:01:00 | INFO | Train Epoch: 3 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 150.057/s, 150.057/s/gpu LR: 0.000196 Logit Scale: 23.781 Contrastive_loss: 0.44379 (0.38292) Loss: 0.44379 (0.38292) 2025-03-18,23:01:21 | INFO | Train Epoch: 3 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 149.643/s, 149.643/s/gpu LR: 0.000196 Logit Scale: 23.746 Contrastive_loss: 0.73016 (0.41449) Loss: 0.73016 (0.41449) 2025-03-18,23:01:42 | INFO | Train Epoch: 3 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 151.056/s, 151.056/s/gpu LR: 0.000196 Logit Scale: 23.728 Contrastive_loss: 0.67498 (0.43619) Loss: 0.67498 (0.43619) 2025-03-18,23:02:04 | INFO | Train Epoch: 3 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 147.356/s, 147.356/s/gpu LR: 0.000196 Logit Scale: 23.722 Contrastive_loss: 0.25468 (0.42223) Loss: 0.25468 (0.42223) 2025-03-18,23:02:25 | INFO | Train Epoch: 3 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.917/s, 149.917/s/gpu LR: 0.000196 Logit Scale: 23.733 Contrastive_loss: 0.17980 (0.40491) Loss: 0.17980 (0.40491) 2025-03-18,23:02:47 | INFO | Train Epoch: 3 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 150.431/s, 150.431/s/gpu LR: 0.000196 Logit Scale: 23.749 Contrastive_loss: 0.22400 (0.39285) Loss: 0.22400 (0.39285) 2025-03-18,23:03:08 | INFO | Train Epoch: 3 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.212, 151.577/s, 151.577/s/gpu LR: 0.000196 Logit Scale: 23.777 Contrastive_loss: 0.33170 (0.38903) Loss: 0.33170 (0.38903) 2025-03-18,23:03:29 | INFO | Train Epoch: 3 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.213, 149.473/s, 149.473/s/gpu LR: 0.000196 Logit Scale: 23.785 Contrastive_loss: 0.41155 (0.39036) Loss: 0.41155 (0.39036) 2025-03-18,23:03:51 | INFO | Train Epoch: 3 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 147.784/s, 147.784/s/gpu LR: 0.000196 Logit Scale: 23.805 Contrastive_loss: 0.52226 (0.39768) Loss: 0.52226 (0.39768) 2025-03-18,23:04:12 | INFO | Train Epoch: 3 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 148.509/s, 148.509/s/gpu LR: 0.000196 Logit Scale: 23.776 Contrastive_loss: 0.54253 (0.40531) Loss: 0.54253 (0.40531) 2025-03-18,23:04:34 | INFO | Train Epoch: 3 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 148.241/s, 148.241/s/gpu LR: 0.000196 Logit Scale: 23.766 Contrastive_loss: 0.61608 (0.41585) Loss: 0.61608 (0.41585) 2025-03-18,23:04:55 | INFO | Train Epoch: 3 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 150.465/s, 150.465/s/gpu LR: 0.000196 Logit Scale: 23.803 Contrastive_loss: 0.14287 (0.40285) Loss: 0.14287 (0.40285) 2025-03-18,23:05:17 | INFO | Train Epoch: 3 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 148.204/s, 148.204/s/gpu LR: 0.000196 Logit Scale: 23.852 Contrastive_loss: 0.38670 (0.40211) Loss: 0.38670 (0.40211) 2025-03-18,23:05:39 | INFO | Train Epoch: 3 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.218, 143.613/s, 143.613/s/gpu LR: 0.000196 Logit Scale: 23.824 Contrastive_loss: 0.54001 (0.40811) Loss: 0.54001 (0.40811) 2025-03-18,23:06:01 | INFO | Train Epoch: 3 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.218, 146.316/s, 146.316/s/gpu LR: 0.000196 Logit Scale: 23.865 Contrastive_loss: 0.41561 (0.40842) Loss: 0.41561 (0.40842) 2025-03-18,23:06:22 | INFO | Train Epoch: 3 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 142.742/s, 142.742/s/gpu LR: 0.000196 Logit Scale: 23.829 Contrastive_loss: 0.39997 (0.40808) Loss: 0.39997 (0.40808) 2025-03-18,23:06:44 | INFO | Train Epoch: 3 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 145.202/s, 145.202/s/gpu LR: 0.000196 Logit Scale: 23.807 Contrastive_loss: 0.42564 (0.40876) Loss: 0.42564 (0.40876) 2025-03-18,23:07:06 | INFO | Train Epoch: 3 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.219, 146.395/s, 146.395/s/gpu LR: 0.000196 Logit Scale: 23.787 Contrastive_loss: 0.26778 (0.40354) Loss: 0.26778 (0.40354) 2025-03-18,23:07:28 | INFO | Train Epoch: 3 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.219, 148.040/s, 148.040/s/gpu LR: 0.000196 Logit Scale: 23.751 Contrastive_loss: 0.17101 (0.39523) Loss: 0.17101 (0.39523) 2025-03-18,23:07:50 | INFO | Train Epoch: 3 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 146.605/s, 146.605/s/gpu LR: 0.000196 Logit Scale: 23.783 Contrastive_loss: 0.38718 (0.39495) Loss: 0.38718 (0.39495) 2025-03-18,23:08:12 | INFO | Train Epoch: 3 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.221, 170.601/s, 170.601/s/gpu LR: 0.000196 Logit Scale: 23.787 Contrastive_loss: 0.53507 (0.39962) Loss: 0.53507 (0.39962) 2025-03-18,23:08:34 | INFO | Train Epoch: 3 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.219, 147.788/s, 147.788/s/gpu LR: 0.000196 Logit Scale: 23.789 Contrastive_loss: 0.19206 (0.39293) Loss: 0.19206 (0.39293) 2025-03-18,23:08:56 | INFO | Train Epoch: 3 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.220, 143.221/s, 143.221/s/gpu LR: 0.000196 Logit Scale: 23.835 Contrastive_loss: 0.50728 (0.39650) Loss: 0.50728 (0.39650) 2025-03-18,23:09:18 | INFO | Train Epoch: 3 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.221, 146.227/s, 146.227/s/gpu LR: 0.000196 Logit Scale: 23.833 Contrastive_loss: 0.17366 (0.38975) Loss: 0.17366 (0.38975) 2025-03-18,23:09:40 | INFO | Train Epoch: 3 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 146.369/s, 146.369/s/gpu LR: 0.000196 Logit Scale: 23.846 Contrastive_loss: 0.21685 (0.38466) Loss: 0.21685 (0.38466) 2025-03-18,23:10:01 | INFO | Train Epoch: 3 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.213, 152.143/s, 152.143/s/gpu LR: 0.000196 Logit Scale: 23.837 Contrastive_loss: 0.44805 (0.38648) Loss: 0.44805 (0.38648) 2025-03-18,23:10:22 | INFO | Train Epoch: 3 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 151.266/s, 151.266/s/gpu LR: 0.000196 Logit Scale: 23.793 Contrastive_loss: 0.48972 (0.38934) Loss: 0.48972 (0.38934) 2025-03-18,23:10:44 | INFO | Train Epoch: 3 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.219, 146.040/s, 146.040/s/gpu LR: 0.000196 Logit Scale: 23.808 Contrastive_loss: 0.24319 (0.38539) Loss: 0.24319 (0.38539) 2025-03-18,23:11:06 | INFO | Train Epoch: 3 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.219, 147.027/s, 147.027/s/gpu LR: 0.000196 Logit Scale: 23.844 Contrastive_loss: 0.043369 (0.37639) Loss: 0.043369 (0.37639) 2025-03-18,23:11:28 | INFO | Train Epoch: 3 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.219, 146.551/s, 146.551/s/gpu LR: 0.000196 Logit Scale: 23.843 Contrastive_loss: 0.40030 (0.37701) Loss: 0.40030 (0.37701) 2025-03-18,23:11:50 | INFO | Train Epoch: 3 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.218, 150.000/s, 150.000/s/gpu LR: 0.000196 Logit Scale: 23.851 Contrastive_loss: 0.42379 (0.37818) Loss: 0.42379 (0.37818) 2025-03-18,23:12:11 | INFO | Train Epoch: 3 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 151.486/s, 151.486/s/gpu LR: 0.000196 Logit Scale: 23.822 Contrastive_loss: 0.36205 (0.37778) Loss: 0.36205 (0.37778) 2025-03-18,23:12:32 | INFO | Train Epoch: 3 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.212, 152.475/s, 152.475/s/gpu LR: 0.000196 Logit Scale: 23.829 Contrastive_loss: 0.51480 (0.38104) Loss: 0.51480 (0.38104) 2025-03-18,23:12:54 | INFO | Train Epoch: 3 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.212, 151.671/s, 151.671/s/gpu LR: 0.000196 Logit Scale: 23.854 Contrastive_loss: 0.21298 (0.37714) Loss: 0.21298 (0.37714) 2025-03-18,23:13:15 | INFO | Train Epoch: 3 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.211, 149.588/s, 149.588/s/gpu LR: 0.000196 Logit Scale: 23.854 Contrastive_loss: 0.20169 (0.37315) Loss: 0.20169 (0.37315) 2025-03-18,23:13:36 | INFO | Train Epoch: 3 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 150.145/s, 150.145/s/gpu LR: 0.000196 Logit Scale: 23.885 Contrastive_loss: 0.77086 (0.38199) Loss: 0.77086 (0.38199) 2025-03-18,23:13:57 | INFO | Train Epoch: 3 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 150.564/s, 150.564/s/gpu LR: 0.000196 Logit Scale: 23.908 Contrastive_loss: 0.80190 (0.39111) Loss: 0.80190 (0.39111) 2025-03-18,23:14:19 | INFO | Train Epoch: 3 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.213, 149.136/s, 149.136/s/gpu LR: 0.000196 Logit Scale: 23.902 Contrastive_loss: 0.37261 (0.39072) Loss: 0.37261 (0.39072) 2025-03-18,23:14:40 | INFO | Train Epoch: 3 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.725/s, 149.725/s/gpu LR: 0.000196 Logit Scale: 23.906 Contrastive_loss: 0.15803 (0.38587) Loss: 0.15803 (0.38587) 2025-03-18,23:15:01 | INFO | Train Epoch: 3 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.213, 144.579/s, 144.579/s/gpu LR: 0.000196 Logit Scale: 23.918 Contrastive_loss: 0.37405 (0.38563) Loss: 0.37405 (0.38563) 2025-03-18,23:15:23 | INFO | Train Epoch: 3 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.526/s, 149.526/s/gpu LR: 0.000196 Logit Scale: 23.884 Contrastive_loss: 0.21145 (0.38215) Loss: 0.21145 (0.38215) 2025-03-18,23:15:44 | INFO | Train Epoch: 3 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.723/s, 149.723/s/gpu LR: 0.000196 Logit Scale: 23.829 Contrastive_loss: 0.38599 (0.38222) Loss: 0.38599 (0.38222) 2025-03-18,23:16:06 | INFO | Train Epoch: 3 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 152.485/s, 152.485/s/gpu LR: 0.000196 Logit Scale: 23.830 Contrastive_loss: 0.17615 (0.37826) Loss: 0.17615 (0.37826) 2025-03-18,23:16:27 | INFO | Train Epoch: 3 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 149.450/s, 149.450/s/gpu LR: 0.000196 Logit Scale: 23.833 Contrastive_loss: 0.33971 (0.37753) Loss: 0.33971 (0.37753) 2025-03-18,23:16:49 | INFO | Train Epoch: 3 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 147.940/s, 147.940/s/gpu LR: 0.000196 Logit Scale: 23.839 Contrastive_loss: 0.46403 (0.37914) Loss: 0.46403 (0.37914) 2025-03-18,23:17:10 | INFO | Train Epoch: 3 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 151.639/s, 151.639/s/gpu LR: 0.000196 Logit Scale: 23.854 Contrastive_loss: 0.16616 (0.37526) Loss: 0.16616 (0.37526) 2025-03-18,23:17:32 | INFO | Train Epoch: 3 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 148.843/s, 148.843/s/gpu LR: 0.000196 Logit Scale: 23.885 Contrastive_loss: 0.25875 (0.37318) Loss: 0.25875 (0.37318) 2025-03-18,23:17:53 | INFO | Train Epoch: 3 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 149.264/s, 149.264/s/gpu LR: 0.000196 Logit Scale: 23.852 Contrastive_loss: 0.14944 (0.36926) Loss: 0.14944 (0.36926) 2025-03-18,23:18:15 | INFO | Train Epoch: 3 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 147.332/s, 147.332/s/gpu LR: 0.000196 Logit Scale: 23.874 Contrastive_loss: 0.34669 (0.36887) Loss: 0.34669 (0.36887) 2025-03-18,23:18:36 | INFO | Train Epoch: 3 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 150.208/s, 150.208/s/gpu LR: 0.000196 Logit Scale: 23.891 Contrastive_loss: 0.54055 (0.37178) Loss: 0.54055 (0.37178) 2025-03-18,23:18:58 | INFO | Train Epoch: 3 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 149.899/s, 149.899/s/gpu LR: 0.000196 Logit Scale: 23.875 Contrastive_loss: 0.21247 (0.36912) Loss: 0.21247 (0.36912) 2025-03-18,23:19:20 | INFO | Train Epoch: 3 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 150.824/s, 150.824/s/gpu LR: 0.000196 Logit Scale: 23.877 Contrastive_loss: 0.82146 (0.37654) Loss: 0.82146 (0.37654) 2025-03-18,23:19:41 | INFO | Train Epoch: 3 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 150.647/s, 150.647/s/gpu LR: 0.000195 Logit Scale: 23.853 Contrastive_loss: 0.72187 (0.38211) Loss: 0.72187 (0.38211) 2025-03-18,23:20:03 | INFO | Train Epoch: 3 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 146.564/s, 146.564/s/gpu LR: 0.000195 Logit Scale: 23.853 Contrastive_loss: 0.34574 (0.38153) Loss: 0.34574 (0.38153) 2025-03-18,23:20:25 | INFO | Train Epoch: 3 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.218, 148.975/s, 148.975/s/gpu LR: 0.000195 Logit Scale: 23.887 Contrastive_loss: 0.25786 (0.37960) Loss: 0.25786 (0.37960) 2025-03-18,23:20:46 | INFO | Train Epoch: 3 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 148.833/s, 148.833/s/gpu LR: 0.000195 Logit Scale: 23.841 Contrastive_loss: 0.47055 (0.38100) Loss: 0.47055 (0.38100) 2025-03-18,23:21:07 | INFO | Train Epoch: 3 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 148.034/s, 148.034/s/gpu LR: 0.000195 Logit Scale: 23.895 Contrastive_loss: 0.65966 (0.38522) Loss: 0.65966 (0.38522) 2025-03-18,23:21:29 | INFO | Train Epoch: 3 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.888/s, 149.888/s/gpu LR: 0.000195 Logit Scale: 23.870 Contrastive_loss: 0.21025 (0.38261) Loss: 0.21025 (0.38261) 2025-03-18,23:21:50 | INFO | Train Epoch: 3 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 151.558/s, 151.558/s/gpu LR: 0.000195 Logit Scale: 23.901 Contrastive_loss: 0.58685 (0.38561) Loss: 0.58685 (0.38561) 2025-03-18,23:22:11 | INFO | Train Epoch: 3 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 151.734/s, 151.734/s/gpu LR: 0.000195 Logit Scale: 23.806 Contrastive_loss: 0.33734 (0.38491) Loss: 0.33734 (0.38491) 2025-03-18,23:22:33 | INFO | Train Epoch: 3 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 150.767/s, 150.767/s/gpu LR: 0.000195 Logit Scale: 23.849 Contrastive_loss: 0.28910 (0.38354) Loss: 0.28910 (0.38354) 2025-03-18,23:22:54 | INFO | Train Epoch: 3 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.212, 151.177/s, 151.177/s/gpu LR: 0.000195 Logit Scale: 23.876 Contrastive_loss: 0.32795 (0.38276) Loss: 0.32795 (0.38276) 2025-03-18,23:23:15 | INFO | Train Epoch: 3 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.212, 151.271/s, 151.271/s/gpu LR: 0.000195 Logit Scale: 23.856 Contrastive_loss: 0.33919 (0.38216) Loss: 0.33919 (0.38216) 2025-03-18,23:23:37 | INFO | Train Epoch: 3 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.213, 151.898/s, 151.898/s/gpu LR: 0.000195 Logit Scale: 23.882 Contrastive_loss: 0.26014 (0.38048) Loss: 0.26014 (0.38048) 2025-03-18,23:23:58 | INFO | Train Epoch: 3 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 150.382/s, 150.382/s/gpu LR: 0.000195 Logit Scale: 23.864 Contrastive_loss: 0.35660 (0.38016) Loss: 0.35660 (0.38016) 2025-03-18,23:24:19 | INFO | Train Epoch: 3 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.213, 150.230/s, 150.230/s/gpu LR: 0.000195 Logit Scale: 23.909 Contrastive_loss: 0.23840 (0.37827) Loss: 0.23840 (0.37827) 2025-03-18,23:24:41 | INFO | Train Epoch: 3 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 150.136/s, 150.136/s/gpu LR: 0.000195 Logit Scale: 23.917 Contrastive_loss: 0.16698 (0.37549) Loss: 0.16698 (0.37549) 2025-03-18,23:25:02 | INFO | Train Epoch: 3 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.212, 149.938/s, 149.938/s/gpu LR: 0.000195 Logit Scale: 23.938 Contrastive_loss: 0.25354 (0.37391) Loss: 0.25354 (0.37391) 2025-03-18,23:25:23 | INFO | Train Epoch: 3 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 148.891/s, 148.891/s/gpu LR: 0.000195 Logit Scale: 23.886 Contrastive_loss: 0.35730 (0.37369) Loss: 0.35730 (0.37369) 2025-03-18,23:25:45 | INFO | Train Epoch: 3 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 150.331/s, 150.331/s/gpu LR: 0.000195 Logit Scale: 23.919 Contrastive_loss: 0.52953 (0.37567) Loss: 0.52953 (0.37567) 2025-03-18,23:26:06 | INFO | Train Epoch: 3 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 150.975/s, 150.975/s/gpu LR: 0.000195 Logit Scale: 23.937 Contrastive_loss: 0.16981 (0.37309) Loss: 0.16981 (0.37309) 2025-03-18,23:26:27 | INFO | Train Epoch: 3 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.212, 150.863/s, 150.863/s/gpu LR: 0.000195 Logit Scale: 23.973 Contrastive_loss: 0.16281 (0.37050) Loss: 0.16281 (0.37050) 2025-03-18,23:26:49 | INFO | Train Epoch: 3 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 148.295/s, 148.295/s/gpu LR: 0.000195 Logit Scale: 23.941 Contrastive_loss: 0.36951 (0.37049) Loss: 0.36951 (0.37049) 2025-03-18,23:27:10 | INFO | Train Epoch: 3 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 149.215/s, 149.215/s/gpu LR: 0.000195 Logit Scale: 23.954 Contrastive_loss: 0.19133 (0.36833) Loss: 0.19133 (0.36833) 2025-03-18,23:27:32 | INFO | Train Epoch: 3 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 149.650/s, 149.650/s/gpu LR: 0.000195 Logit Scale: 23.976 Contrastive_loss: 0.21400 (0.36649) Loss: 0.21400 (0.36649) 2025-03-18,23:27:54 | INFO | Train Epoch: 3 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 147.975/s, 147.975/s/gpu LR: 0.000195 Logit Scale: 24.007 Contrastive_loss: 0.61765 (0.36944) Loss: 0.61765 (0.36944) 2025-03-18,23:28:15 | INFO | Train Epoch: 3 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 150.044/s, 150.044/s/gpu LR: 0.000195 Logit Scale: 23.999 Contrastive_loss: 0.18762 (0.36733) Loss: 0.18762 (0.36733) 2025-03-18,23:28:37 | INFO | Train Epoch: 3 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.552/s, 149.552/s/gpu LR: 0.000195 Logit Scale: 24.008 Contrastive_loss: 0.25921 (0.36609) Loss: 0.25921 (0.36609) 2025-03-18,23:28:58 | INFO | Train Epoch: 3 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 148.031/s, 148.031/s/gpu LR: 0.000195 Logit Scale: 24.015 Contrastive_loss: 0.53686 (0.36803) Loss: 0.53686 (0.36803) 2025-03-18,23:29:20 | INFO | Train Epoch: 3 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.217, 148.176/s, 148.176/s/gpu LR: 0.000195 Logit Scale: 24.020 Contrastive_loss: 0.30188 (0.36729) Loss: 0.30188 (0.36729) 2025-03-18,23:29:41 | INFO | Train Epoch: 3 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 152.326/s, 152.326/s/gpu LR: 0.000195 Logit Scale: 23.997 Contrastive_loss: 0.61925 (0.37009) Loss: 0.61925 (0.37009) 2025-03-18,23:30:03 | INFO | Train Epoch: 3 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.213, 145.790/s, 145.790/s/gpu LR: 0.000195 Logit Scale: 23.965 Contrastive_loss: 0.41232 (0.37055) Loss: 0.41232 (0.37055) 2025-03-18,23:30:24 | INFO | Train Epoch: 3 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.217, 148.001/s, 148.001/s/gpu LR: 0.000195 Logit Scale: 23.969 Contrastive_loss: 0.32081 (0.37001) Loss: 0.32081 (0.37001) 2025-03-18,23:30:46 | INFO | Train Epoch: 3 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.451/s, 149.451/s/gpu LR: 0.000195 Logit Scale: 23.993 Contrastive_loss: 0.73728 (0.37396) Loss: 0.73728 (0.37396) 2025-03-18,23:31:07 | INFO | Train Epoch: 3 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.693/s, 148.693/s/gpu LR: 0.000195 Logit Scale: 23.953 Contrastive_loss: 0.21719 (0.37229) Loss: 0.21719 (0.37229) 2025-03-18,23:31:29 | INFO | Train Epoch: 3 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 147.762/s, 147.762/s/gpu LR: 0.000195 Logit Scale: 23.979 Contrastive_loss: 0.28187 (0.37134) Loss: 0.28187 (0.37134) 2025-03-18,23:31:50 | INFO | Train Epoch: 3 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.299/s, 149.299/s/gpu LR: 0.000195 Logit Scale: 24.021 Contrastive_loss: 0.40861 (0.37173) Loss: 0.40861 (0.37173) 2025-03-18,23:32:12 | INFO | Train Epoch: 3 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 150.159/s, 150.159/s/gpu LR: 0.000195 Logit Scale: 24.059 Contrastive_loss: 0.32444 (0.37124) Loss: 0.32444 (0.37124) 2025-03-18,23:32:33 | INFO | Train Epoch: 3 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.212, 152.143/s, 152.143/s/gpu LR: 0.000195 Logit Scale: 24.050 Contrastive_loss: 0.40638 (0.37160) Loss: 0.40638 (0.37160) 2025-03-18,23:32:54 | INFO | Train Epoch: 3 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 148.661/s, 148.661/s/gpu LR: 0.000195 Logit Scale: 24.059 Contrastive_loss: 0.13855 (0.36924) Loss: 0.13855 (0.36924) 2025-03-18,23:33:16 | INFO | Train Epoch: 3 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 149.287/s, 149.287/s/gpu LR: 0.000195 Logit Scale: 24.026 Contrastive_loss: 0.34476 (0.36900) Loss: 0.34476 (0.36900) 2025-03-18,23:33:37 | INFO | Train Epoch: 3 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.508/s, 149.508/s/gpu LR: 0.000195 Logit Scale: 24.007 Contrastive_loss: 0.46400 (0.36994) Loss: 0.46400 (0.36994) 2025-03-18,23:33:59 | INFO | Train Epoch: 3 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.212, 152.199/s, 152.199/s/gpu LR: 0.000195 Logit Scale: 24.003 Contrastive_loss: 0.54434 (0.37165) Loss: 0.54434 (0.37165) 2025-03-18,23:34:20 | INFO | Train Epoch: 3 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.212, 152.160/s, 152.160/s/gpu LR: 0.000195 Logit Scale: 23.982 Contrastive_loss: 0.25674 (0.37053) Loss: 0.25674 (0.37053) 2025-03-18,23:34:41 | INFO | Train Epoch: 3 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.212, 152.346/s, 152.346/s/gpu LR: 0.000195 Logit Scale: 23.943 Contrastive_loss: 0.27611 (0.36963) Loss: 0.27611 (0.36963) 2025-03-18,23:35:03 | INFO | Train Epoch: 3 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 147.631/s, 147.631/s/gpu LR: 0.000195 Logit Scale: 23.976 Contrastive_loss: 0.30165 (0.36898) Loss: 0.30165 (0.36898) 2025-03-18,23:35:25 | INFO | Train Epoch: 3 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.221, 142.853/s, 142.853/s/gpu LR: 0.000195 Logit Scale: 23.954 Contrastive_loss: 0.58726 (0.37104) Loss: 0.58726 (0.37104) 2025-03-18,23:35:47 | INFO | Train Epoch: 3 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.217, 150.342/s, 150.342/s/gpu LR: 0.000195 Logit Scale: 23.955 Contrastive_loss: 0.25941 (0.36999) Loss: 0.25941 (0.36999) 2025-03-18,23:36:08 | INFO | Train Epoch: 3 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.218, 146.960/s, 146.960/s/gpu LR: 0.000195 Logit Scale: 24.025 Contrastive_loss: 0.28307 (0.36919) Loss: 0.28307 (0.36919) 2025-03-18,23:36:31 | INFO | Train Epoch: 3 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.221, 145.355/s, 145.355/s/gpu LR: 0.000195 Logit Scale: 23.991 Contrastive_loss: 0.68087 (0.37205) Loss: 0.68087 (0.37205) 2025-03-18,23:36:52 | INFO | Train Epoch: 3 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 148.893/s, 148.893/s/gpu LR: 0.000195 Logit Scale: 24.026 Contrastive_loss: 0.47088 (0.37295) Loss: 0.47088 (0.37295) 2025-03-18,23:37:14 | INFO | Train Epoch: 3 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.194/s, 149.194/s/gpu LR: 0.000195 Logit Scale: 24.019 Contrastive_loss: 0.48063 (0.37392) Loss: 0.48063 (0.37392) 2025-03-18,23:37:36 | INFO | Train Epoch: 3 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.218, 145.499/s, 145.499/s/gpu LR: 0.000195 Logit Scale: 24.023 Contrastive_loss: 0.41046 (0.37424) Loss: 0.41046 (0.37424) 2025-03-18,23:37:57 | INFO | Train Epoch: 3 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 148.893/s, 148.893/s/gpu LR: 0.000195 Logit Scale: 24.008 Contrastive_loss: 0.67054 (0.37687) Loss: 0.67054 (0.37687) 2025-03-18,23:38:18 | INFO | Train Epoch: 3 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 150.040/s, 150.040/s/gpu LR: 0.000195 Logit Scale: 23.971 Contrastive_loss: 0.24982 (0.37575) Loss: 0.24982 (0.37575) 2025-03-18,23:38:40 | INFO | Train Epoch: 3 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 148.926/s, 148.926/s/gpu LR: 0.000195 Logit Scale: 23.926 Contrastive_loss: 0.20228 (0.37424) Loss: 0.20228 (0.37424) 2025-03-18,23:39:01 | INFO | Train Epoch: 3 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.218, 148.606/s, 148.606/s/gpu LR: 0.000195 Logit Scale: 23.949 Contrastive_loss: 0.58034 (0.37602) Loss: 0.58034 (0.37602) 2025-03-18,23:39:23 | INFO | Train Epoch: 3 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.218, 147.968/s, 147.968/s/gpu LR: 0.000195 Logit Scale: 24.033 Contrastive_loss: 0.55546 (0.37755) Loss: 0.55546 (0.37755) 2025-03-18,23:39:45 | INFO | Train Epoch: 3 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 148.622/s, 148.622/s/gpu LR: 0.000195 Logit Scale: 24.024 Contrastive_loss: 0.55874 (0.37909) Loss: 0.55874 (0.37909) 2025-03-18,23:40:07 | INFO | Train Epoch: 3 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.219, 145.374/s, 145.374/s/gpu LR: 0.000195 Logit Scale: 24.006 Contrastive_loss: 0.58154 (0.38079) Loss: 0.58154 (0.38079) 2025-03-18,23:40:29 | INFO | Train Epoch: 3 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.219, 147.686/s, 147.686/s/gpu LR: 0.000195 Logit Scale: 23.973 Contrastive_loss: 0.64109 (0.38296) Loss: 0.64109 (0.38296) 2025-03-18,23:40:51 | INFO | Train Epoch: 3 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.217, 148.604/s, 148.604/s/gpu LR: 0.000195 Logit Scale: 24.026 Contrastive_loss: 0.50023 (0.38393) Loss: 0.50023 (0.38393) 2025-03-18,23:41:12 | INFO | Train Epoch: 3 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 151.386/s, 151.386/s/gpu LR: 0.000195 Logit Scale: 24.029 Contrastive_loss: 0.23336 (0.38269) Loss: 0.23336 (0.38269) 2025-03-18,23:41:33 | INFO | Train Epoch: 3 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.213, 151.751/s, 151.751/s/gpu LR: 0.000195 Logit Scale: 24.025 Contrastive_loss: 0.29180 (0.38196) Loss: 0.29180 (0.38196) 2025-03-18,23:41:55 | INFO | Train Epoch: 3 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.212, 148.488/s, 148.488/s/gpu LR: 0.000195 Logit Scale: 24.008 Contrastive_loss: 0.46419 (0.38262) Loss: 0.46419 (0.38262) 2025-03-18,23:42:16 | INFO | Train Epoch: 3 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.212, 148.306/s, 148.306/s/gpu LR: 0.000195 Logit Scale: 24.028 Contrastive_loss: 0.35108 (0.38237) Loss: 0.35108 (0.38237) 2025-03-18,23:42:38 | INFO | Train Epoch: 3 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.220, 146.293/s, 146.293/s/gpu LR: 0.000195 Logit Scale: 24.031 Contrastive_loss: 0.81873 (0.38583) Loss: 0.81873 (0.38583) 2025-03-18,23:43:00 | INFO | Train Epoch: 3 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.221, 141.043/s, 141.043/s/gpu LR: 0.000195 Logit Scale: 24.017 Contrastive_loss: 0.42830 (0.38616) Loss: 0.42830 (0.38616) 2025-03-18,23:43:22 | INFO | Train Epoch: 3 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 144.650/s, 144.650/s/gpu LR: 0.000195 Logit Scale: 24.091 Contrastive_loss: 0.61841 (0.38798) Loss: 0.61841 (0.38798) 2025-03-18,23:43:43 | INFO | Train Epoch: 3 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 147.056/s, 147.056/s/gpu LR: 0.000195 Logit Scale: 24.062 Contrastive_loss: 0.39516 (0.38803) Loss: 0.39516 (0.38803) 2025-03-18,23:44:05 | INFO | Train Epoch: 3 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.219, 146.066/s, 146.066/s/gpu LR: 0.000195 Logit Scale: 24.065 Contrastive_loss: 0.47190 (0.38868) Loss: 0.47190 (0.38868) 2025-03-18,23:44:27 | INFO | Train Epoch: 3 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.217, 151.226/s, 151.226/s/gpu LR: 0.000195 Logit Scale: 24.024 Contrastive_loss: 0.50163 (0.38954) Loss: 0.50163 (0.38954) 2025-03-18,23:44:49 | INFO | Train Epoch: 3 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 144.922/s, 144.922/s/gpu LR: 0.000195 Logit Scale: 24.076 Contrastive_loss: 0.48193 (0.39024) Loss: 0.48193 (0.39024) 2025-03-18,23:45:11 | INFO | Train Epoch: 3 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 149.140/s, 149.140/s/gpu LR: 0.000195 Logit Scale: 24.035 Contrastive_loss: 0.17841 (0.38865) Loss: 0.17841 (0.38865) 2025-03-18,23:45:32 | INFO | Train Epoch: 3 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 147.745/s, 147.745/s/gpu LR: 0.000194 Logit Scale: 24.008 Contrastive_loss: 0.61956 (0.39037) Loss: 0.61956 (0.39037) 2025-03-18,23:45:55 | INFO | Train Epoch: 3 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.221, 144.966/s, 144.966/s/gpu LR: 0.000194 Logit Scale: 24.060 Contrastive_loss: 0.70217 (0.39268) Loss: 0.70217 (0.39268) 2025-03-18,23:46:17 | INFO | Train Epoch: 3 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.220, 146.798/s, 146.798/s/gpu LR: 0.000194 Logit Scale: 24.073 Contrastive_loss: 0.36134 (0.39245) Loss: 0.36134 (0.39245) 2025-03-18,23:46:38 | INFO | Train Epoch: 3 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.218, 146.794/s, 146.794/s/gpu LR: 0.000194 Logit Scale: 24.049 Contrastive_loss: 0.49156 (0.39317) Loss: 0.49156 (0.39317) 2025-03-18,23:47:00 | INFO | Train Epoch: 3 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.217, 147.218/s, 147.218/s/gpu LR: 0.000194 Logit Scale: 24.062 Contrastive_loss: 0.37536 (0.39305) Loss: 0.37536 (0.39305) 2025-03-18,23:47:22 | INFO | Train Epoch: 3 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.218, 148.995/s, 148.995/s/gpu LR: 0.000194 Logit Scale: 24.092 Contrastive_loss: 0.47761 (0.39365) Loss: 0.47761 (0.39365) 2025-03-18,23:47:44 | INFO | Train Epoch: 3 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 147.686/s, 147.686/s/gpu LR: 0.000194 Logit Scale: 24.120 Contrastive_loss: 0.064565 (0.39130) Loss: 0.064565 (0.39130) 2025-03-18,23:48:05 | INFO | Train Epoch: 3 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 149.707/s, 149.707/s/gpu LR: 0.000194 Logit Scale: 24.065 Contrastive_loss: 0.29597 (0.39063) Loss: 0.29597 (0.39063) 2025-03-18,23:48:27 | INFO | Train Epoch: 3 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 149.805/s, 149.805/s/gpu LR: 0.000194 Logit Scale: 24.107 Contrastive_loss: 0.27298 (0.38980) Loss: 0.27298 (0.38980) 2025-03-18,23:48:48 | INFO | Train Epoch: 3 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 148.241/s, 148.241/s/gpu LR: 0.000194 Logit Scale: 24.106 Contrastive_loss: 0.45958 (0.39029) Loss: 0.45958 (0.39029) 2025-03-18,23:49:10 | INFO | Train Epoch: 3 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 148.484/s, 148.484/s/gpu LR: 0.000194 Logit Scale: 24.111 Contrastive_loss: 0.54435 (0.39136) Loss: 0.54435 (0.39136) 2025-03-18,23:49:32 | INFO | Train Epoch: 3 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 148.819/s, 148.819/s/gpu LR: 0.000194 Logit Scale: 24.075 Contrastive_loss: 0.15807 (0.38975) Loss: 0.15807 (0.38975) 2025-03-18,23:49:53 | INFO | Train Epoch: 3 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.135/s, 149.135/s/gpu LR: 0.000194 Logit Scale: 24.091 Contrastive_loss: 0.53995 (0.39078) Loss: 0.53995 (0.39078) 2025-03-18,23:50:15 | INFO | Train Epoch: 3 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.056/s, 149.056/s/gpu LR: 0.000194 Logit Scale: 24.105 Contrastive_loss: 0.43823 (0.39110) Loss: 0.43823 (0.39110) 2025-03-18,23:50:36 | INFO | Train Epoch: 3 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.249/s, 148.249/s/gpu LR: 0.000194 Logit Scale: 24.076 Contrastive_loss: 0.41054 (0.39123) Loss: 0.41054 (0.39123) 2025-03-18,23:50:58 | INFO | Train Epoch: 3 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.217, 140.688/s, 140.688/s/gpu LR: 0.000194 Logit Scale: 24.092 Contrastive_loss: 0.16120 (0.38969) Loss: 0.16120 (0.38969) 2025-03-18,23:51:19 | INFO | Train Epoch: 3 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 147.279/s, 147.279/s/gpu LR: 0.000194 Logit Scale: 24.104 Contrastive_loss: 0.46302 (0.39018) Loss: 0.46302 (0.39018) 2025-03-18,23:51:41 | INFO | Train Epoch: 3 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 148.691/s, 148.691/s/gpu LR: 0.000194 Logit Scale: 24.115 Contrastive_loss: 0.27346 (0.38940) Loss: 0.27346 (0.38940) 2025-03-18,23:52:03 | INFO | Train Epoch: 3 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 147.579/s, 147.579/s/gpu LR: 0.000194 Logit Scale: 24.109 Contrastive_loss: 0.15167 (0.38784) Loss: 0.15167 (0.38784) 2025-03-18,23:52:24 | INFO | Train Epoch: 3 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 149.410/s, 149.410/s/gpu LR: 0.000194 Logit Scale: 24.128 Contrastive_loss: 0.70706 (0.38992) Loss: 0.70706 (0.38992) 2025-03-18,23:52:46 | INFO | Train Epoch: 3 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.114/s, 149.114/s/gpu LR: 0.000194 Logit Scale: 24.138 Contrastive_loss: 0.57599 (0.39113) Loss: 0.57599 (0.39113) 2025-03-18,23:53:07 | INFO | Train Epoch: 3 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 147.393/s, 147.393/s/gpu LR: 0.000194 Logit Scale: 24.116 Contrastive_loss: 0.29927 (0.39054) Loss: 0.29927 (0.39054) 2025-03-18,23:53:29 | INFO | Train Epoch: 3 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 144.220/s, 144.220/s/gpu LR: 0.000194 Logit Scale: 24.092 Contrastive_loss: 0.43786 (0.39084) Loss: 0.43786 (0.39084) 2025-03-18,23:53:51 | INFO | Train Epoch: 3 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 149.016/s, 149.016/s/gpu LR: 0.000194 Logit Scale: 24.088 Contrastive_loss: 0.33119 (0.39046) Loss: 0.33119 (0.39046) 2025-03-18,23:54:12 | INFO | Train Epoch: 3 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 148.492/s, 148.492/s/gpu LR: 0.000194 Logit Scale: 24.082 Contrastive_loss: 0.080824 (0.38850) Loss: 0.080824 (0.38850) 2025-03-18,23:54:33 | INFO | Train Epoch: 3 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 147.275/s, 147.275/s/gpu LR: 0.000194 Logit Scale: 24.103 Contrastive_loss: 0.048166 (0.38636) Loss: 0.048166 (0.38636) 2025-03-18,23:54:55 | INFO | Train Epoch: 3 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 146.662/s, 146.662/s/gpu LR: 0.000194 Logit Scale: 24.056 Contrastive_loss: 0.39856 (0.38644) Loss: 0.39856 (0.38644) 2025-03-18,23:55:17 | INFO | Train Epoch: 3 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 149.540/s, 149.540/s/gpu LR: 0.000194 Logit Scale: 24.059 Contrastive_loss: 0.26893 (0.38571) Loss: 0.26893 (0.38571) 2025-03-18,23:55:38 | INFO | Train Epoch: 3 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 150.175/s, 150.175/s/gpu LR: 0.000194 Logit Scale: 24.058 Contrastive_loss: 0.16692 (0.38436) Loss: 0.16692 (0.38436) 2025-03-18,23:55:59 | INFO | Train Epoch: 3 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 148.952/s, 148.952/s/gpu LR: 0.000194 Logit Scale: 24.049 Contrastive_loss: 0.75860 (0.38666) Loss: 0.75860 (0.38666) 2025-03-18,23:56:21 | INFO | Train Epoch: 3 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 149.920/s, 149.920/s/gpu LR: 0.000194 Logit Scale: 24.076 Contrastive_loss: 0.39295 (0.38669) Loss: 0.39295 (0.38669) 2025-03-18,23:56:43 | INFO | Train Epoch: 3 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 146.192/s, 146.192/s/gpu LR: 0.000194 Logit Scale: 24.092 Contrastive_loss: 0.28241 (0.38606) Loss: 0.28241 (0.38606) 2025-03-18,23:57:04 | INFO | Train Epoch: 3 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 146.320/s, 146.320/s/gpu LR: 0.000194 Logit Scale: 24.075 Contrastive_loss: 0.31627 (0.38564) Loss: 0.31627 (0.38564) 2025-03-18,23:57:26 | INFO | Train Epoch: 3 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 137.764/s, 137.764/s/gpu LR: 0.000194 Logit Scale: 24.053 Contrastive_loss: 0.30836 (0.38518) Loss: 0.30836 (0.38518) 2025-03-18,23:57:48 | INFO | Train Epoch: 3 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 145.802/s, 145.802/s/gpu LR: 0.000194 Logit Scale: 24.088 Contrastive_loss: 0.44577 (0.38554) Loss: 0.44577 (0.38554) 2025-03-18,23:58:09 | INFO | Train Epoch: 3 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 147.658/s, 147.658/s/gpu LR: 0.000194 Logit Scale: 24.112 Contrastive_loss: 0.54441 (0.38648) Loss: 0.54441 (0.38648) 2025-03-18,23:58:31 | INFO | Train Epoch: 3 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 147.954/s, 147.954/s/gpu LR: 0.000194 Logit Scale: 24.083 Contrastive_loss: 0.27384 (0.38582) Loss: 0.27384 (0.38582) 2025-03-18,23:58:53 | INFO | Train Epoch: 3 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 149.391/s, 149.391/s/gpu LR: 0.000194 Logit Scale: 24.084 Contrastive_loss: 0.46879 (0.38630) Loss: 0.46879 (0.38630) 2025-03-18,23:59:14 | INFO | Train Epoch: 3 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 148.019/s, 148.019/s/gpu LR: 0.000194 Logit Scale: 24.083 Contrastive_loss: 0.59473 (0.38751) Loss: 0.59473 (0.38751) 2025-03-18,23:59:36 | INFO | Train Epoch: 3 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 152.509/s, 152.509/s/gpu LR: 0.000194 Logit Scale: 24.083 Contrastive_loss: 0.21502 (0.38652) Loss: 0.21502 (0.38652) 2025-03-18,23:59:57 | INFO | Train Epoch: 3 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.211, 151.795/s, 151.795/s/gpu LR: 0.000194 Logit Scale: 24.112 Contrastive_loss: 0.67119 (0.38815) Loss: 0.67119 (0.38815) 2025-03-19,00:00:18 | INFO | Train Epoch: 3 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 148.690/s, 148.690/s/gpu LR: 0.000194 Logit Scale: 24.083 Contrastive_loss: 0.27618 (0.38751) Loss: 0.27618 (0.38751) 2025-03-19,00:00:40 | INFO | Train Epoch: 3 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 150.024/s, 150.024/s/gpu LR: 0.000194 Logit Scale: 24.129 Contrastive_loss: 0.21408 (0.38653) Loss: 0.21408 (0.38653) 2025-03-19,00:01:01 | INFO | Train Epoch: 3 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 150.301/s, 150.301/s/gpu LR: 0.000194 Logit Scale: 24.072 Contrastive_loss: 0.15247 (0.38520) Loss: 0.15247 (0.38520) 2025-03-19,00:01:23 | INFO | Train Epoch: 3 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 150.127/s, 150.127/s/gpu LR: 0.000194 Logit Scale: 24.087 Contrastive_loss: 0.18051 (0.38405) Loss: 0.18051 (0.38405) 2025-03-19,00:01:44 | INFO | Train Epoch: 3 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 150.373/s, 150.373/s/gpu LR: 0.000194 Logit Scale: 24.092 Contrastive_loss: 0.52955 (0.38487) Loss: 0.52955 (0.38487) 2025-03-19,00:02:06 | INFO | Train Epoch: 3 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.213, 150.999/s, 150.999/s/gpu LR: 0.000194 Logit Scale: 24.075 Contrastive_loss: 0.38515 (0.38487) Loss: 0.38515 (0.38487) 2025-03-19,00:02:27 | INFO | Train Epoch: 3 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 151.383/s, 151.383/s/gpu LR: 0.000194 Logit Scale: 24.102 Contrastive_loss: 0.22559 (0.38399) Loss: 0.22559 (0.38399) 2025-03-19,00:02:48 | INFO | Train Epoch: 3 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.213, 149.901/s, 149.901/s/gpu LR: 0.000194 Logit Scale: 24.081 Contrastive_loss: 0.31310 (0.38360) Loss: 0.31310 (0.38360) 2025-03-19,00:03:10 | INFO | Train Epoch: 3 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 148.331/s, 148.331/s/gpu LR: 0.000194 Logit Scale: 24.118 Contrastive_loss: 0.24650 (0.38285) Loss: 0.24650 (0.38285) 2025-03-19,00:03:31 | INFO | Train Epoch: 3 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.217, 149.413/s, 149.413/s/gpu LR: 0.000194 Logit Scale: 24.073 Contrastive_loss: 0.30766 (0.38244) Loss: 0.30766 (0.38244) 2025-03-19,00:03:53 | INFO | Train Epoch: 3 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 148.735/s, 148.735/s/gpu LR: 0.000194 Logit Scale: 24.111 Contrastive_loss: 0.53915 (0.38329) Loss: 0.53915 (0.38329) 2025-03-19,00:04:14 | INFO | Train Epoch: 3 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.327/s, 149.327/s/gpu LR: 0.000194 Logit Scale: 24.093 Contrastive_loss: 0.38211 (0.38328) Loss: 0.38211 (0.38328) 2025-03-19,00:04:36 | INFO | Train Epoch: 3 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 148.122/s, 148.122/s/gpu LR: 0.000194 Logit Scale: 24.058 Contrastive_loss: 0.19302 (0.38227) Loss: 0.19302 (0.38227) 2025-03-19,00:04:57 | INFO | Train Epoch: 3 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.213, 152.140/s, 152.140/s/gpu LR: 0.000194 Logit Scale: 24.102 Contrastive_loss: 0.16343 (0.38110) Loss: 0.16343 (0.38110) 2025-03-19,00:05:18 | INFO | Train Epoch: 3 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.212, 151.027/s, 151.027/s/gpu LR: 0.000194 Logit Scale: 24.092 Contrastive_loss: 0.65403 (0.38255) Loss: 0.65403 (0.38255) 2025-03-19,00:05:40 | INFO | Train Epoch: 3 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.606/s, 149.606/s/gpu LR: 0.000194 Logit Scale: 24.111 Contrastive_loss: 0.61190 (0.38375) Loss: 0.61190 (0.38375) 2025-03-19,00:06:01 | INFO | Train Epoch: 3 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 148.608/s, 148.608/s/gpu LR: 0.000194 Logit Scale: 24.094 Contrastive_loss: 0.46642 (0.38419) Loss: 0.46642 (0.38419) 2025-03-19,00:06:23 | INFO | Train Epoch: 3 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 149.356/s, 149.356/s/gpu LR: 0.000194 Logit Scale: 24.079 Contrastive_loss: 0.18661 (0.38316) Loss: 0.18661 (0.38316) 2025-03-19,00:06:44 | INFO | Train Epoch: 3 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.213, 147.970/s, 147.970/s/gpu LR: 0.000194 Logit Scale: 24.106 Contrastive_loss: 0.18015 (0.38210) Loss: 0.18015 (0.38210) 2025-03-19,00:07:06 | INFO | Train Epoch: 3 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 147.754/s, 147.754/s/gpu LR: 0.000194 Logit Scale: 24.134 Contrastive_loss: 0.49586 (0.38269) Loss: 0.49586 (0.38269) 2025-03-19,00:07:27 | INFO | Train Epoch: 3 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 150.079/s, 150.079/s/gpu LR: 0.000194 Logit Scale: 24.116 Contrastive_loss: 0.21571 (0.38183) Loss: 0.21571 (0.38183) 2025-03-19,00:07:49 | INFO | Train Epoch: 3 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 149.349/s, 149.349/s/gpu LR: 0.000194 Logit Scale: 24.114 Contrastive_loss: 0.21748 (0.38100) Loss: 0.21748 (0.38100) 2025-03-19,00:08:11 | INFO | Train Epoch: 3 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.479/s, 149.479/s/gpu LR: 0.000194 Logit Scale: 24.109 Contrastive_loss: 0.43850 (0.38129) Loss: 0.43850 (0.38129) 2025-03-19,00:08:32 | INFO | Train Epoch: 3 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.087/s, 149.087/s/gpu LR: 0.000194 Logit Scale: 24.106 Contrastive_loss: 0.55409 (0.38216) Loss: 0.55409 (0.38216) 2025-03-19,00:08:54 | INFO | Train Epoch: 3 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.565/s, 149.565/s/gpu LR: 0.000194 Logit Scale: 24.119 Contrastive_loss: 0.39133 (0.38221) Loss: 0.39133 (0.38221) 2025-03-19,00:09:15 | INFO | Train Epoch: 3 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 151.252/s, 151.252/s/gpu LR: 0.000194 Logit Scale: 24.117 Contrastive_loss: 0.58799 (0.38324) Loss: 0.58799 (0.38324) 2025-03-19,00:09:36 | INFO | Train Epoch: 3 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.213, 151.867/s, 151.867/s/gpu LR: 0.000193 Logit Scale: 24.071 Contrastive_loss: 0.51704 (0.38390) Loss: 0.51704 (0.38390) 2025-03-19,00:09:58 | INFO | Train Epoch: 3 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 143.247/s, 143.247/s/gpu LR: 0.000193 Logit Scale: 24.060 Contrastive_loss: 0.53391 (0.38464) Loss: 0.53391 (0.38464) 2025-03-19,00:10:20 | INFO | Train Epoch: 3 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.219, 147.652/s, 147.652/s/gpu LR: 0.000193 Logit Scale: 24.069 Contrastive_loss: 0.41818 (0.38481) Loss: 0.41818 (0.38481) 2025-03-19,00:10:42 | INFO | Train Epoch: 3 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.219, 146.073/s, 146.073/s/gpu LR: 0.000193 Logit Scale: 24.072 Contrastive_loss: 0.63566 (0.38604) Loss: 0.63566 (0.38604) 2025-03-19,00:11:03 | INFO | Train Epoch: 3 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 151.262/s, 151.262/s/gpu LR: 0.000193 Logit Scale: 24.058 Contrastive_loss: 0.65017 (0.38733) Loss: 0.65017 (0.38733) 2025-03-19,00:11:25 | INFO | Train Epoch: 3 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.213, 148.881/s, 148.881/s/gpu LR: 0.000193 Logit Scale: 24.096 Contrastive_loss: 0.33638 (0.38708) Loss: 0.33638 (0.38708) 2025-03-19,00:11:46 | INFO | Train Epoch: 3 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 145.527/s, 145.527/s/gpu LR: 0.000193 Logit Scale: 24.094 Contrastive_loss: 0.29761 (0.38665) Loss: 0.29761 (0.38665) 2025-03-19,00:12:08 | INFO | Train Epoch: 3 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.219, 146.730/s, 146.730/s/gpu LR: 0.000193 Logit Scale: 24.077 Contrastive_loss: 0.34459 (0.38645) Loss: 0.34459 (0.38645) 2025-03-19,00:12:30 | INFO | Train Epoch: 3 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 148.334/s, 148.334/s/gpu LR: 0.000193 Logit Scale: 24.109 Contrastive_loss: 0.32876 (0.38617) Loss: 0.32876 (0.38617) 2025-03-19,00:12:52 | INFO | Train Epoch: 3 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 149.862/s, 149.862/s/gpu LR: 0.000193 Logit Scale: 24.122 Contrastive_loss: 0.35035 (0.38600) Loss: 0.35035 (0.38600) 2025-03-19,00:13:13 | INFO | Train Epoch: 3 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 150.097/s, 150.097/s/gpu LR: 0.000193 Logit Scale: 24.112 Contrastive_loss: 0.47922 (0.38644) Loss: 0.47922 (0.38644) 2025-03-19,00:13:34 | INFO | Train Epoch: 3 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 150.499/s, 150.499/s/gpu LR: 0.000193 Logit Scale: 24.118 Contrastive_loss: 0.51601 (0.38705) Loss: 0.51601 (0.38705) 2025-03-19,00:13:56 | INFO | Train Epoch: 3 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 150.139/s, 150.139/s/gpu LR: 0.000193 Logit Scale: 24.131 Contrastive_loss: 0.20842 (0.38621) Loss: 0.20842 (0.38621) 2025-03-19,00:14:17 | INFO | Train Epoch: 3 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.018/s, 148.018/s/gpu LR: 0.000193 Logit Scale: 24.137 Contrastive_loss: 0.20617 (0.38537) Loss: 0.20617 (0.38537) 2025-03-19,00:14:39 | INFO | Train Epoch: 3 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 149.686/s, 149.686/s/gpu LR: 0.000193 Logit Scale: 24.136 Contrastive_loss: 0.19462 (0.38448) Loss: 0.19462 (0.38448) 2025-03-19,00:15:00 | INFO | Train Epoch: 3 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 146.700/s, 146.700/s/gpu LR: 0.000193 Logit Scale: 24.143 Contrastive_loss: 0.30338 (0.38411) Loss: 0.30338 (0.38411) 2025-03-19,00:15:22 | INFO | Train Epoch: 3 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.213, 150.293/s, 150.293/s/gpu LR: 0.000193 Logit Scale: 24.151 Contrastive_loss: 0.39188 (0.38414) Loss: 0.39188 (0.38414) 2025-03-19,00:15:43 | INFO | Train Epoch: 3 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 148.307/s, 148.307/s/gpu LR: 0.000193 Logit Scale: 24.107 Contrastive_loss: 0.10199 (0.38285) Loss: 0.10199 (0.38285) 2025-03-19,00:16:05 | INFO | Train Epoch: 3 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.248/s, 149.248/s/gpu LR: 0.000193 Logit Scale: 24.123 Contrastive_loss: 0.51120 (0.38344) Loss: 0.51120 (0.38344) 2025-03-19,00:16:26 | INFO | Train Epoch: 3 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 148.277/s, 148.277/s/gpu LR: 0.000193 Logit Scale: 24.143 Contrastive_loss: 0.10100 (0.38215) Loss: 0.10100 (0.38215) 2025-03-19,00:16:48 | INFO | Train Epoch: 3 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 150.069/s, 150.069/s/gpu LR: 0.000193 Logit Scale: 24.180 Contrastive_loss: 0.19693 (0.38131) Loss: 0.19693 (0.38131) 2025-03-19,00:17:09 | INFO | Train Epoch: 3 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 149.780/s, 149.780/s/gpu LR: 0.000193 Logit Scale: 24.156 Contrastive_loss: 0.42909 (0.38153) Loss: 0.42909 (0.38153) 2025-03-19,00:17:31 | INFO | Train Epoch: 3 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.514/s, 149.514/s/gpu LR: 0.000193 Logit Scale: 24.161 Contrastive_loss: 0.58586 (0.38245) Loss: 0.58586 (0.38245) 2025-03-19,00:17:52 | INFO | Train Epoch: 3 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 148.896/s, 148.896/s/gpu LR: 0.000193 Logit Scale: 24.143 Contrastive_loss: 0.14876 (0.38140) Loss: 0.14876 (0.38140) 2025-03-19,00:18:14 | INFO | Train Epoch: 3 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 147.781/s, 147.781/s/gpu LR: 0.000193 Logit Scale: 24.130 Contrastive_loss: 0.14258 (0.38034) Loss: 0.14258 (0.38034) 2025-03-19,00:18:35 | INFO | Train Epoch: 3 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 150.335/s, 150.335/s/gpu LR: 0.000193 Logit Scale: 24.131 Contrastive_loss: 0.26137 (0.37982) Loss: 0.26137 (0.37982) 2025-03-19,00:18:57 | INFO | Train Epoch: 3 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 152.680/s, 152.680/s/gpu LR: 0.000193 Logit Scale: 24.157 Contrastive_loss: 0.22777 (0.37915) Loss: 0.22777 (0.37915) 2025-03-19,00:19:18 | INFO | Train Epoch: 3 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.213, 150.607/s, 150.607/s/gpu LR: 0.000193 Logit Scale: 24.154 Contrastive_loss: 0.57461 (0.38000) Loss: 0.57461 (0.38000) 2025-03-19,00:19:39 | INFO | Train Epoch: 3 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 151.614/s, 151.614/s/gpu LR: 0.000193 Logit Scale: 24.146 Contrastive_loss: 0.075949 (0.37867) Loss: 0.075949 (0.37867) 2025-03-19,00:20:01 | INFO | Train Epoch: 3 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 149.989/s, 149.989/s/gpu LR: 0.000193 Logit Scale: 24.156 Contrastive_loss: 0.26120 (0.37816) Loss: 0.26120 (0.37816) 2025-03-19,00:20:22 | INFO | Train Epoch: 3 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 151.381/s, 151.381/s/gpu LR: 0.000193 Logit Scale: 24.198 Contrastive_loss: 0.30817 (0.37786) Loss: 0.30817 (0.37786) 2025-03-19,00:20:44 | INFO | Train Epoch: 3 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 151.379/s, 151.379/s/gpu LR: 0.000193 Logit Scale: 24.210 Contrastive_loss: 0.24743 (0.37730) Loss: 0.24743 (0.37730) 2025-03-19,00:21:05 | INFO | Train Epoch: 3 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.677/s, 149.677/s/gpu LR: 0.000193 Logit Scale: 24.167 Contrastive_loss: 0.48439 (0.37776) Loss: 0.48439 (0.37776) 2025-03-19,00:21:27 | INFO | Train Epoch: 3 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 148.906/s, 148.906/s/gpu LR: 0.000193 Logit Scale: 24.180 Contrastive_loss: 0.54037 (0.37845) Loss: 0.54037 (0.37845) 2025-03-19,00:21:48 | INFO | Train Epoch: 3 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 150.467/s, 150.467/s/gpu LR: 0.000193 Logit Scale: 24.173 Contrastive_loss: 0.50790 (0.37900) Loss: 0.50790 (0.37900) 2025-03-19,00:22:10 | INFO | Train Epoch: 3 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 143.210/s, 143.210/s/gpu LR: 0.000193 Logit Scale: 24.131 Contrastive_loss: 0.56794 (0.37980) Loss: 0.56794 (0.37980) 2025-03-19,00:22:31 | INFO | Train Epoch: 3 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 151.480/s, 151.480/s/gpu LR: 0.000193 Logit Scale: 24.144 Contrastive_loss: 0.14123 (0.37880) Loss: 0.14123 (0.37880) 2025-03-19,00:22:53 | INFO | Train Epoch: 3 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 145.905/s, 145.905/s/gpu LR: 0.000193 Logit Scale: 24.137 Contrastive_loss: 0.38606 (0.37883) Loss: 0.38606 (0.37883) 2025-03-19,00:23:14 | INFO | Train Epoch: 3 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 146.130/s, 146.130/s/gpu LR: 0.000193 Logit Scale: 24.150 Contrastive_loss: 0.23232 (0.37822) Loss: 0.23232 (0.37822) 2025-03-19,00:23:36 | INFO | Train Epoch: 3 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.220, 146.098/s, 146.098/s/gpu LR: 0.000193 Logit Scale: 24.168 Contrastive_loss: 0.31566 (0.37796) Loss: 0.31566 (0.37796) 2025-03-19,00:23:44 | INFO | Train Epoch: 3 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.217, 144.882/s, 144.882/s/gpu LR: 0.000193 Logit Scale: 24.145 Contrastive_loss: 0.77615 (0.37961) Loss: 0.77615 (0.37961) 2025-03-19,00:23:45 | INFO | Eval Epoch: 4 [32 / 7443] Clip Loss: 3.057892 2025-03-19,00:23:50 | INFO | Eval Epoch: 4 [3232 / 7443] Clip Loss: 1.009933 2025-03-19,00:23:56 | INFO | Eval Epoch: 4 [6432 / 7443] Clip Loss: 0.780866 2025-03-19,00:23:59 | INFO | Eval Epoch: 4 image_to_text_mean_rank: 119.5191 image_to_text_median_rank: 9.0000 image_to_text_R@1: 0.1099 image_to_text_R@5: 0.3782 image_to_text_R@10: 0.5417 text_to_image_mean_rank: 83.6890 text_to_image_median_rank: 9.0000 text_to_image_R@1: 0.1100 text_to_image_R@5: 0.3779 text_to_image_R@10: 0.5472 clip_val_loss: 0.7335 epoch: 4.0000 num_samples: 7443.0000 2025-03-19,00:24:32 | INFO | Start epoch 4 2025-03-19,00:24:32 | INFO | Train Epoch: 4 [ 32/766009 (0%)] Data (t): 0.173 Batch (t): 0.371, 86.2849/s, 86.2849/s/gpu LR: 0.000193 Logit Scale: 24.146 Contrastive_loss: 0.32945 (0.32945) Loss: 0.32945 (0.32945) 2025-03-19,00:24:54 | INFO | Train Epoch: 4 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.215, 149.222/s, 149.222/s/gpu LR: 0.000193 Logit Scale: 24.228 Contrastive_loss: 0.30519 (0.31732) Loss: 0.30519 (0.31732) 2025-03-19,00:25:15 | INFO | Train Epoch: 4 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 150.691/s, 150.691/s/gpu LR: 0.000193 Logit Scale: 24.227 Contrastive_loss: 0.26599 (0.30021) Loss: 0.26599 (0.30021) 2025-03-19,00:25:36 | INFO | Train Epoch: 4 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.212, 146.922/s, 146.922/s/gpu LR: 0.000193 Logit Scale: 24.212 Contrastive_loss: 0.28192 (0.29564) Loss: 0.28192 (0.29564) 2025-03-19,00:25:58 | INFO | Train Epoch: 4 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 151.124/s, 151.124/s/gpu LR: 0.000193 Logit Scale: 24.263 Contrastive_loss: 0.30571 (0.29765) Loss: 0.30571 (0.29765) 2025-03-19,00:26:19 | INFO | Train Epoch: 4 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 147.782/s, 147.782/s/gpu LR: 0.000193 Logit Scale: 24.292 Contrastive_loss: 0.45474 (0.32383) Loss: 0.45474 (0.32383) 2025-03-19,00:26:41 | INFO | Train Epoch: 4 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 150.373/s, 150.373/s/gpu LR: 0.000193 Logit Scale: 24.267 Contrastive_loss: 0.19192 (0.30499) Loss: 0.19192 (0.30499) 2025-03-19,00:27:03 | INFO | Train Epoch: 4 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 147.643/s, 147.643/s/gpu LR: 0.000193 Logit Scale: 24.261 Contrastive_loss: 0.11566 (0.28132) Loss: 0.11566 (0.28132) 2025-03-19,00:27:24 | INFO | Train Epoch: 4 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 149.888/s, 149.888/s/gpu LR: 0.000193 Logit Scale: 24.305 Contrastive_loss: 0.23946 (0.27667) Loss: 0.23946 (0.27667) 2025-03-19,00:27:45 | INFO | Train Epoch: 4 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 150.352/s, 150.352/s/gpu LR: 0.000193 Logit Scale: 24.288 Contrastive_loss: 0.27068 (0.27607) Loss: 0.27068 (0.27607) 2025-03-19,00:28:06 | INFO | Train Epoch: 4 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.213, 151.758/s, 151.758/s/gpu LR: 0.000193 Logit Scale: 24.250 Contrastive_loss: 0.55178 (0.30114) Loss: 0.55178 (0.30114) 2025-03-19,00:28:28 | INFO | Train Epoch: 4 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 149.249/s, 149.249/s/gpu LR: 0.000193 Logit Scale: 24.209 Contrastive_loss: 0.24715 (0.29664) Loss: 0.24715 (0.29664) 2025-03-19,00:28:50 | INFO | Train Epoch: 4 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.324/s, 148.324/s/gpu LR: 0.000193 Logit Scale: 24.258 Contrastive_loss: 0.26170 (0.29395) Loss: 0.26170 (0.29395) 2025-03-19,00:29:11 | INFO | Train Epoch: 4 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 147.564/s, 147.564/s/gpu LR: 0.000193 Logit Scale: 24.285 Contrastive_loss: 0.24122 (0.29018) Loss: 0.24122 (0.29018) 2025-03-19,00:29:33 | INFO | Train Epoch: 4 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.213, 149.958/s, 149.958/s/gpu LR: 0.000193 Logit Scale: 24.297 Contrastive_loss: 0.70511 (0.31784) Loss: 0.70511 (0.31784) 2025-03-19,00:29:54 | INFO | Train Epoch: 4 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 151.710/s, 151.710/s/gpu LR: 0.000193 Logit Scale: 24.302 Contrastive_loss: 0.15879 (0.30790) Loss: 0.15879 (0.30790) 2025-03-19,00:30:16 | INFO | Train Epoch: 4 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 148.940/s, 148.940/s/gpu LR: 0.000193 Logit Scale: 24.315 Contrastive_loss: 0.35200 (0.31050) Loss: 0.35200 (0.31050) 2025-03-19,00:30:37 | INFO | Train Epoch: 4 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 147.200/s, 147.200/s/gpu LR: 0.000193 Logit Scale: 24.335 Contrastive_loss: 0.19765 (0.30423) Loss: 0.19765 (0.30423) 2025-03-19,00:30:59 | INFO | Train Epoch: 4 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.218, 146.722/s, 146.722/s/gpu LR: 0.000193 Logit Scale: 24.294 Contrastive_loss: 0.44390 (0.31158) Loss: 0.44390 (0.31158) 2025-03-19,00:31:21 | INFO | Train Epoch: 4 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.220, 144.250/s, 144.250/s/gpu LR: 0.000193 Logit Scale: 24.315 Contrastive_loss: 0.27229 (0.30961) Loss: 0.27229 (0.30961) 2025-03-19,00:31:43 | INFO | Train Epoch: 4 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.221, 146.326/s, 146.326/s/gpu LR: 0.000193 Logit Scale: 24.295 Contrastive_loss: 0.22955 (0.30580) Loss: 0.22955 (0.30580) 2025-03-19,00:32:05 | INFO | Train Epoch: 4 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 148.466/s, 148.466/s/gpu LR: 0.000193 Logit Scale: 24.319 Contrastive_loss: 0.67378 (0.32253) Loss: 0.67378 (0.32253) 2025-03-19,00:32:27 | INFO | Train Epoch: 4 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 149.589/s, 149.589/s/gpu LR: 0.000192 Logit Scale: 24.287 Contrastive_loss: 0.095604 (0.31266) Loss: 0.095604 (0.31266) 2025-03-19,00:32:48 | INFO | Train Epoch: 4 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 148.582/s, 148.582/s/gpu LR: 0.000192 Logit Scale: 24.302 Contrastive_loss: 0.13050 (0.30507) Loss: 0.13050 (0.30507) 2025-03-19,00:33:09 | INFO | Train Epoch: 4 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 151.687/s, 151.687/s/gpu LR: 0.000192 Logit Scale: 24.324 Contrastive_loss: 0.31967 (0.30566) Loss: 0.31967 (0.30566) 2025-03-19,00:33:31 | INFO | Train Epoch: 4 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 148.311/s, 148.311/s/gpu LR: 0.000192 Logit Scale: 24.304 Contrastive_loss: 0.13556 (0.29911) Loss: 0.13556 (0.29911) 2025-03-19,00:33:53 | INFO | Train Epoch: 4 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.218, 146.457/s, 146.457/s/gpu LR: 0.000192 Logit Scale: 24.332 Contrastive_loss: 0.49554 (0.30639) Loss: 0.49554 (0.30639) 2025-03-19,00:34:14 | INFO | Train Epoch: 4 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 147.330/s, 147.330/s/gpu LR: 0.000192 Logit Scale: 24.333 Contrastive_loss: 0.23627 (0.30388) Loss: 0.23627 (0.30388) 2025-03-19,00:34:36 | INFO | Train Epoch: 4 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.218, 146.696/s, 146.696/s/gpu LR: 0.000192 Logit Scale: 24.345 Contrastive_loss: 0.27986 (0.30306) Loss: 0.27986 (0.30306) 2025-03-19,00:34:58 | INFO | Train Epoch: 4 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 148.767/s, 148.767/s/gpu LR: 0.000192 Logit Scale: 24.338 Contrastive_loss: 0.25704 (0.30152) Loss: 0.25704 (0.30152) 2025-03-19,00:35:19 | INFO | Train Epoch: 4 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 147.962/s, 147.962/s/gpu LR: 0.000192 Logit Scale: 24.307 Contrastive_loss: 0.58387 (0.31063) Loss: 0.58387 (0.31063) 2025-03-19,00:35:41 | INFO | Train Epoch: 4 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 148.014/s, 148.014/s/gpu LR: 0.000192 Logit Scale: 24.334 Contrastive_loss: 0.55817 (0.31837) Loss: 0.55817 (0.31837) 2025-03-19,00:36:02 | INFO | Train Epoch: 4 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 149.397/s, 149.397/s/gpu LR: 0.000192 Logit Scale: 24.295 Contrastive_loss: 0.13222 (0.31273) Loss: 0.13222 (0.31273) 2025-03-19,00:36:24 | INFO | Train Epoch: 4 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.218, 145.499/s, 145.499/s/gpu LR: 0.000192 Logit Scale: 24.312 Contrastive_loss: 0.83963 (0.32822) Loss: 0.83963 (0.32822) 2025-03-19,00:36:46 | INFO | Train Epoch: 4 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.220, 146.810/s, 146.810/s/gpu LR: 0.000192 Logit Scale: 24.333 Contrastive_loss: 0.17798 (0.32393) Loss: 0.17798 (0.32393) 2025-03-19,00:37:08 | INFO | Train Epoch: 4 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 145.095/s, 145.095/s/gpu LR: 0.000192 Logit Scale: 24.337 Contrastive_loss: 0.17976 (0.31992) Loss: 0.17976 (0.31992) 2025-03-19,00:37:30 | INFO | Train Epoch: 4 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.219, 149.824/s, 149.824/s/gpu LR: 0.000192 Logit Scale: 24.361 Contrastive_loss: 0.41915 (0.32261) Loss: 0.41915 (0.32261) 2025-03-19,00:37:52 | INFO | Train Epoch: 4 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.220, 148.040/s, 148.040/s/gpu LR: 0.000192 Logit Scale: 24.388 Contrastive_loss: 0.42266 (0.32524) Loss: 0.42266 (0.32524) 2025-03-19,00:38:13 | INFO | Train Epoch: 4 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 152.993/s, 152.993/s/gpu LR: 0.000192 Logit Scale: 24.360 Contrastive_loss: 0.22777 (0.32274) Loss: 0.22777 (0.32274) 2025-03-19,00:38:35 | INFO | Train Epoch: 4 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.219, 143.433/s, 143.433/s/gpu LR: 0.000192 Logit Scale: 24.398 Contrastive_loss: 0.35995 (0.32367) Loss: 0.35995 (0.32367) 2025-03-19,00:38:57 | INFO | Train Epoch: 4 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.219, 146.798/s, 146.798/s/gpu LR: 0.000192 Logit Scale: 24.345 Contrastive_loss: 0.16081 (0.31970) Loss: 0.16081 (0.31970) 2025-03-19,00:39:19 | INFO | Train Epoch: 4 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.220, 147.747/s, 147.747/s/gpu LR: 0.000192 Logit Scale: 24.351 Contrastive_loss: 0.10357 (0.31455) Loss: 0.10357 (0.31455) 2025-03-19,00:39:40 | INFO | Train Epoch: 4 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.494/s, 147.494/s/gpu LR: 0.000192 Logit Scale: 24.341 Contrastive_loss: 0.17134 (0.31122) Loss: 0.17134 (0.31122) 2025-03-19,00:40:02 | INFO | Train Epoch: 4 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 147.689/s, 147.689/s/gpu LR: 0.000192 Logit Scale: 24.354 Contrastive_loss: 0.24893 (0.30981) Loss: 0.24893 (0.30981) 2025-03-19,00:40:23 | INFO | Train Epoch: 4 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.303/s, 147.303/s/gpu LR: 0.000192 Logit Scale: 24.375 Contrastive_loss: 0.35830 (0.31088) Loss: 0.35830 (0.31088) 2025-03-19,00:40:45 | INFO | Train Epoch: 4 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 151.610/s, 151.610/s/gpu LR: 0.000192 Logit Scale: 24.400 Contrastive_loss: 0.091342 (0.30611) Loss: 0.091342 (0.30611) 2025-03-19,00:41:07 | INFO | Train Epoch: 4 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 147.229/s, 147.229/s/gpu LR: 0.000192 Logit Scale: 24.392 Contrastive_loss: 0.24420 (0.30479) Loss: 0.24420 (0.30479) 2025-03-19,00:41:28 | INFO | Train Epoch: 4 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 147.363/s, 147.363/s/gpu LR: 0.000192 Logit Scale: 24.420 Contrastive_loss: 0.20606 (0.30274) Loss: 0.20606 (0.30274) 2025-03-19,00:41:50 | INFO | Train Epoch: 4 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.218, 144.959/s, 144.959/s/gpu LR: 0.000192 Logit Scale: 24.443 Contrastive_loss: 0.39022 (0.30452) Loss: 0.39022 (0.30452) 2025-03-19,00:42:12 | INFO | Train Epoch: 4 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.218, 151.384/s, 151.384/s/gpu LR: 0.000192 Logit Scale: 24.409 Contrastive_loss: 0.35657 (0.30556) Loss: 0.35657 (0.30556) 2025-03-19,00:42:34 | INFO | Train Epoch: 4 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 149.965/s, 149.965/s/gpu LR: 0.000192 Logit Scale: 24.367 Contrastive_loss: 0.22333 (0.30395) Loss: 0.22333 (0.30395) 2025-03-19,00:42:55 | INFO | Train Epoch: 4 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.213, 152.442/s, 152.442/s/gpu LR: 0.000192 Logit Scale: 24.412 Contrastive_loss: 0.40537 (0.30590) Loss: 0.40537 (0.30590) 2025-03-19,00:43:16 | INFO | Train Epoch: 4 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.213, 151.652/s, 151.652/s/gpu LR: 0.000192 Logit Scale: 24.396 Contrastive_loss: 0.29142 (0.30563) Loss: 0.29142 (0.30563) 2025-03-19,00:43:38 | INFO | Train Epoch: 4 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.217, 148.037/s, 148.037/s/gpu LR: 0.000192 Logit Scale: 24.378 Contrastive_loss: 0.34798 (0.30641) Loss: 0.34798 (0.30641) 2025-03-19,00:43:59 | INFO | Train Epoch: 4 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.213, 149.362/s, 149.362/s/gpu LR: 0.000192 Logit Scale: 24.383 Contrastive_loss: 0.19896 (0.30446) Loss: 0.19896 (0.30446) 2025-03-19,00:44:21 | INFO | Train Epoch: 4 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 148.248/s, 148.248/s/gpu LR: 0.000192 Logit Scale: 24.378 Contrastive_loss: 0.31557 (0.30466) Loss: 0.31557 (0.30466) 2025-03-19,00:44:42 | INFO | Train Epoch: 4 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.418/s, 149.418/s/gpu LR: 0.000192 Logit Scale: 24.317 Contrastive_loss: 0.19001 (0.30265) Loss: 0.19001 (0.30265) 2025-03-19,00:45:03 | INFO | Train Epoch: 4 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.213, 149.835/s, 149.835/s/gpu LR: 0.000192 Logit Scale: 24.312 Contrastive_loss: 0.11793 (0.29946) Loss: 0.11793 (0.29946) 2025-03-19,00:45:25 | INFO | Train Epoch: 4 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 149.835/s, 149.835/s/gpu LR: 0.000192 Logit Scale: 24.361 Contrastive_loss: 0.41714 (0.30146) Loss: 0.41714 (0.30146) 2025-03-19,00:45:47 | INFO | Train Epoch: 4 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 146.542/s, 146.542/s/gpu LR: 0.000192 Logit Scale: 24.362 Contrastive_loss: 0.23831 (0.30040) Loss: 0.23831 (0.30040) 2025-03-19,00:46:08 | INFO | Train Epoch: 4 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 148.648/s, 148.648/s/gpu LR: 0.000192 Logit Scale: 24.401 Contrastive_loss: 0.17808 (0.29840) Loss: 0.17808 (0.29840) 2025-03-19,00:46:30 | INFO | Train Epoch: 4 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 149.158/s, 149.158/s/gpu LR: 0.000192 Logit Scale: 24.432 Contrastive_loss: 0.19868 (0.29679) Loss: 0.19868 (0.29679) 2025-03-19,00:46:51 | INFO | Train Epoch: 4 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 151.243/s, 151.243/s/gpu LR: 0.000192 Logit Scale: 24.431 Contrastive_loss: 0.15083 (0.29447) Loss: 0.15083 (0.29447) 2025-03-19,00:47:13 | INFO | Train Epoch: 4 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 149.793/s, 149.793/s/gpu LR: 0.000192 Logit Scale: 24.431 Contrastive_loss: 0.24560 (0.29371) Loss: 0.24560 (0.29371) 2025-03-19,00:47:34 | INFO | Train Epoch: 4 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 150.557/s, 150.557/s/gpu LR: 0.000192 Logit Scale: 24.400 Contrastive_loss: 0.073407 (0.29032) Loss: 0.073407 (0.29032) 2025-03-19,00:47:56 | INFO | Train Epoch: 4 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 149.702/s, 149.702/s/gpu LR: 0.000192 Logit Scale: 24.437 Contrastive_loss: 0.32603 (0.29086) Loss: 0.32603 (0.29086) 2025-03-19,00:48:17 | INFO | Train Epoch: 4 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.741/s, 149.741/s/gpu LR: 0.000192 Logit Scale: 24.401 Contrastive_loss: 0.33948 (0.29159) Loss: 0.33948 (0.29159) 2025-03-19,00:48:38 | INFO | Train Epoch: 4 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.683/s, 149.683/s/gpu LR: 0.000192 Logit Scale: 24.437 Contrastive_loss: 0.34374 (0.29235) Loss: 0.34374 (0.29235) 2025-03-19,00:49:00 | INFO | Train Epoch: 4 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 149.597/s, 149.597/s/gpu LR: 0.000192 Logit Scale: 24.429 Contrastive_loss: 0.28555 (0.29226) Loss: 0.28555 (0.29226) 2025-03-19,00:49:21 | INFO | Train Epoch: 4 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 148.045/s, 148.045/s/gpu LR: 0.000192 Logit Scale: 24.436 Contrastive_loss: 0.45089 (0.29452) Loss: 0.45089 (0.29452) 2025-03-19,00:49:43 | INFO | Train Epoch: 4 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 147.571/s, 147.571/s/gpu LR: 0.000192 Logit Scale: 24.414 Contrastive_loss: 0.40330 (0.29605) Loss: 0.40330 (0.29605) 2025-03-19,00:50:04 | INFO | Train Epoch: 4 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 148.935/s, 148.935/s/gpu LR: 0.000192 Logit Scale: 24.412 Contrastive_loss: 0.42072 (0.29778) Loss: 0.42072 (0.29778) 2025-03-19,00:50:26 | INFO | Train Epoch: 4 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 147.732/s, 147.732/s/gpu LR: 0.000192 Logit Scale: 24.427 Contrastive_loss: 0.22319 (0.29676) Loss: 0.22319 (0.29676) 2025-03-19,00:50:47 | INFO | Train Epoch: 4 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.083/s, 149.083/s/gpu LR: 0.000192 Logit Scale: 24.435 Contrastive_loss: 0.25937 (0.29626) Loss: 0.25937 (0.29626) 2025-03-19,00:51:09 | INFO | Train Epoch: 4 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 150.775/s, 150.775/s/gpu LR: 0.000192 Logit Scale: 24.378 Contrastive_loss: 0.45634 (0.29839) Loss: 0.45634 (0.29839) 2025-03-19,00:51:30 | INFO | Train Epoch: 4 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 149.645/s, 149.645/s/gpu LR: 0.000192 Logit Scale: 24.346 Contrastive_loss: 0.28129 (0.29817) Loss: 0.28129 (0.29817) 2025-03-19,00:51:52 | INFO | Train Epoch: 4 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 150.770/s, 150.770/s/gpu LR: 0.000192 Logit Scale: 24.373 Contrastive_loss: 0.39902 (0.29948) Loss: 0.39902 (0.29948) 2025-03-19,00:52:13 | INFO | Train Epoch: 4 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 149.686/s, 149.686/s/gpu LR: 0.000192 Logit Scale: 24.370 Contrastive_loss: 0.12454 (0.29723) Loss: 0.12454 (0.29723) 2025-03-19,00:52:35 | INFO | Train Epoch: 4 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.494/s, 149.494/s/gpu LR: 0.000192 Logit Scale: 24.385 Contrastive_loss: 0.14969 (0.29537) Loss: 0.14969 (0.29537) 2025-03-19,00:52:56 | INFO | Train Epoch: 4 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.255/s, 147.255/s/gpu LR: 0.000191 Logit Scale: 24.359 Contrastive_loss: 0.19596 (0.29412) Loss: 0.19596 (0.29412) 2025-03-19,00:53:18 | INFO | Train Epoch: 4 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 149.751/s, 149.751/s/gpu LR: 0.000191 Logit Scale: 24.346 Contrastive_loss: 0.21437 (0.29314) Loss: 0.21437 (0.29314) 2025-03-19,00:53:39 | INFO | Train Epoch: 4 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.212, 151.999/s, 151.999/s/gpu LR: 0.000191 Logit Scale: 24.369 Contrastive_loss: 0.41035 (0.29457) Loss: 0.41035 (0.29457) 2025-03-19,00:54:00 | INFO | Train Epoch: 4 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.405/s, 149.405/s/gpu LR: 0.000191 Logit Scale: 24.358 Contrastive_loss: 0.30837 (0.29473) Loss: 0.30837 (0.29473) 2025-03-19,00:54:22 | INFO | Train Epoch: 4 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.041/s, 148.041/s/gpu LR: 0.000191 Logit Scale: 24.346 Contrastive_loss: 0.26398 (0.29437) Loss: 0.26398 (0.29437) 2025-03-19,00:54:43 | INFO | Train Epoch: 4 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 149.164/s, 149.164/s/gpu LR: 0.000191 Logit Scale: 24.358 Contrastive_loss: 0.40107 (0.29562) Loss: 0.40107 (0.29562) 2025-03-19,00:55:05 | INFO | Train Epoch: 4 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.218, 143.707/s, 143.707/s/gpu LR: 0.000191 Logit Scale: 24.365 Contrastive_loss: 0.26125 (0.29522) Loss: 0.26125 (0.29522) 2025-03-19,00:55:27 | INFO | Train Epoch: 4 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.221, 146.941/s, 146.941/s/gpu LR: 0.000191 Logit Scale: 24.370 Contrastive_loss: 0.26826 (0.29491) Loss: 0.26826 (0.29491) 2025-03-19,00:55:49 | INFO | Train Epoch: 4 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.218, 147.033/s, 147.033/s/gpu LR: 0.000191 Logit Scale: 24.398 Contrastive_loss: 0.043191 (0.29205) Loss: 0.043191 (0.29205) 2025-03-19,00:56:11 | INFO | Train Epoch: 4 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.220, 145.047/s, 145.047/s/gpu LR: 0.000191 Logit Scale: 24.404 Contrastive_loss: 0.19091 (0.29092) Loss: 0.19091 (0.29092) 2025-03-19,00:56:33 | INFO | Train Epoch: 4 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.219, 144.916/s, 144.916/s/gpu LR: 0.000191 Logit Scale: 24.353 Contrastive_loss: 0.27693 (0.29076) Loss: 0.27693 (0.29076) 2025-03-19,00:56:55 | INFO | Train Epoch: 4 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.218, 146.453/s, 146.453/s/gpu LR: 0.000191 Logit Scale: 24.379 Contrastive_loss: 0.41038 (0.29208) Loss: 0.41038 (0.29208) 2025-03-19,00:57:17 | INFO | Train Epoch: 4 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.219, 145.399/s, 145.399/s/gpu LR: 0.000191 Logit Scale: 24.378 Contrastive_loss: 0.44841 (0.29378) Loss: 0.44841 (0.29378) 2025-03-19,00:57:39 | INFO | Train Epoch: 4 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.218, 146.033/s, 146.033/s/gpu LR: 0.000191 Logit Scale: 24.313 Contrastive_loss: 0.25238 (0.29333) Loss: 0.25238 (0.29333) 2025-03-19,00:58:00 | INFO | Train Epoch: 4 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.218, 148.580/s, 148.580/s/gpu LR: 0.000191 Logit Scale: 24.278 Contrastive_loss: 0.48645 (0.29539) Loss: 0.48645 (0.29539) 2025-03-19,00:58:22 | INFO | Train Epoch: 4 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 147.854/s, 147.854/s/gpu LR: 0.000191 Logit Scale: 24.282 Contrastive_loss: 0.22106 (0.29460) Loss: 0.22106 (0.29460) 2025-03-19,00:58:44 | INFO | Train Epoch: 4 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 147.796/s, 147.796/s/gpu LR: 0.000191 Logit Scale: 24.296 Contrastive_loss: 0.26288 (0.29427) Loss: 0.26288 (0.29427) 2025-03-19,00:59:05 | INFO | Train Epoch: 4 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 147.706/s, 147.706/s/gpu LR: 0.000191 Logit Scale: 24.336 Contrastive_loss: 0.77686 (0.29925) Loss: 0.77686 (0.29925) 2025-03-19,00:59:27 | INFO | Train Epoch: 4 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 147.159/s, 147.159/s/gpu LR: 0.000191 Logit Scale: 24.295 Contrastive_loss: 0.31040 (0.29936) Loss: 0.31040 (0.29936) 2025-03-19,00:59:48 | INFO | Train Epoch: 4 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.754/s, 148.754/s/gpu LR: 0.000191 Logit Scale: 24.282 Contrastive_loss: 0.37966 (0.30017) Loss: 0.37966 (0.30017) 2025-03-19,01:00:10 | INFO | Train Epoch: 4 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 148.574/s, 148.574/s/gpu LR: 0.000191 Logit Scale: 24.277 Contrastive_loss: 0.13626 (0.29853) Loss: 0.13626 (0.29853) 2025-03-19,01:00:31 | INFO | Train Epoch: 4 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 148.275/s, 148.275/s/gpu LR: 0.000191 Logit Scale: 24.275 Contrastive_loss: 0.26374 (0.29819) Loss: 0.26374 (0.29819) 2025-03-19,01:00:53 | INFO | Train Epoch: 4 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 149.464/s, 149.464/s/gpu LR: 0.000191 Logit Scale: 24.258 Contrastive_loss: 0.21330 (0.29736) Loss: 0.21330 (0.29736) 2025-03-19,01:01:15 | INFO | Train Epoch: 4 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 149.240/s, 149.240/s/gpu LR: 0.000191 Logit Scale: 24.274 Contrastive_loss: 0.22196 (0.29662) Loss: 0.22196 (0.29662) 2025-03-19,01:01:36 | INFO | Train Epoch: 4 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 146.326/s, 146.326/s/gpu LR: 0.000191 Logit Scale: 24.315 Contrastive_loss: 0.013858 (0.29391) Loss: 0.013858 (0.29391) 2025-03-19,01:01:58 | INFO | Train Epoch: 4 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 149.373/s, 149.373/s/gpu LR: 0.000191 Logit Scale: 24.333 Contrastive_loss: 0.22163 (0.29322) Loss: 0.22163 (0.29322) 2025-03-19,01:02:19 | INFO | Train Epoch: 4 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 149.535/s, 149.535/s/gpu LR: 0.000191 Logit Scale: 24.341 Contrastive_loss: 0.36394 (0.29388) Loss: 0.36394 (0.29388) 2025-03-19,01:02:41 | INFO | Train Epoch: 4 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 148.219/s, 148.219/s/gpu LR: 0.000191 Logit Scale: 24.338 Contrastive_loss: 0.29288 (0.29388) Loss: 0.29288 (0.29388) 2025-03-19,01:03:02 | INFO | Train Epoch: 4 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 148.671/s, 148.671/s/gpu LR: 0.000191 Logit Scale: 24.347 Contrastive_loss: 0.35278 (0.29442) Loss: 0.35278 (0.29442) 2025-03-19,01:03:24 | INFO | Train Epoch: 4 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 148.124/s, 148.124/s/gpu LR: 0.000191 Logit Scale: 24.339 Contrastive_loss: 0.26359 (0.29414) Loss: 0.26359 (0.29414) 2025-03-19,01:03:45 | INFO | Train Epoch: 4 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 147.378/s, 147.378/s/gpu LR: 0.000191 Logit Scale: 24.346 Contrastive_loss: 0.15677 (0.29289) Loss: 0.15677 (0.29289) 2025-03-19,01:04:07 | INFO | Train Epoch: 4 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 147.905/s, 147.905/s/gpu LR: 0.000191 Logit Scale: 24.360 Contrastive_loss: 0.42621 (0.29409) Loss: 0.42621 (0.29409) 2025-03-19,01:04:29 | INFO | Train Epoch: 4 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 151.200/s, 151.200/s/gpu LR: 0.000191 Logit Scale: 24.353 Contrastive_loss: 0.13954 (0.29271) Loss: 0.13954 (0.29271) 2025-03-19,01:04:50 | INFO | Train Epoch: 4 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 148.532/s, 148.532/s/gpu LR: 0.000191 Logit Scale: 24.353 Contrastive_loss: 0.12728 (0.29125) Loss: 0.12728 (0.29125) 2025-03-19,01:05:12 | INFO | Train Epoch: 4 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 145.961/s, 145.961/s/gpu LR: 0.000191 Logit Scale: 24.366 Contrastive_loss: 0.34616 (0.29173) Loss: 0.34616 (0.29173) 2025-03-19,01:05:34 | INFO | Train Epoch: 4 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.218, 147.164/s, 147.164/s/gpu LR: 0.000191 Logit Scale: 24.382 Contrastive_loss: 0.28537 (0.29167) Loss: 0.28537 (0.29167) 2025-03-19,01:05:56 | INFO | Train Epoch: 4 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 143.316/s, 143.316/s/gpu LR: 0.000191 Logit Scale: 24.363 Contrastive_loss: 0.31298 (0.29186) Loss: 0.31298 (0.29186) 2025-03-19,01:06:18 | INFO | Train Epoch: 4 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.219, 146.775/s, 146.775/s/gpu LR: 0.000191 Logit Scale: 24.370 Contrastive_loss: 0.23069 (0.29133) Loss: 0.23069 (0.29133) 2025-03-19,01:06:39 | INFO | Train Epoch: 4 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 148.202/s, 148.202/s/gpu LR: 0.000191 Logit Scale: 24.378 Contrastive_loss: 0.26604 (0.29112) Loss: 0.26604 (0.29112) 2025-03-19,01:07:01 | INFO | Train Epoch: 4 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.219, 141.137/s, 141.137/s/gpu LR: 0.000191 Logit Scale: 24.377 Contrastive_loss: 0.71135 (0.29465) Loss: 0.71135 (0.29465) 2025-03-19,01:07:23 | INFO | Train Epoch: 4 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.221, 145.572/s, 145.572/s/gpu LR: 0.000191 Logit Scale: 24.364 Contrastive_loss: 0.26049 (0.29437) Loss: 0.26049 (0.29437) 2025-03-19,01:07:45 | INFO | Train Epoch: 4 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.222, 142.835/s, 142.835/s/gpu LR: 0.000191 Logit Scale: 24.361 Contrastive_loss: 0.18986 (0.29350) Loss: 0.18986 (0.29350) 2025-03-19,01:08:07 | INFO | Train Epoch: 4 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.219, 146.090/s, 146.090/s/gpu LR: 0.000191 Logit Scale: 24.352 Contrastive_loss: 0.045917 (0.29147) Loss: 0.045917 (0.29147) 2025-03-19,01:08:29 | INFO | Train Epoch: 4 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 150.485/s, 150.485/s/gpu LR: 0.000191 Logit Scale: 24.339 Contrastive_loss: 0.14989 (0.29032) Loss: 0.14989 (0.29032) 2025-03-19,01:08:51 | INFO | Train Epoch: 4 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 147.749/s, 147.749/s/gpu LR: 0.000191 Logit Scale: 24.363 Contrastive_loss: 0.26388 (0.29011) Loss: 0.26388 (0.29011) 2025-03-19,01:09:13 | INFO | Train Epoch: 4 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.218, 151.597/s, 151.597/s/gpu LR: 0.000191 Logit Scale: 24.383 Contrastive_loss: 0.43288 (0.29125) Loss: 0.43288 (0.29125) 2025-03-19,01:09:34 | INFO | Train Epoch: 4 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 146.757/s, 146.757/s/gpu LR: 0.000191 Logit Scale: 24.338 Contrastive_loss: 0.34918 (0.29171) Loss: 0.34918 (0.29171) 2025-03-19,01:09:56 | INFO | Train Epoch: 4 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.220, 148.411/s, 148.411/s/gpu LR: 0.000191 Logit Scale: 24.358 Contrastive_loss: 0.48199 (0.29321) Loss: 0.48199 (0.29321) 2025-03-19,01:10:18 | INFO | Train Epoch: 4 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 147.181/s, 147.181/s/gpu LR: 0.000191 Logit Scale: 24.318 Contrastive_loss: 0.28269 (0.29313) Loss: 0.28269 (0.29313) 2025-03-19,01:10:40 | INFO | Train Epoch: 4 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 148.021/s, 148.021/s/gpu LR: 0.000191 Logit Scale: 24.339 Contrastive_loss: 0.44141 (0.29428) Loss: 0.44141 (0.29428) 2025-03-19,01:11:02 | INFO | Train Epoch: 4 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.220, 146.265/s, 146.265/s/gpu LR: 0.000191 Logit Scale: 24.311 Contrastive_loss: 0.37709 (0.29491) Loss: 0.37709 (0.29491) 2025-03-19,01:11:24 | INFO | Train Epoch: 4 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.218, 147.694/s, 147.694/s/gpu LR: 0.000191 Logit Scale: 24.325 Contrastive_loss: 0.22163 (0.29435) Loss: 0.22163 (0.29435) 2025-03-19,01:11:46 | INFO | Train Epoch: 4 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.220, 144.749/s, 144.749/s/gpu LR: 0.000191 Logit Scale: 24.336 Contrastive_loss: 0.35215 (0.29479) Loss: 0.35215 (0.29479) 2025-03-19,01:12:07 | INFO | Train Epoch: 4 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 151.230/s, 151.230/s/gpu LR: 0.000191 Logit Scale: 24.399 Contrastive_loss: 0.26127 (0.29454) Loss: 0.26127 (0.29454) 2025-03-19,01:12:28 | INFO | Train Epoch: 4 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 150.062/s, 150.062/s/gpu LR: 0.000190 Logit Scale: 24.335 Contrastive_loss: 0.38036 (0.29518) Loss: 0.38036 (0.29518) 2025-03-19,01:12:50 | INFO | Train Epoch: 4 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 148.515/s, 148.515/s/gpu LR: 0.000190 Logit Scale: 24.383 Contrastive_loss: 0.28518 (0.29511) Loss: 0.28518 (0.29511) 2025-03-19,01:13:12 | INFO | Train Epoch: 4 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 145.568/s, 145.568/s/gpu LR: 0.000190 Logit Scale: 24.378 Contrastive_loss: 0.30429 (0.29517) Loss: 0.30429 (0.29517) 2025-03-19,01:13:33 | INFO | Train Epoch: 4 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.217, 149.272/s, 149.272/s/gpu LR: 0.000190 Logit Scale: 24.379 Contrastive_loss: 0.20201 (0.29449) Loss: 0.20201 (0.29449) 2025-03-19,01:13:55 | INFO | Train Epoch: 4 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 149.578/s, 149.578/s/gpu LR: 0.000190 Logit Scale: 24.344 Contrastive_loss: 0.34468 (0.29486) Loss: 0.34468 (0.29486) 2025-03-19,01:14:17 | INFO | Train Epoch: 4 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 150.213/s, 150.213/s/gpu LR: 0.000190 Logit Scale: 24.405 Contrastive_loss: 0.23271 (0.29441) Loss: 0.23271 (0.29441) 2025-03-19,01:14:38 | INFO | Train Epoch: 4 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.323/s, 149.323/s/gpu LR: 0.000190 Logit Scale: 24.448 Contrastive_loss: 0.31826 (0.29458) Loss: 0.31826 (0.29458) 2025-03-19,01:14:59 | INFO | Train Epoch: 4 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.778/s, 149.778/s/gpu LR: 0.000190 Logit Scale: 24.439 Contrastive_loss: 0.62171 (0.29690) Loss: 0.62171 (0.29690) 2025-03-19,01:15:21 | INFO | Train Epoch: 4 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.297/s, 149.297/s/gpu LR: 0.000190 Logit Scale: 24.465 Contrastive_loss: 1.1365 (0.30281) Loss: 1.1365 (0.30281) 2025-03-19,01:15:42 | INFO | Train Epoch: 4 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.927/s, 149.927/s/gpu LR: 0.000190 Logit Scale: 24.479 Contrastive_loss: 0.49610 (0.30416) Loss: 0.49610 (0.30416) 2025-03-19,01:16:04 | INFO | Train Epoch: 4 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 150.226/s, 150.226/s/gpu LR: 0.000190 Logit Scale: 24.458 Contrastive_loss: 0.11766 (0.30287) Loss: 0.11766 (0.30287) 2025-03-19,01:16:25 | INFO | Train Epoch: 4 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.600/s, 149.600/s/gpu LR: 0.000190 Logit Scale: 24.459 Contrastive_loss: 0.25905 (0.30257) Loss: 0.25905 (0.30257) 2025-03-19,01:16:47 | INFO | Train Epoch: 4 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 147.239/s, 147.239/s/gpu LR: 0.000190 Logit Scale: 24.443 Contrastive_loss: 0.33417 (0.30278) Loss: 0.33417 (0.30278) 2025-03-19,01:17:08 | INFO | Train Epoch: 4 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.218, 148.224/s, 148.224/s/gpu LR: 0.000190 Logit Scale: 24.453 Contrastive_loss: 0.38725 (0.30336) Loss: 0.38725 (0.30336) 2025-03-19,01:17:30 | INFO | Train Epoch: 4 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.217, 147.806/s, 147.806/s/gpu LR: 0.000190 Logit Scale: 24.437 Contrastive_loss: 0.28946 (0.30326) Loss: 0.28946 (0.30326) 2025-03-19,01:17:52 | INFO | Train Epoch: 4 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 144.144/s, 144.144/s/gpu LR: 0.000190 Logit Scale: 24.446 Contrastive_loss: 0.43576 (0.30415) Loss: 0.43576 (0.30415) 2025-03-19,01:18:13 | INFO | Train Epoch: 4 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 147.421/s, 147.421/s/gpu LR: 0.000190 Logit Scale: 24.443 Contrastive_loss: 0.17318 (0.30328) Loss: 0.17318 (0.30328) 2025-03-19,01:18:35 | INFO | Train Epoch: 4 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 150.955/s, 150.955/s/gpu LR: 0.000190 Logit Scale: 24.453 Contrastive_loss: 0.33332 (0.30348) Loss: 0.33332 (0.30348) 2025-03-19,01:18:56 | INFO | Train Epoch: 4 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.747/s, 148.747/s/gpu LR: 0.000190 Logit Scale: 24.458 Contrastive_loss: 0.59585 (0.30540) Loss: 0.59585 (0.30540) 2025-03-19,01:19:18 | INFO | Train Epoch: 4 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 146.804/s, 146.804/s/gpu LR: 0.000190 Logit Scale: 24.472 Contrastive_loss: 0.22747 (0.30489) Loss: 0.22747 (0.30489) 2025-03-19,01:19:39 | INFO | Train Epoch: 4 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.218, 147.516/s, 147.516/s/gpu LR: 0.000190 Logit Scale: 24.420 Contrastive_loss: 0.67763 (0.30731) Loss: 0.67763 (0.30731) 2025-03-19,01:20:01 | INFO | Train Epoch: 4 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 149.343/s, 149.343/s/gpu LR: 0.000190 Logit Scale: 24.434 Contrastive_loss: 0.57001 (0.30901) Loss: 0.57001 (0.30901) 2025-03-19,01:20:23 | INFO | Train Epoch: 4 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 150.801/s, 150.801/s/gpu LR: 0.000190 Logit Scale: 24.436 Contrastive_loss: 0.26456 (0.30872) Loss: 0.26456 (0.30872) 2025-03-19,01:20:44 | INFO | Train Epoch: 4 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 150.176/s, 150.176/s/gpu LR: 0.000190 Logit Scale: 24.406 Contrastive_loss: 0.41259 (0.30939) Loss: 0.41259 (0.30939) 2025-03-19,01:21:06 | INFO | Train Epoch: 4 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 147.386/s, 147.386/s/gpu LR: 0.000190 Logit Scale: 24.434 Contrastive_loss: 0.46917 (0.31040) Loss: 0.46917 (0.31040) 2025-03-19,01:21:28 | INFO | Train Epoch: 4 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 148.778/s, 148.778/s/gpu LR: 0.000190 Logit Scale: 24.435 Contrastive_loss: 0.23439 (0.30992) Loss: 0.23439 (0.30992) 2025-03-19,01:21:49 | INFO | Train Epoch: 4 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.213, 150.379/s, 150.379/s/gpu LR: 0.000190 Logit Scale: 24.440 Contrastive_loss: 0.19737 (0.30922) Loss: 0.19737 (0.30922) 2025-03-19,01:22:10 | INFO | Train Epoch: 4 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 147.496/s, 147.496/s/gpu LR: 0.000190 Logit Scale: 24.413 Contrastive_loss: 0.19680 (0.30852) Loss: 0.19680 (0.30852) 2025-03-19,01:22:32 | INFO | Train Epoch: 4 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 149.663/s, 149.663/s/gpu LR: 0.000190 Logit Scale: 24.451 Contrastive_loss: 0.19878 (0.30784) Loss: 0.19878 (0.30784) 2025-03-19,01:22:54 | INFO | Train Epoch: 4 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 147.987/s, 147.987/s/gpu LR: 0.000190 Logit Scale: 24.491 Contrastive_loss: 0.16437 (0.30696) Loss: 0.16437 (0.30696) 2025-03-19,01:23:15 | INFO | Train Epoch: 4 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 150.811/s, 150.811/s/gpu LR: 0.000190 Logit Scale: 24.477 Contrastive_loss: 0.41378 (0.30761) Loss: 0.41378 (0.30761) 2025-03-19,01:23:37 | INFO | Train Epoch: 4 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 147.906/s, 147.906/s/gpu LR: 0.000190 Logit Scale: 24.437 Contrastive_loss: 0.58000 (0.30926) Loss: 0.58000 (0.30926) 2025-03-19,01:23:58 | INFO | Train Epoch: 4 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 148.986/s, 148.986/s/gpu LR: 0.000190 Logit Scale: 24.476 Contrastive_loss: 0.36566 (0.30960) Loss: 0.36566 (0.30960) 2025-03-19,01:24:20 | INFO | Train Epoch: 4 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 147.644/s, 147.644/s/gpu LR: 0.000190 Logit Scale: 24.461 Contrastive_loss: 0.34484 (0.30981) Loss: 0.34484 (0.30981) 2025-03-19,01:24:42 | INFO | Train Epoch: 4 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 150.395/s, 150.395/s/gpu LR: 0.000190 Logit Scale: 24.435 Contrastive_loss: 0.24847 (0.30945) Loss: 0.24847 (0.30945) 2025-03-19,01:25:03 | INFO | Train Epoch: 4 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.214, 152.058/s, 152.058/s/gpu LR: 0.000190 Logit Scale: 24.484 Contrastive_loss: 0.41985 (0.31010) Loss: 0.41985 (0.31010) 2025-03-19,01:25:24 | INFO | Train Epoch: 4 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.213, 147.385/s, 147.385/s/gpu LR: 0.000190 Logit Scale: 24.458 Contrastive_loss: 0.37501 (0.31048) Loss: 0.37501 (0.31048) 2025-03-19,01:25:46 | INFO | Train Epoch: 4 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 149.655/s, 149.655/s/gpu LR: 0.000190 Logit Scale: 24.461 Contrastive_loss: 0.22503 (0.30998) Loss: 0.22503 (0.30998) 2025-03-19,01:26:07 | INFO | Train Epoch: 4 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.213, 148.636/s, 148.636/s/gpu LR: 0.000190 Logit Scale: 24.392 Contrastive_loss: 0.27288 (0.30977) Loss: 0.27288 (0.30977) 2025-03-19,01:26:29 | INFO | Train Epoch: 4 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 147.815/s, 147.815/s/gpu LR: 0.000190 Logit Scale: 24.433 Contrastive_loss: 0.10778 (0.30860) Loss: 0.10778 (0.30860) 2025-03-19,01:26:50 | INFO | Train Epoch: 4 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 148.229/s, 148.229/s/gpu LR: 0.000190 Logit Scale: 24.463 Contrastive_loss: 0.37506 (0.30898) Loss: 0.37506 (0.30898) 2025-03-19,01:27:12 | INFO | Train Epoch: 4 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.213, 152.113/s, 152.113/s/gpu LR: 0.000190 Logit Scale: 24.416 Contrastive_loss: 0.31937 (0.30904) Loss: 0.31937 (0.30904) 2025-03-19,01:27:33 | INFO | Train Epoch: 4 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 148.885/s, 148.885/s/gpu LR: 0.000190 Logit Scale: 24.473 Contrastive_loss: 0.47171 (0.30997) Loss: 0.47171 (0.30997) 2025-03-19,01:27:55 | INFO | Train Epoch: 4 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 148.585/s, 148.585/s/gpu LR: 0.000190 Logit Scale: 24.496 Contrastive_loss: 0.30614 (0.30994) Loss: 0.30614 (0.30994) 2025-03-19,01:28:16 | INFO | Train Epoch: 4 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 149.954/s, 149.954/s/gpu LR: 0.000190 Logit Scale: 24.509 Contrastive_loss: 0.28074 (0.30978) Loss: 0.28074 (0.30978) 2025-03-19,01:28:37 | INFO | Train Epoch: 4 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.946/s, 149.946/s/gpu LR: 0.000190 Logit Scale: 24.489 Contrastive_loss: 0.65682 (0.31172) Loss: 0.65682 (0.31172) 2025-03-19,01:28:59 | INFO | Train Epoch: 4 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.739/s, 149.739/s/gpu LR: 0.000190 Logit Scale: 24.478 Contrastive_loss: 0.29712 (0.31164) Loss: 0.29712 (0.31164) 2025-03-19,01:29:20 | INFO | Train Epoch: 4 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 148.991/s, 148.991/s/gpu LR: 0.000190 Logit Scale: 24.502 Contrastive_loss: 0.41744 (0.31222) Loss: 0.41744 (0.31222) 2025-03-19,01:29:42 | INFO | Train Epoch: 4 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.583/s, 149.583/s/gpu LR: 0.000190 Logit Scale: 24.472 Contrastive_loss: 0.54333 (0.31349) Loss: 0.54333 (0.31349) 2025-03-19,01:30:03 | INFO | Train Epoch: 4 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 149.352/s, 149.352/s/gpu LR: 0.000190 Logit Scale: 24.487 Contrastive_loss: 0.25328 (0.31316) Loss: 0.25328 (0.31316) 2025-03-19,01:30:25 | INFO | Train Epoch: 4 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.819/s, 149.819/s/gpu LR: 0.000190 Logit Scale: 24.474 Contrastive_loss: 0.25228 (0.31283) Loss: 0.25228 (0.31283) 2025-03-19,01:30:46 | INFO | Train Epoch: 4 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 148.418/s, 148.418/s/gpu LR: 0.000190 Logit Scale: 24.448 Contrastive_loss: 0.35140 (0.31304) Loss: 0.35140 (0.31304) 2025-03-19,01:31:08 | INFO | Train Epoch: 4 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.515/s, 149.515/s/gpu LR: 0.000189 Logit Scale: 24.455 Contrastive_loss: 0.39106 (0.31346) Loss: 0.39106 (0.31346) 2025-03-19,01:31:29 | INFO | Train Epoch: 4 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 150.132/s, 150.132/s/gpu LR: 0.000189 Logit Scale: 24.451 Contrastive_loss: 0.50248 (0.31447) Loss: 0.50248 (0.31447) 2025-03-19,01:31:51 | INFO | Train Epoch: 4 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 147.328/s, 147.328/s/gpu LR: 0.000189 Logit Scale: 24.429 Contrastive_loss: 0.19429 (0.31383) Loss: 0.19429 (0.31383) 2025-03-19,01:32:12 | INFO | Train Epoch: 4 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.772/s, 149.772/s/gpu LR: 0.000189 Logit Scale: 24.398 Contrastive_loss: 0.15986 (0.31302) Loss: 0.15986 (0.31302) 2025-03-19,01:32:34 | INFO | Train Epoch: 4 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 144.019/s, 144.019/s/gpu LR: 0.000189 Logit Scale: 24.391 Contrastive_loss: 0.27451 (0.31281) Loss: 0.27451 (0.31281) 2025-03-19,01:32:56 | INFO | Train Epoch: 4 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.220, 148.033/s, 148.033/s/gpu LR: 0.000189 Logit Scale: 24.371 Contrastive_loss: 0.46618 (0.31362) Loss: 0.46618 (0.31362) 2025-03-19,01:33:18 | INFO | Train Epoch: 4 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.218, 143.243/s, 143.243/s/gpu LR: 0.000189 Logit Scale: 24.353 Contrastive_loss: 0.30619 (0.31358) Loss: 0.30619 (0.31358) 2025-03-19,01:33:39 | INFO | Train Epoch: 4 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 150.663/s, 150.663/s/gpu LR: 0.000189 Logit Scale: 24.394 Contrastive_loss: 0.19336 (0.31296) Loss: 0.19336 (0.31296) 2025-03-19,01:34:01 | INFO | Train Epoch: 4 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 145.850/s, 145.850/s/gpu LR: 0.000189 Logit Scale: 24.394 Contrastive_loss: 0.26050 (0.31268) Loss: 0.26050 (0.31268) 2025-03-19,01:34:23 | INFO | Train Epoch: 4 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 146.393/s, 146.393/s/gpu LR: 0.000189 Logit Scale: 24.394 Contrastive_loss: 0.40913 (0.31318) Loss: 0.40913 (0.31318) 2025-03-19,01:34:45 | INFO | Train Epoch: 4 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 148.810/s, 148.810/s/gpu LR: 0.000189 Logit Scale: 24.430 Contrastive_loss: 0.16483 (0.31242) Loss: 0.16483 (0.31242) 2025-03-19,01:35:06 | INFO | Train Epoch: 4 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.212, 151.711/s, 151.711/s/gpu LR: 0.000189 Logit Scale: 24.413 Contrastive_loss: 0.75403 (0.31466) Loss: 0.75403 (0.31466) 2025-03-19,01:35:27 | INFO | Train Epoch: 4 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 151.119/s, 151.119/s/gpu LR: 0.000189 Logit Scale: 24.422 Contrastive_loss: 0.60234 (0.31612) Loss: 0.60234 (0.31612) 2025-03-19,01:35:49 | INFO | Train Epoch: 4 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 150.426/s, 150.426/s/gpu LR: 0.000189 Logit Scale: 24.417 Contrastive_loss: 0.31700 (0.31612) Loss: 0.31700 (0.31612) 2025-03-19,01:36:10 | INFO | Train Epoch: 4 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.217, 147.841/s, 147.841/s/gpu LR: 0.000189 Logit Scale: 24.464 Contrastive_loss: 0.39223 (0.31650) Loss: 0.39223 (0.31650) 2025-03-19,01:36:32 | INFO | Train Epoch: 4 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 151.086/s, 151.086/s/gpu LR: 0.000189 Logit Scale: 24.397 Contrastive_loss: 0.25924 (0.31622) Loss: 0.25924 (0.31622) 2025-03-19,01:36:53 | INFO | Train Epoch: 4 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 147.953/s, 147.953/s/gpu LR: 0.000189 Logit Scale: 24.403 Contrastive_loss: 0.032462 (0.31481) Loss: 0.032462 (0.31481) 2025-03-19,01:37:15 | INFO | Train Epoch: 4 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 147.743/s, 147.743/s/gpu LR: 0.000189 Logit Scale: 24.411 Contrastive_loss: 0.27722 (0.31463) Loss: 0.27722 (0.31463) 2025-03-19,01:37:37 | INFO | Train Epoch: 4 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 147.228/s, 147.228/s/gpu LR: 0.000189 Logit Scale: 24.418 Contrastive_loss: 0.58630 (0.31596) Loss: 0.58630 (0.31596) 2025-03-19,01:37:59 | INFO | Train Epoch: 4 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 147.369/s, 147.369/s/gpu LR: 0.000189 Logit Scale: 24.427 Contrastive_loss: 0.17678 (0.31528) Loss: 0.17678 (0.31528) 2025-03-19,01:38:21 | INFO | Train Epoch: 4 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.219, 148.480/s, 148.480/s/gpu LR: 0.000189 Logit Scale: 24.412 Contrastive_loss: 0.65869 (0.31695) Loss: 0.65869 (0.31695) 2025-03-19,01:38:42 | INFO | Train Epoch: 4 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.400/s, 148.400/s/gpu LR: 0.000189 Logit Scale: 24.456 Contrastive_loss: 0.21101 (0.31644) Loss: 0.21101 (0.31644) 2025-03-19,01:39:04 | INFO | Train Epoch: 4 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 151.204/s, 151.204/s/gpu LR: 0.000189 Logit Scale: 24.418 Contrastive_loss: 0.25883 (0.31616) Loss: 0.25883 (0.31616) 2025-03-19,01:39:25 | INFO | Train Epoch: 4 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.212, 152.058/s, 152.058/s/gpu LR: 0.000189 Logit Scale: 24.441 Contrastive_loss: 0.27978 (0.31598) Loss: 0.27978 (0.31598) 2025-03-19,01:39:46 | INFO | Train Epoch: 4 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.212, 148.994/s, 148.994/s/gpu LR: 0.000189 Logit Scale: 24.448 Contrastive_loss: 0.39057 (0.31634) Loss: 0.39057 (0.31634) 2025-03-19,01:40:08 | INFO | Train Epoch: 4 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.218, 146.037/s, 146.037/s/gpu LR: 0.000189 Logit Scale: 24.480 Contrastive_loss: 0.37802 (0.31663) Loss: 0.37802 (0.31663) 2025-03-19,01:40:30 | INFO | Train Epoch: 4 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.218, 143.959/s, 143.959/s/gpu LR: 0.000189 Logit Scale: 24.431 Contrastive_loss: 0.40855 (0.31707) Loss: 0.40855 (0.31707) 2025-03-19,01:40:52 | INFO | Train Epoch: 4 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 145.431/s, 145.431/s/gpu LR: 0.000189 Logit Scale: 24.470 Contrastive_loss: 0.17876 (0.31642) Loss: 0.17876 (0.31642) 2025-03-19,01:41:13 | INFO | Train Epoch: 4 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.218, 148.009/s, 148.009/s/gpu LR: 0.000189 Logit Scale: 24.420 Contrastive_loss: 0.14460 (0.31561) Loss: 0.14460 (0.31561) 2025-03-19,01:41:35 | INFO | Train Epoch: 4 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 148.513/s, 148.513/s/gpu LR: 0.000189 Logit Scale: 24.368 Contrastive_loss: 0.13284 (0.31476) Loss: 0.13284 (0.31476) 2025-03-19,01:41:56 | INFO | Train Epoch: 4 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 149.465/s, 149.465/s/gpu LR: 0.000189 Logit Scale: 24.408 Contrastive_loss: 0.075475 (0.31366) Loss: 0.075475 (0.31366) 2025-03-19,01:42:18 | INFO | Train Epoch: 4 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 149.140/s, 149.140/s/gpu LR: 0.000189 Logit Scale: 24.445 Contrastive_loss: 0.67999 (0.31534) Loss: 0.67999 (0.31534) 2025-03-19,01:42:39 | INFO | Train Epoch: 4 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 149.415/s, 149.415/s/gpu LR: 0.000189 Logit Scale: 24.479 Contrastive_loss: 0.49318 (0.31616) Loss: 0.49318 (0.31616) 2025-03-19,01:43:01 | INFO | Train Epoch: 4 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.318/s, 149.318/s/gpu LR: 0.000189 Logit Scale: 24.476 Contrastive_loss: 0.35529 (0.31634) Loss: 0.35529 (0.31634) 2025-03-19,01:43:22 | INFO | Train Epoch: 4 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 145.902/s, 145.902/s/gpu LR: 0.000189 Logit Scale: 24.491 Contrastive_loss: 0.28637 (0.31620) Loss: 0.28637 (0.31620) 2025-03-19,01:43:44 | INFO | Train Epoch: 4 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 147.056/s, 147.056/s/gpu LR: 0.000189 Logit Scale: 24.510 Contrastive_loss: 0.30123 (0.31613) Loss: 0.30123 (0.31613) 2025-03-19,01:44:06 | INFO | Train Epoch: 4 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 146.963/s, 146.963/s/gpu LR: 0.000189 Logit Scale: 24.544 Contrastive_loss: 0.57077 (0.31728) Loss: 0.57077 (0.31728) 2025-03-19,01:44:27 | INFO | Train Epoch: 4 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.219/s, 149.219/s/gpu LR: 0.000189 Logit Scale: 24.524 Contrastive_loss: 0.25897 (0.31702) Loss: 0.25897 (0.31702) 2025-03-19,01:44:49 | INFO | Train Epoch: 4 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 148.811/s, 148.811/s/gpu LR: 0.000189 Logit Scale: 24.513 Contrastive_loss: 0.47496 (0.31772) Loss: 0.47496 (0.31772) 2025-03-19,01:45:10 | INFO | Train Epoch: 4 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.044/s, 149.044/s/gpu LR: 0.000189 Logit Scale: 24.528 Contrastive_loss: 0.41212 (0.31814) Loss: 0.41212 (0.31814) 2025-03-19,01:45:32 | INFO | Train Epoch: 4 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 145.710/s, 145.710/s/gpu LR: 0.000189 Logit Scale: 24.517 Contrastive_loss: 0.23214 (0.31776) Loss: 0.23214 (0.31776) 2025-03-19,01:45:54 | INFO | Train Epoch: 4 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.218, 148.242/s, 148.242/s/gpu LR: 0.000189 Logit Scale: 24.519 Contrastive_loss: 0.27318 (0.31757) Loss: 0.27318 (0.31757) 2025-03-19,01:46:15 | INFO | Train Epoch: 4 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 149.672/s, 149.672/s/gpu LR: 0.000189 Logit Scale: 24.491 Contrastive_loss: 0.41512 (0.31799) Loss: 0.41512 (0.31799) 2025-03-19,01:46:37 | INFO | Train Epoch: 4 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.212, 150.574/s, 150.574/s/gpu LR: 0.000189 Logit Scale: 24.509 Contrastive_loss: 0.23108 (0.31762) Loss: 0.23108 (0.31762) 2025-03-19,01:46:58 | INFO | Train Epoch: 4 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.212, 150.832/s, 150.832/s/gpu LR: 0.000189 Logit Scale: 24.471 Contrastive_loss: 0.29281 (0.31751) Loss: 0.29281 (0.31751) 2025-03-19,01:47:20 | INFO | Train Epoch: 4 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.218, 145.688/s, 145.688/s/gpu LR: 0.000189 Logit Scale: 24.493 Contrastive_loss: 0.27626 (0.31733) Loss: 0.27626 (0.31733) 2025-03-19,01:47:42 | INFO | Train Epoch: 4 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.220, 146.589/s, 146.589/s/gpu LR: 0.000189 Logit Scale: 24.496 Contrastive_loss: 0.33357 (0.31740) Loss: 0.33357 (0.31740) 2025-03-19,01:48:03 | INFO | Train Epoch: 4 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.219, 145.766/s, 145.766/s/gpu LR: 0.000189 Logit Scale: 24.510 Contrastive_loss: 0.45193 (0.31798) Loss: 0.45193 (0.31798) 2025-03-19,01:48:26 | INFO | Train Epoch: 4 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.221, 145.518/s, 145.518/s/gpu LR: 0.000189 Logit Scale: 24.526 Contrastive_loss: 0.11991 (0.31713) Loss: 0.11991 (0.31713) 2025-03-19,01:48:48 | INFO | Train Epoch: 4 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.220, 147.749/s, 147.749/s/gpu LR: 0.000189 Logit Scale: 24.528 Contrastive_loss: 0.36195 (0.31732) Loss: 0.36195 (0.31732) 2025-03-19,01:49:09 | INFO | Train Epoch: 4 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 147.365/s, 147.365/s/gpu LR: 0.000188 Logit Scale: 24.516 Contrastive_loss: 0.42077 (0.31776) Loss: 0.42077 (0.31776) 2025-03-19,01:49:31 | INFO | Train Epoch: 4 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 146.650/s, 146.650/s/gpu LR: 0.000188 Logit Scale: 24.592 Contrastive_loss: 0.29495 (0.31766) Loss: 0.29495 (0.31766) 2025-03-19,01:49:53 | INFO | Train Epoch: 4 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 147.298/s, 147.298/s/gpu LR: 0.000188 Logit Scale: 24.580 Contrastive_loss: 0.10325 (0.31676) Loss: 0.10325 (0.31676) 2025-03-19,01:50:15 | INFO | Train Epoch: 4 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 146.582/s, 146.582/s/gpu LR: 0.000188 Logit Scale: 24.578 Contrastive_loss: 0.54995 (0.31774) Loss: 0.54995 (0.31774) 2025-03-19,01:50:36 | INFO | Train Epoch: 4 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.217, 150.506/s, 150.506/s/gpu LR: 0.000188 Logit Scale: 24.537 Contrastive_loss: 0.13994 (0.31700) Loss: 0.13994 (0.31700) 2025-03-19,01:50:44 | INFO | Train Epoch: 4 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.214, 150.777/s, 150.777/s/gpu LR: 0.000188 Logit Scale: 24.544 Contrastive_loss: 0.28233 (0.31685) Loss: 0.28233 (0.31685) 2025-03-19,01:50:44 | INFO | Eval Epoch: 5 [32 / 7443] Clip Loss: 2.969658 2025-03-19,01:50:50 | INFO | Eval Epoch: 5 [3232 / 7443] Clip Loss: 0.954650 2025-03-19,01:50:56 | INFO | Eval Epoch: 5 [6432 / 7443] Clip Loss: 0.743831 2025-03-19,01:50:59 | INFO | Eval Epoch: 5 image_to_text_mean_rank: 112.0824 image_to_text_median_rank: 8.0000 image_to_text_R@1: 0.1079 image_to_text_R@5: 0.3883 image_to_text_R@10: 0.5593 text_to_image_mean_rank: 77.2623 text_to_image_median_rank: 8.0000 text_to_image_R@1: 0.1119 text_to_image_R@5: 0.3879 text_to_image_R@10: 0.5531 clip_val_loss: 0.7101 epoch: 5.0000 num_samples: 7443.0000 2025-03-19,01:51:32 | INFO | Start epoch 5 2025-03-19,01:51:32 | INFO | Train Epoch: 5 [ 32/766009 (0%)] Data (t): 0.170 Batch (t): 0.370, 86.4759/s, 86.4759/s/gpu LR: 0.000188 Logit Scale: 24.544 Contrastive_loss: 0.22564 (0.22564) Loss: 0.22564 (0.22564) 2025-03-19,01:51:54 | INFO | Train Epoch: 5 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.221, 145.274/s, 145.274/s/gpu LR: 0.000188 Logit Scale: 24.546 Contrastive_loss: 0.40091 (0.31328) Loss: 0.40091 (0.31328) 2025-03-19,01:52:16 | INFO | Train Epoch: 5 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.218, 145.478/s, 145.478/s/gpu LR: 0.000188 Logit Scale: 24.582 Contrastive_loss: 0.26963 (0.29873) Loss: 0.26963 (0.29873) 2025-03-19,01:52:38 | INFO | Train Epoch: 5 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 148.721/s, 148.721/s/gpu LR: 0.000188 Logit Scale: 24.617 Contrastive_loss: 0.24243 (0.28465) Loss: 0.24243 (0.28465) 2025-03-19,01:52:59 | INFO | Train Epoch: 5 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.764/s, 149.764/s/gpu LR: 0.000188 Logit Scale: 24.623 Contrastive_loss: 0.020288 (0.23178) Loss: 0.020288 (0.23178) 2025-03-19,01:53:20 | INFO | Train Epoch: 5 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 148.951/s, 148.951/s/gpu LR: 0.000188 Logit Scale: 24.620 Contrastive_loss: 0.49304 (0.27532) Loss: 0.49304 (0.27532) 2025-03-19,01:53:42 | INFO | Train Epoch: 5 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.521/s, 149.521/s/gpu LR: 0.000188 Logit Scale: 24.629 Contrastive_loss: 0.30519 (0.27959) Loss: 0.30519 (0.27959) 2025-03-19,01:54:03 | INFO | Train Epoch: 5 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 147.831/s, 147.831/s/gpu LR: 0.000188 Logit Scale: 24.669 Contrastive_loss: 0.50997 (0.30839) Loss: 0.50997 (0.30839) 2025-03-19,01:54:25 | INFO | Train Epoch: 5 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 148.136/s, 148.136/s/gpu LR: 0.000188 Logit Scale: 24.688 Contrastive_loss: 0.22594 (0.29923) Loss: 0.22594 (0.29923) 2025-03-19,01:54:47 | INFO | Train Epoch: 5 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.216, 149.615/s, 149.615/s/gpu LR: 0.000188 Logit Scale: 24.718 Contrastive_loss: 0.31064 (0.30037) Loss: 0.31064 (0.30037) 2025-03-19,01:55:08 | INFO | Train Epoch: 5 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 150.384/s, 150.384/s/gpu LR: 0.000188 Logit Scale: 24.690 Contrastive_loss: 0.31044 (0.30128) Loss: 0.31044 (0.30128) 2025-03-19,01:55:30 | INFO | Train Epoch: 5 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 147.412/s, 147.412/s/gpu LR: 0.000188 Logit Scale: 24.655 Contrastive_loss: 0.18123 (0.29128) Loss: 0.18123 (0.29128) 2025-03-19,01:55:52 | INFO | Train Epoch: 5 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 147.669/s, 147.669/s/gpu LR: 0.000188 Logit Scale: 24.680 Contrastive_loss: 0.29953 (0.29191) Loss: 0.29953 (0.29191) 2025-03-19,01:56:13 | INFO | Train Epoch: 5 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.219, 148.341/s, 148.341/s/gpu LR: 0.000188 Logit Scale: 24.656 Contrastive_loss: 0.44909 (0.30314) Loss: 0.44909 (0.30314) 2025-03-19,01:56:35 | INFO | Train Epoch: 5 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.220, 147.039/s, 147.039/s/gpu LR: 0.000188 Logit Scale: 24.675 Contrastive_loss: 0.18247 (0.29510) Loss: 0.18247 (0.29510) 2025-03-19,01:56:57 | INFO | Train Epoch: 5 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.217, 147.895/s, 147.895/s/gpu LR: 0.000188 Logit Scale: 24.672 Contrastive_loss: 0.32177 (0.29676) Loss: 0.32177 (0.29676) 2025-03-19,01:57:19 | INFO | Train Epoch: 5 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 148.345/s, 148.345/s/gpu LR: 0.000188 Logit Scale: 24.653 Contrastive_loss: 0.67960 (0.31928) Loss: 0.67960 (0.31928) 2025-03-19,01:57:41 | INFO | Train Epoch: 5 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 145.724/s, 145.724/s/gpu LR: 0.000188 Logit Scale: 24.618 Contrastive_loss: 0.29305 (0.31783) Loss: 0.29305 (0.31783) 2025-03-19,01:58:03 | INFO | Train Epoch: 5 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.221, 145.612/s, 145.612/s/gpu LR: 0.000188 Logit Scale: 24.642 Contrastive_loss: 0.17758 (0.31044) Loss: 0.17758 (0.31044) 2025-03-19,01:58:25 | INFO | Train Epoch: 5 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.219, 146.814/s, 146.814/s/gpu LR: 0.000188 Logit Scale: 24.613 Contrastive_loss: 0.49512 (0.31968) Loss: 0.49512 (0.31968) 2025-03-19,01:58:46 | INFO | Train Epoch: 5 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.218, 147.619/s, 147.619/s/gpu LR: 0.000188 Logit Scale: 24.616 Contrastive_loss: 0.028144 (0.30580) Loss: 0.028144 (0.30580) 2025-03-19,01:59:08 | INFO | Train Epoch: 5 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.219, 146.530/s, 146.530/s/gpu LR: 0.000188 Logit Scale: 24.637 Contrastive_loss: 0.22087 (0.30194) Loss: 0.22087 (0.30194) 2025-03-19,01:59:30 | INFO | Train Epoch: 5 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.219, 145.409/s, 145.409/s/gpu LR: 0.000188 Logit Scale: 24.631 Contrastive_loss: 0.48959 (0.31009) Loss: 0.48959 (0.31009) 2025-03-19,01:59:52 | INFO | Train Epoch: 5 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 150.838/s, 150.838/s/gpu LR: 0.000188 Logit Scale: 24.670 Contrastive_loss: 0.41692 (0.31455) Loss: 0.41692 (0.31455) 2025-03-19,02:00:14 | INFO | Train Epoch: 5 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 147.930/s, 147.930/s/gpu LR: 0.000188 Logit Scale: 24.694 Contrastive_loss: 0.28863 (0.31351) Loss: 0.28863 (0.31351) 2025-03-19,02:00:35 | INFO | Train Epoch: 5 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.218, 148.214/s, 148.214/s/gpu LR: 0.000188 Logit Scale: 24.697 Contrastive_loss: 0.15829 (0.30754) Loss: 0.15829 (0.30754) 2025-03-19,02:00:57 | INFO | Train Epoch: 5 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.215, 149.181/s, 149.181/s/gpu LR: 0.000188 Logit Scale: 24.696 Contrastive_loss: 0.45904 (0.31315) Loss: 0.45904 (0.31315) 2025-03-19,02:01:18 | INFO | Train Epoch: 5 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.215, 150.118/s, 150.118/s/gpu LR: 0.000188 Logit Scale: 24.693 Contrastive_loss: 0.11375 (0.30603) Loss: 0.11375 (0.30603) 2025-03-19,02:01:40 | INFO | Train Epoch: 5 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.214, 149.882/s, 149.882/s/gpu LR: 0.000188 Logit Scale: 24.707 Contrastive_loss: 0.22319 (0.30317) Loss: 0.22319 (0.30317) 2025-03-19,02:02:01 | INFO | Train Epoch: 5 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.214, 149.439/s, 149.439/s/gpu LR: 0.000188 Logit Scale: 24.747 Contrastive_loss: 0.25979 (0.30173) Loss: 0.25979 (0.30173) 2025-03-19,02:02:23 | INFO | Train Epoch: 5 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.225/s, 149.225/s/gpu LR: 0.000188 Logit Scale: 24.798 Contrastive_loss: 0.65875 (0.31324) Loss: 0.65875 (0.31324) 2025-03-19,02:02:44 | INFO | Train Epoch: 5 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 150.583/s, 150.583/s/gpu LR: 0.000188 Logit Scale: 24.787 Contrastive_loss: 0.21858 (0.31029) Loss: 0.21858 (0.31029) 2025-03-19,02:03:06 | INFO | Train Epoch: 5 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.213, 150.087/s, 150.087/s/gpu LR: 0.000188 Logit Scale: 24.809 Contrastive_loss: 0.41324 (0.31340) Loss: 0.41324 (0.31340) 2025-03-19,02:03:27 | INFO | Train Epoch: 5 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.363/s, 148.363/s/gpu LR: 0.000188 Logit Scale: 24.724 Contrastive_loss: 0.40042 (0.31596) Loss: 0.40042 (0.31596) 2025-03-19,02:03:49 | INFO | Train Epoch: 5 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 147.385/s, 147.385/s/gpu LR: 0.000188 Logit Scale: 24.720 Contrastive_loss: 0.41522 (0.31880) Loss: 0.41522 (0.31880) 2025-03-19,02:04:11 | INFO | Train Epoch: 5 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.220, 84.6829/s, 84.6829/s/gpu LR: 0.000188 Logit Scale: 24.681 Contrastive_loss: 0.36282 (0.32002) Loss: 0.36282 (0.32002) 2025-03-19,02:04:32 | INFO | Train Epoch: 5 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 150.385/s, 150.385/s/gpu LR: 0.000188 Logit Scale: 24.689 Contrastive_loss: 0.11009 (0.31435) Loss: 0.11009 (0.31435) 2025-03-19,02:04:55 | INFO | Train Epoch: 5 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.221, 141.870/s, 141.870/s/gpu LR: 0.000188 Logit Scale: 24.669 Contrastive_loss: 0.19597 (0.31123) Loss: 0.19597 (0.31123) 2025-03-19,02:05:17 | INFO | Train Epoch: 5 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.220, 147.348/s, 147.348/s/gpu LR: 0.000188 Logit Scale: 24.729 Contrastive_loss: 0.38360 (0.31309) Loss: 0.38360 (0.31309) 2025-03-19,02:05:38 | INFO | Train Epoch: 5 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.220, 137.895/s, 137.895/s/gpu LR: 0.000188 Logit Scale: 24.724 Contrastive_loss: 0.56006 (0.31926) Loss: 0.56006 (0.31926) 2025-03-19,02:06:01 | INFO | Train Epoch: 5 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.222, 146.309/s, 146.309/s/gpu LR: 0.000188 Logit Scale: 24.741 Contrastive_loss: 0.27915 (0.31829) Loss: 0.27915 (0.31829) 2025-03-19,02:06:22 | INFO | Train Epoch: 5 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 148.240/s, 148.240/s/gpu LR: 0.000188 Logit Scale: 24.783 Contrastive_loss: 0.35357 (0.31913) Loss: 0.35357 (0.31913) 2025-03-19,02:06:44 | INFO | Train Epoch: 5 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.596/s, 147.596/s/gpu LR: 0.000188 Logit Scale: 24.795 Contrastive_loss: 0.26057 (0.31776) Loss: 0.26057 (0.31776) 2025-03-19,02:07:06 | INFO | Train Epoch: 5 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 148.369/s, 148.369/s/gpu LR: 0.000187 Logit Scale: 24.756 Contrastive_loss: 0.21467 (0.31542) Loss: 0.21467 (0.31542) 2025-03-19,02:07:28 | INFO | Train Epoch: 5 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.222, 144.067/s, 144.067/s/gpu LR: 0.000187 Logit Scale: 24.730 Contrastive_loss: 0.63726 (0.32257) Loss: 0.63726 (0.32257) 2025-03-19,02:07:50 | INFO | Train Epoch: 5 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.220, 146.703/s, 146.703/s/gpu LR: 0.000187 Logit Scale: 24.768 Contrastive_loss: 0.27286 (0.32149) Loss: 0.27286 (0.32149) 2025-03-19,02:08:11 | INFO | Train Epoch: 5 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 148.891/s, 148.891/s/gpu LR: 0.000187 Logit Scale: 24.758 Contrastive_loss: 0.61340 (0.32770) Loss: 0.61340 (0.32770) 2025-03-19,02:08:33 | INFO | Train Epoch: 5 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.550/s, 149.550/s/gpu LR: 0.000187 Logit Scale: 24.756 Contrastive_loss: 0.37824 (0.32876) Loss: 0.37824 (0.32876) 2025-03-19,02:08:54 | INFO | Train Epoch: 5 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.247/s, 149.247/s/gpu LR: 0.000187 Logit Scale: 24.793 Contrastive_loss: 0.15724 (0.32526) Loss: 0.15724 (0.32526) 2025-03-19,02:09:16 | INFO | Train Epoch: 5 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.624/s, 149.624/s/gpu LR: 0.000187 Logit Scale: 24.754 Contrastive_loss: 0.23872 (0.32352) Loss: 0.23872 (0.32352) 2025-03-19,02:09:37 | INFO | Train Epoch: 5 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 152.116/s, 152.116/s/gpu LR: 0.000187 Logit Scale: 24.771 Contrastive_loss: 0.40500 (0.32512) Loss: 0.40500 (0.32512) 2025-03-19,02:09:58 | INFO | Train Epoch: 5 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.212, 151.542/s, 151.542/s/gpu LR: 0.000187 Logit Scale: 24.779 Contrastive_loss: 0.28524 (0.32436) Loss: 0.28524 (0.32436) 2025-03-19,02:10:20 | INFO | Train Epoch: 5 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.468/s, 149.468/s/gpu LR: 0.000187 Logit Scale: 24.778 Contrastive_loss: 0.49969 (0.32766) Loss: 0.49969 (0.32766) 2025-03-19,02:10:41 | INFO | Train Epoch: 5 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 149.659/s, 149.659/s/gpu LR: 0.000187 Logit Scale: 24.807 Contrastive_loss: 0.13524 (0.32410) Loss: 0.13524 (0.32410) 2025-03-19,02:11:03 | INFO | Train Epoch: 5 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 146.900/s, 146.900/s/gpu LR: 0.000187 Logit Scale: 24.797 Contrastive_loss: 0.19618 (0.32177) Loss: 0.19618 (0.32177) 2025-03-19,02:11:24 | INFO | Train Epoch: 5 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 144.303/s, 144.303/s/gpu LR: 0.000187 Logit Scale: 24.798 Contrastive_loss: 0.31331 (0.32162) Loss: 0.31331 (0.32162) 2025-03-19,02:11:46 | INFO | Train Epoch: 5 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.256/s, 149.256/s/gpu LR: 0.000187 Logit Scale: 24.835 Contrastive_loss: 0.34256 (0.32199) Loss: 0.34256 (0.32199) 2025-03-19,02:12:07 | INFO | Train Epoch: 5 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 148.443/s, 148.443/s/gpu LR: 0.000187 Logit Scale: 24.779 Contrastive_loss: 0.58643 (0.32655) Loss: 0.58643 (0.32655) 2025-03-19,02:12:29 | INFO | Train Epoch: 5 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 148.828/s, 148.828/s/gpu LR: 0.000187 Logit Scale: 24.805 Contrastive_loss: 0.16331 (0.32378) Loss: 0.16331 (0.32378) 2025-03-19,02:12:51 | INFO | Train Epoch: 5 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 148.532/s, 148.532/s/gpu LR: 0.000187 Logit Scale: 24.832 Contrastive_loss: 0.37434 (0.32463) Loss: 0.37434 (0.32463) 2025-03-19,02:13:12 | INFO | Train Epoch: 5 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 150.066/s, 150.066/s/gpu LR: 0.000187 Logit Scale: 24.770 Contrastive_loss: 0.14461 (0.32167) Loss: 0.14461 (0.32167) 2025-03-19,02:13:33 | INFO | Train Epoch: 5 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 150.135/s, 150.135/s/gpu LR: 0.000187 Logit Scale: 24.782 Contrastive_loss: 0.19449 (0.31962) Loss: 0.19449 (0.31962) 2025-03-19,02:13:55 | INFO | Train Epoch: 5 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 149.822/s, 149.822/s/gpu LR: 0.000187 Logit Scale: 24.802 Contrastive_loss: 0.35352 (0.32016) Loss: 0.35352 (0.32016) 2025-03-19,02:14:16 | INFO | Train Epoch: 5 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 150.079/s, 150.079/s/gpu LR: 0.000187 Logit Scale: 24.776 Contrastive_loss: 0.35756 (0.32075) Loss: 0.35756 (0.32075) 2025-03-19,02:14:38 | INFO | Train Epoch: 5 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 149.812/s, 149.812/s/gpu LR: 0.000187 Logit Scale: 24.782 Contrastive_loss: 0.15291 (0.31816) Loss: 0.15291 (0.31816) 2025-03-19,02:14:59 | INFO | Train Epoch: 5 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 150.424/s, 150.424/s/gpu LR: 0.000187 Logit Scale: 24.745 Contrastive_loss: 0.71761 (0.32422) Loss: 0.71761 (0.32422) 2025-03-19,02:15:20 | INFO | Train Epoch: 5 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.491/s, 149.491/s/gpu LR: 0.000187 Logit Scale: 24.729 Contrastive_loss: 0.61532 (0.32856) Loss: 0.61532 (0.32856) 2025-03-19,02:15:42 | INFO | Train Epoch: 5 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 148.884/s, 148.884/s/gpu LR: 0.000187 Logit Scale: 24.693 Contrastive_loss: 0.35338 (0.32893) Loss: 0.35338 (0.32893) 2025-03-19,02:16:03 | INFO | Train Epoch: 5 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 150.098/s, 150.098/s/gpu LR: 0.000187 Logit Scale: 24.758 Contrastive_loss: 0.13334 (0.32609) Loss: 0.13334 (0.32609) 2025-03-19,02:16:25 | INFO | Train Epoch: 5 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.213, 148.798/s, 148.798/s/gpu LR: 0.000187 Logit Scale: 24.735 Contrastive_loss: 0.27965 (0.32543) Loss: 0.27965 (0.32543) 2025-03-19,02:16:46 | INFO | Train Epoch: 5 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 149.932/s, 149.932/s/gpu LR: 0.000187 Logit Scale: 24.753 Contrastive_loss: 0.38763 (0.32630) Loss: 0.38763 (0.32630) 2025-03-19,02:17:08 | INFO | Train Epoch: 5 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.855/s, 149.855/s/gpu LR: 0.000187 Logit Scale: 24.806 Contrastive_loss: 0.10849 (0.32328) Loss: 0.10849 (0.32328) 2025-03-19,02:17:29 | INFO | Train Epoch: 5 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.213, 150.875/s, 150.875/s/gpu LR: 0.000187 Logit Scale: 24.819 Contrastive_loss: 0.23818 (0.32211) Loss: 0.23818 (0.32211) 2025-03-19,02:17:50 | INFO | Train Epoch: 5 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 150.137/s, 150.137/s/gpu LR: 0.000187 Logit Scale: 24.799 Contrastive_loss: 0.31383 (0.32200) Loss: 0.31383 (0.32200) 2025-03-19,02:18:12 | INFO | Train Epoch: 5 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.897/s, 148.897/s/gpu LR: 0.000187 Logit Scale: 24.767 Contrastive_loss: 0.35611 (0.32246) Loss: 0.35611 (0.32246) 2025-03-19,02:18:33 | INFO | Train Epoch: 5 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 148.810/s, 148.810/s/gpu LR: 0.000187 Logit Scale: 24.784 Contrastive_loss: 0.78508 (0.32854) Loss: 0.78508 (0.32854) 2025-03-19,02:18:55 | INFO | Train Epoch: 5 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 141.162/s, 141.162/s/gpu LR: 0.000187 Logit Scale: 24.772 Contrastive_loss: 0.35193 (0.32885) Loss: 0.35193 (0.32885) 2025-03-19,02:19:16 | INFO | Train Epoch: 5 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 149.524/s, 149.524/s/gpu LR: 0.000187 Logit Scale: 24.764 Contrastive_loss: 0.12572 (0.32624) Loss: 0.12572 (0.32624) 2025-03-19,02:19:38 | INFO | Train Epoch: 5 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 149.696/s, 149.696/s/gpu LR: 0.000187 Logit Scale: 24.750 Contrastive_loss: 0.28148 (0.32568) Loss: 0.28148 (0.32568) 2025-03-19,02:19:59 | INFO | Train Epoch: 5 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 148.793/s, 148.793/s/gpu LR: 0.000187 Logit Scale: 24.735 Contrastive_loss: 0.23392 (0.32453) Loss: 0.23392 (0.32453) 2025-03-19,02:20:21 | INFO | Train Epoch: 5 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 151.655/s, 151.655/s/gpu LR: 0.000187 Logit Scale: 24.752 Contrastive_loss: 0.24422 (0.32354) Loss: 0.24422 (0.32354) 2025-03-19,02:20:42 | INFO | Train Epoch: 5 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 149.659/s, 149.659/s/gpu LR: 0.000187 Logit Scale: 24.744 Contrastive_loss: 0.17934 (0.32178) Loss: 0.17934 (0.32178) 2025-03-19,02:21:04 | INFO | Train Epoch: 5 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 148.752/s, 148.752/s/gpu LR: 0.000187 Logit Scale: 24.718 Contrastive_loss: 0.76615 (0.32713) Loss: 0.76615 (0.32713) 2025-03-19,02:21:25 | INFO | Train Epoch: 5 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 149.267/s, 149.267/s/gpu LR: 0.000187 Logit Scale: 24.695 Contrastive_loss: 0.26718 (0.32642) Loss: 0.26718 (0.32642) 2025-03-19,02:21:46 | INFO | Train Epoch: 5 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 149.039/s, 149.039/s/gpu LR: 0.000187 Logit Scale: 24.727 Contrastive_loss: 0.36209 (0.32684) Loss: 0.36209 (0.32684) 2025-03-19,02:22:08 | INFO | Train Epoch: 5 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.594/s, 149.594/s/gpu LR: 0.000187 Logit Scale: 24.687 Contrastive_loss: 0.40942 (0.32780) Loss: 0.40942 (0.32780) 2025-03-19,02:22:29 | INFO | Train Epoch: 5 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.704/s, 149.704/s/gpu LR: 0.000187 Logit Scale: 24.662 Contrastive_loss: 0.43291 (0.32901) Loss: 0.43291 (0.32901) 2025-03-19,02:22:51 | INFO | Train Epoch: 5 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.691/s, 149.691/s/gpu LR: 0.000187 Logit Scale: 24.647 Contrastive_loss: 0.40171 (0.32983) Loss: 0.40171 (0.32983) 2025-03-19,02:23:12 | INFO | Train Epoch: 5 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 149.989/s, 149.989/s/gpu LR: 0.000186 Logit Scale: 24.673 Contrastive_loss: 0.48654 (0.33159) Loss: 0.48654 (0.33159) 2025-03-19,02:23:34 | INFO | Train Epoch: 5 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 150.784/s, 150.784/s/gpu LR: 0.000186 Logit Scale: 24.705 Contrastive_loss: 0.28714 (0.33110) Loss: 0.28714 (0.33110) 2025-03-19,02:23:55 | INFO | Train Epoch: 5 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.280/s, 149.280/s/gpu LR: 0.000186 Logit Scale: 24.692 Contrastive_loss: 0.30768 (0.33084) Loss: 0.30768 (0.33084) 2025-03-19,02:24:17 | INFO | Train Epoch: 5 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 147.415/s, 147.415/s/gpu LR: 0.000186 Logit Scale: 24.713 Contrastive_loss: 0.18777 (0.32929) Loss: 0.18777 (0.32929) 2025-03-19,02:24:38 | INFO | Train Epoch: 5 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.568/s, 148.568/s/gpu LR: 0.000186 Logit Scale: 24.723 Contrastive_loss: 0.044762 (0.32623) Loss: 0.044762 (0.32623) 2025-03-19,02:25:00 | INFO | Train Epoch: 5 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 148.623/s, 148.623/s/gpu LR: 0.000186 Logit Scale: 24.703 Contrastive_loss: 0.22924 (0.32520) Loss: 0.22924 (0.32520) 2025-03-19,02:25:21 | INFO | Train Epoch: 5 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 147.375/s, 147.375/s/gpu LR: 0.000186 Logit Scale: 24.766 Contrastive_loss: 0.23361 (0.32423) Loss: 0.23361 (0.32423) 2025-03-19,02:25:43 | INFO | Train Epoch: 5 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 149.149/s, 149.149/s/gpu LR: 0.000186 Logit Scale: 24.777 Contrastive_loss: 0.41913 (0.32522) Loss: 0.41913 (0.32522) 2025-03-19,02:26:05 | INFO | Train Epoch: 5 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 174.156/s, 174.156/s/gpu LR: 0.000186 Logit Scale: 24.785 Contrastive_loss: 0.44170 (0.32642) Loss: 0.44170 (0.32642) 2025-03-19,02:26:26 | INFO | Train Epoch: 5 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.659/s, 149.659/s/gpu LR: 0.000186 Logit Scale: 24.772 Contrastive_loss: 0.42068 (0.32738) Loss: 0.42068 (0.32738) 2025-03-19,02:26:47 | INFO | Train Epoch: 5 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 149.006/s, 149.006/s/gpu LR: 0.000186 Logit Scale: 24.767 Contrastive_loss: 0.30744 (0.32718) Loss: 0.30744 (0.32718) 2025-03-19,02:27:09 | INFO | Train Epoch: 5 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 146.550/s, 146.550/s/gpu LR: 0.000186 Logit Scale: 24.764 Contrastive_loss: 0.42363 (0.32815) Loss: 0.42363 (0.32815) 2025-03-19,02:27:31 | INFO | Train Epoch: 5 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.221, 143.575/s, 143.575/s/gpu LR: 0.000186 Logit Scale: 24.795 Contrastive_loss: 0.26595 (0.32753) Loss: 0.26595 (0.32753) 2025-03-19,02:27:53 | INFO | Train Epoch: 5 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.217, 147.196/s, 147.196/s/gpu LR: 0.000186 Logit Scale: 24.800 Contrastive_loss: 0.090816 (0.32521) Loss: 0.090816 (0.32521) 2025-03-19,02:28:15 | INFO | Train Epoch: 5 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 150.334/s, 150.334/s/gpu LR: 0.000186 Logit Scale: 24.789 Contrastive_loss: 0.17396 (0.32374) Loss: 0.17396 (0.32374) 2025-03-19,02:28:36 | INFO | Train Epoch: 5 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.272/s, 149.272/s/gpu LR: 0.000186 Logit Scale: 24.764 Contrastive_loss: 0.20971 (0.32264) Loss: 0.20971 (0.32264) 2025-03-19,02:28:58 | INFO | Train Epoch: 5 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 148.010/s, 148.010/s/gpu LR: 0.000186 Logit Scale: 24.763 Contrastive_loss: 0.057120 (0.32012) Loss: 0.057120 (0.32012) 2025-03-19,02:29:20 | INFO | Train Epoch: 5 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.219, 147.413/s, 147.413/s/gpu LR: 0.000186 Logit Scale: 24.765 Contrastive_loss: 0.28260 (0.31976) Loss: 0.28260 (0.31976) 2025-03-19,02:29:42 | INFO | Train Epoch: 5 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.218, 148.200/s, 148.200/s/gpu LR: 0.000186 Logit Scale: 24.787 Contrastive_loss: 0.26805 (0.31928) Loss: 0.26805 (0.31928) 2025-03-19,02:30:03 | INFO | Train Epoch: 5 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 148.587/s, 148.587/s/gpu LR: 0.000186 Logit Scale: 24.795 Contrastive_loss: 0.15438 (0.31775) Loss: 0.15438 (0.31775) 2025-03-19,02:30:25 | INFO | Train Epoch: 5 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.217, 148.220/s, 148.220/s/gpu LR: 0.000186 Logit Scale: 24.803 Contrastive_loss: 0.28835 (0.31748) Loss: 0.28835 (0.31748) 2025-03-19,02:30:47 | INFO | Train Epoch: 5 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.336/s, 149.336/s/gpu LR: 0.000186 Logit Scale: 24.842 Contrastive_loss: 0.37644 (0.31802) Loss: 0.37644 (0.31802) 2025-03-19,02:31:08 | INFO | Train Epoch: 5 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 145.829/s, 145.829/s/gpu LR: 0.000186 Logit Scale: 24.844 Contrastive_loss: 0.33141 (0.31814) Loss: 0.33141 (0.31814) 2025-03-19,02:31:30 | INFO | Train Epoch: 5 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 152.587/s, 152.587/s/gpu LR: 0.000186 Logit Scale: 24.870 Contrastive_loss: 0.55791 (0.32028) Loss: 0.55791 (0.32028) 2025-03-19,02:31:51 | INFO | Train Epoch: 5 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 145.921/s, 145.921/s/gpu LR: 0.000186 Logit Scale: 24.862 Contrastive_loss: 0.36988 (0.32072) Loss: 0.36988 (0.32072) 2025-03-19,02:32:13 | INFO | Train Epoch: 5 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 139.245/s, 139.245/s/gpu LR: 0.000186 Logit Scale: 24.825 Contrastive_loss: 0.14227 (0.31915) Loss: 0.14227 (0.31915) 2025-03-19,02:32:35 | INFO | Train Epoch: 5 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 147.118/s, 147.118/s/gpu LR: 0.000186 Logit Scale: 24.818 Contrastive_loss: 0.43251 (0.32014) Loss: 0.43251 (0.32014) 2025-03-19,02:32:57 | INFO | Train Epoch: 5 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 145.502/s, 145.502/s/gpu LR: 0.000186 Logit Scale: 24.780 Contrastive_loss: 0.16150 (0.31877) Loss: 0.16150 (0.31877) 2025-03-19,02:33:19 | INFO | Train Epoch: 5 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.219, 144.788/s, 144.788/s/gpu LR: 0.000186 Logit Scale: 24.797 Contrastive_loss: 0.14316 (0.31727) Loss: 0.14316 (0.31727) 2025-03-19,02:33:41 | INFO | Train Epoch: 5 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.221, 145.636/s, 145.636/s/gpu LR: 0.000186 Logit Scale: 24.801 Contrastive_loss: 0.25989 (0.31678) Loss: 0.25989 (0.31678) 2025-03-19,02:34:03 | INFO | Train Epoch: 5 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.221, 141.620/s, 141.620/s/gpu LR: 0.000186 Logit Scale: 24.785 Contrastive_loss: 0.50154 (0.31834) Loss: 0.50154 (0.31834) 2025-03-19,02:34:25 | INFO | Train Epoch: 5 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.221, 145.264/s, 145.264/s/gpu LR: 0.000186 Logit Scale: 24.742 Contrastive_loss: 0.10354 (0.31655) Loss: 0.10354 (0.31655) 2025-03-19,02:34:47 | INFO | Train Epoch: 5 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.221, 145.558/s, 145.558/s/gpu LR: 0.000186 Logit Scale: 24.808 Contrastive_loss: 0.15427 (0.31521) Loss: 0.15427 (0.31521) 2025-03-19,02:35:09 | INFO | Train Epoch: 5 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 149.223/s, 149.223/s/gpu LR: 0.000186 Logit Scale: 24.828 Contrastive_loss: 0.20835 (0.31433) Loss: 0.20835 (0.31433) 2025-03-19,02:35:30 | INFO | Train Epoch: 5 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 148.727/s, 148.727/s/gpu LR: 0.000186 Logit Scale: 24.831 Contrastive_loss: 0.058601 (0.31225) Loss: 0.058601 (0.31225) 2025-03-19,02:35:52 | INFO | Train Epoch: 5 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 148.074/s, 148.074/s/gpu LR: 0.000186 Logit Scale: 24.839 Contrastive_loss: 0.30495 (0.31219) Loss: 0.30495 (0.31219) 2025-03-19,02:36:13 | INFO | Train Epoch: 5 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.294/s, 148.294/s/gpu LR: 0.000186 Logit Scale: 24.811 Contrastive_loss: 0.35643 (0.31255) Loss: 0.35643 (0.31255) 2025-03-19,02:36:35 | INFO | Train Epoch: 5 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.686/s, 148.686/s/gpu LR: 0.000186 Logit Scale: 24.859 Contrastive_loss: 0.58974 (0.31475) Loss: 0.58974 (0.31475) 2025-03-19,02:36:57 | INFO | Train Epoch: 5 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.045/s, 148.045/s/gpu LR: 0.000186 Logit Scale: 24.832 Contrastive_loss: 0.079415 (0.31289) Loss: 0.079415 (0.31289) 2025-03-19,02:37:18 | INFO | Train Epoch: 5 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 147.757/s, 147.757/s/gpu LR: 0.000186 Logit Scale: 24.825 Contrastive_loss: 0.15044 (0.31162) Loss: 0.15044 (0.31162) 2025-03-19,02:37:40 | INFO | Train Epoch: 5 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 148.537/s, 148.537/s/gpu LR: 0.000186 Logit Scale: 24.790 Contrastive_loss: 0.47536 (0.31289) Loss: 0.47536 (0.31289) 2025-03-19,02:38:02 | INFO | Train Epoch: 5 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.217, 149.234/s, 149.234/s/gpu LR: 0.000186 Logit Scale: 24.830 Contrastive_loss: 0.17158 (0.31181) Loss: 0.17158 (0.31181) 2025-03-19,02:38:23 | INFO | Train Epoch: 5 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 147.706/s, 147.706/s/gpu LR: 0.000186 Logit Scale: 24.787 Contrastive_loss: 0.36993 (0.31225) Loss: 0.36993 (0.31225) 2025-03-19,02:38:45 | INFO | Train Epoch: 5 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 149.422/s, 149.422/s/gpu LR: 0.000186 Logit Scale: 24.810 Contrastive_loss: 0.39415 (0.31287) Loss: 0.39415 (0.31287) 2025-03-19,02:39:06 | INFO | Train Epoch: 5 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 149.362/s, 149.362/s/gpu LR: 0.000186 Logit Scale: 24.857 Contrastive_loss: 0.13058 (0.31150) Loss: 0.13058 (0.31150) 2025-03-19,02:39:28 | INFO | Train Epoch: 5 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 147.948/s, 147.948/s/gpu LR: 0.000185 Logit Scale: 24.843 Contrastive_loss: 0.28927 (0.31133) Loss: 0.28927 (0.31133) 2025-03-19,02:39:50 | INFO | Train Epoch: 5 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 145.629/s, 145.629/s/gpu LR: 0.000185 Logit Scale: 24.800 Contrastive_loss: 0.44588 (0.31233) Loss: 0.44588 (0.31233) 2025-03-19,02:40:12 | INFO | Train Epoch: 5 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.222, 142.531/s, 142.531/s/gpu LR: 0.000185 Logit Scale: 24.808 Contrastive_loss: 0.30767 (0.31230) Loss: 0.30767 (0.31230) 2025-03-19,02:40:34 | INFO | Train Epoch: 5 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.221, 146.236/s, 146.236/s/gpu LR: 0.000185 Logit Scale: 24.830 Contrastive_loss: 0.19044 (0.31141) Loss: 0.19044 (0.31141) 2025-03-19,02:40:56 | INFO | Train Epoch: 5 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.220, 147.027/s, 147.027/s/gpu LR: 0.000185 Logit Scale: 24.841 Contrastive_loss: 0.025571 (0.30934) Loss: 0.025571 (0.30934) 2025-03-19,02:41:18 | INFO | Train Epoch: 5 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.219, 148.705/s, 148.705/s/gpu LR: 0.000185 Logit Scale: 24.856 Contrastive_loss: 0.093417 (0.30778) Loss: 0.093417 (0.30778) 2025-03-19,02:41:40 | INFO | Train Epoch: 5 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 148.089/s, 148.089/s/gpu LR: 0.000185 Logit Scale: 24.795 Contrastive_loss: 0.087213 (0.30621) Loss: 0.087213 (0.30621) 2025-03-19,02:42:01 | INFO | Train Epoch: 5 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.218, 148.260/s, 148.260/s/gpu LR: 0.000185 Logit Scale: 24.839 Contrastive_loss: 0.085649 (0.30464) Loss: 0.085649 (0.30464) 2025-03-19,02:42:23 | INFO | Train Epoch: 5 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 147.454/s, 147.454/s/gpu LR: 0.000185 Logit Scale: 24.792 Contrastive_loss: 0.15998 (0.30362) Loss: 0.15998 (0.30362) 2025-03-19,02:42:45 | INFO | Train Epoch: 5 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.218, 148.221/s, 148.221/s/gpu LR: 0.000185 Logit Scale: 24.805 Contrastive_loss: 0.31257 (0.30369) Loss: 0.31257 (0.30369) 2025-03-19,02:43:06 | INFO | Train Epoch: 5 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 147.759/s, 147.759/s/gpu LR: 0.000185 Logit Scale: 24.832 Contrastive_loss: 0.53963 (0.30532) Loss: 0.53963 (0.30532) 2025-03-19,02:43:28 | INFO | Train Epoch: 5 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 145.240/s, 145.240/s/gpu LR: 0.000185 Logit Scale: 24.809 Contrastive_loss: 0.17171 (0.30440) Loss: 0.17171 (0.30440) 2025-03-19,02:43:50 | INFO | Train Epoch: 5 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.218, 147.537/s, 147.537/s/gpu LR: 0.000185 Logit Scale: 24.871 Contrastive_loss: 0.52585 (0.30592) Loss: 0.52585 (0.30592) 2025-03-19,02:44:12 | INFO | Train Epoch: 5 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.163/s, 148.163/s/gpu LR: 0.000185 Logit Scale: 24.840 Contrastive_loss: 0.39215 (0.30651) Loss: 0.39215 (0.30651) 2025-03-19,02:44:33 | INFO | Train Epoch: 5 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.668/s, 148.668/s/gpu LR: 0.000185 Logit Scale: 24.848 Contrastive_loss: 0.19929 (0.30578) Loss: 0.19929 (0.30578) 2025-03-19,02:44:55 | INFO | Train Epoch: 5 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 147.911/s, 147.911/s/gpu LR: 0.000185 Logit Scale: 24.815 Contrastive_loss: 0.28279 (0.30563) Loss: 0.28279 (0.30563) 2025-03-19,02:45:16 | INFO | Train Epoch: 5 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 148.627/s, 148.627/s/gpu LR: 0.000185 Logit Scale: 24.832 Contrastive_loss: 0.13269 (0.30447) Loss: 0.13269 (0.30447) 2025-03-19,02:45:38 | INFO | Train Epoch: 5 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.219, 146.120/s, 146.120/s/gpu LR: 0.000185 Logit Scale: 24.835 Contrastive_loss: 0.12112 (0.30326) Loss: 0.12112 (0.30326) 2025-03-19,02:46:00 | INFO | Train Epoch: 5 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.220, 145.575/s, 145.575/s/gpu LR: 0.000185 Logit Scale: 24.856 Contrastive_loss: 0.20704 (0.30263) Loss: 0.20704 (0.30263) 2025-03-19,02:46:22 | INFO | Train Epoch: 5 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.221, 144.268/s, 144.268/s/gpu LR: 0.000185 Logit Scale: 24.787 Contrastive_loss: 0.48500 (0.30382) Loss: 0.48500 (0.30382) 2025-03-19,02:46:45 | INFO | Train Epoch: 5 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.222, 144.176/s, 144.176/s/gpu LR: 0.000185 Logit Scale: 24.800 Contrastive_loss: 0.49510 (0.30506) Loss: 0.49510 (0.30506) 2025-03-19,02:47:07 | INFO | Train Epoch: 5 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.220, 146.645/s, 146.645/s/gpu LR: 0.000185 Logit Scale: 24.821 Contrastive_loss: 0.32968 (0.30522) Loss: 0.32968 (0.30522) 2025-03-19,02:47:29 | INFO | Train Epoch: 5 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.220, 146.680/s, 146.680/s/gpu LR: 0.000185 Logit Scale: 24.816 Contrastive_loss: 0.15525 (0.30426) Loss: 0.15525 (0.30426) 2025-03-19,02:47:51 | INFO | Train Epoch: 5 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.221, 145.783/s, 145.783/s/gpu LR: 0.000185 Logit Scale: 24.819 Contrastive_loss: 0.50302 (0.30552) Loss: 0.50302 (0.30552) 2025-03-19,02:48:13 | INFO | Train Epoch: 5 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.220, 144.280/s, 144.280/s/gpu LR: 0.000185 Logit Scale: 24.811 Contrastive_loss: 0.27362 (0.30532) Loss: 0.27362 (0.30532) 2025-03-19,02:48:35 | INFO | Train Epoch: 5 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.221, 146.182/s, 146.182/s/gpu LR: 0.000185 Logit Scale: 24.841 Contrastive_loss: 0.16792 (0.30446) Loss: 0.16792 (0.30446) 2025-03-19,02:48:57 | INFO | Train Epoch: 5 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.220, 145.673/s, 145.673/s/gpu LR: 0.000185 Logit Scale: 24.850 Contrastive_loss: 0.17607 (0.30366) Loss: 0.17607 (0.30366) 2025-03-19,02:49:19 | INFO | Train Epoch: 5 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.222, 144.130/s, 144.130/s/gpu LR: 0.000185 Logit Scale: 24.848 Contrastive_loss: 0.27591 (0.30348) Loss: 0.27591 (0.30348) 2025-03-19,02:49:41 | INFO | Train Epoch: 5 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.220, 146.600/s, 146.600/s/gpu LR: 0.000185 Logit Scale: 24.822 Contrastive_loss: 0.054679 (0.30195) Loss: 0.054679 (0.30195) 2025-03-19,02:50:03 | INFO | Train Epoch: 5 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 147.422/s, 147.422/s/gpu LR: 0.000185 Logit Scale: 24.802 Contrastive_loss: 0.23593 (0.30154) Loss: 0.23593 (0.30154) 2025-03-19,02:50:24 | INFO | Train Epoch: 5 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 146.058/s, 146.058/s/gpu LR: 0.000185 Logit Scale: 24.849 Contrastive_loss: 0.62594 (0.30352) Loss: 0.62594 (0.30352) 2025-03-19,02:50:46 | INFO | Train Epoch: 5 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 146.383/s, 146.383/s/gpu LR: 0.000185 Logit Scale: 24.870 Contrastive_loss: 0.21749 (0.30300) Loss: 0.21749 (0.30300) 2025-03-19,02:51:08 | INFO | Train Epoch: 5 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 146.865/s, 146.865/s/gpu LR: 0.000185 Logit Scale: 24.874 Contrastive_loss: 0.16639 (0.30218) Loss: 0.16639 (0.30218) 2025-03-19,02:51:30 | INFO | Train Epoch: 5 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 143.715/s, 143.715/s/gpu LR: 0.000185 Logit Scale: 24.879 Contrastive_loss: 0.26927 (0.30198) Loss: 0.26927 (0.30198) 2025-03-19,02:51:52 | INFO | Train Epoch: 5 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.220, 147.145/s, 147.145/s/gpu LR: 0.000185 Logit Scale: 24.910 Contrastive_loss: 0.19656 (0.30135) Loss: 0.19656 (0.30135) 2025-03-19,02:52:14 | INFO | Train Epoch: 5 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.219, 146.616/s, 146.616/s/gpu LR: 0.000185 Logit Scale: 24.866 Contrastive_loss: 0.11896 (0.30027) Loss: 0.11896 (0.30027) 2025-03-19,02:52:36 | INFO | Train Epoch: 5 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 143.145/s, 143.145/s/gpu LR: 0.000185 Logit Scale: 24.840 Contrastive_loss: 0.38830 (0.30079) Loss: 0.38830 (0.30079) 2025-03-19,02:52:58 | INFO | Train Epoch: 5 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.221, 144.678/s, 144.678/s/gpu LR: 0.000185 Logit Scale: 24.827 Contrastive_loss: 0.18597 (0.30012) Loss: 0.18597 (0.30012) 2025-03-19,02:53:20 | INFO | Train Epoch: 5 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 147.024/s, 147.024/s/gpu LR: 0.000185 Logit Scale: 24.800 Contrastive_loss: 0.16831 (0.29935) Loss: 0.16831 (0.29935) 2025-03-19,02:53:42 | INFO | Train Epoch: 5 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 145.727/s, 145.727/s/gpu LR: 0.000185 Logit Scale: 24.852 Contrastive_loss: 0.45590 (0.30026) Loss: 0.45590 (0.30026) 2025-03-19,02:54:04 | INFO | Train Epoch: 5 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 147.828/s, 147.828/s/gpu LR: 0.000185 Logit Scale: 24.849 Contrastive_loss: 0.26531 (0.30006) Loss: 0.26531 (0.30006) 2025-03-19,02:54:25 | INFO | Train Epoch: 5 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.214, 148.089/s, 148.089/s/gpu LR: 0.000185 Logit Scale: 24.830 Contrastive_loss: 0.38975 (0.30057) Loss: 0.38975 (0.30057) 2025-03-19,02:54:47 | INFO | Train Epoch: 5 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 148.765/s, 148.765/s/gpu LR: 0.000184 Logit Scale: 24.876 Contrastive_loss: 0.32986 (0.30074) Loss: 0.32986 (0.30074) 2025-03-19,02:55:09 | INFO | Train Epoch: 5 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 145.828/s, 145.828/s/gpu LR: 0.000184 Logit Scale: 24.852 Contrastive_loss: 0.25733 (0.30049) Loss: 0.25733 (0.30049) 2025-03-19,02:55:31 | INFO | Train Epoch: 5 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 146.154/s, 146.154/s/gpu LR: 0.000184 Logit Scale: 24.842 Contrastive_loss: 0.79731 (0.30328) Loss: 0.79731 (0.30328) 2025-03-19,02:55:53 | INFO | Train Epoch: 5 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 144.428/s, 144.428/s/gpu LR: 0.000184 Logit Scale: 24.866 Contrastive_loss: 0.17547 (0.30257) Loss: 0.17547 (0.30257) 2025-03-19,02:56:15 | INFO | Train Epoch: 5 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.218, 148.034/s, 148.034/s/gpu LR: 0.000184 Logit Scale: 24.876 Contrastive_loss: 0.094931 (0.30141) Loss: 0.094931 (0.30141) 2025-03-19,02:56:37 | INFO | Train Epoch: 5 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.221, 143.309/s, 143.309/s/gpu LR: 0.000184 Logit Scale: 24.856 Contrastive_loss: 0.24350 (0.30109) Loss: 0.24350 (0.30109) 2025-03-19,02:56:59 | INFO | Train Epoch: 5 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 149.854/s, 149.854/s/gpu LR: 0.000184 Logit Scale: 24.858 Contrastive_loss: 0.59934 (0.30273) Loss: 0.59934 (0.30273) 2025-03-19,02:57:20 | INFO | Train Epoch: 5 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.757/s, 148.757/s/gpu LR: 0.000184 Logit Scale: 24.859 Contrastive_loss: 0.16797 (0.30200) Loss: 0.16797 (0.30200) 2025-03-19,02:57:42 | INFO | Train Epoch: 5 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.399/s, 148.399/s/gpu LR: 0.000184 Logit Scale: 24.879 Contrastive_loss: 0.26352 (0.30179) Loss: 0.26352 (0.30179) 2025-03-19,02:58:03 | INFO | Train Epoch: 5 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.217, 152.363/s, 152.363/s/gpu LR: 0.000184 Logit Scale: 24.873 Contrastive_loss: 0.10353 (0.30072) Loss: 0.10353 (0.30072) 2025-03-19,02:58:24 | INFO | Train Epoch: 5 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.212, 149.310/s, 149.310/s/gpu LR: 0.000184 Logit Scale: 24.853 Contrastive_loss: 0.10305 (0.29965) Loss: 0.10305 (0.29965) 2025-03-19,02:58:46 | INFO | Train Epoch: 5 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 151.236/s, 151.236/s/gpu LR: 0.000184 Logit Scale: 24.881 Contrastive_loss: 0.43209 (0.30036) Loss: 0.43209 (0.30036) 2025-03-19,02:59:07 | INFO | Train Epoch: 5 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.213, 149.706/s, 149.706/s/gpu LR: 0.000184 Logit Scale: 24.881 Contrastive_loss: 0.27772 (0.30024) Loss: 0.27772 (0.30024) 2025-03-19,02:59:29 | INFO | Train Epoch: 5 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.551/s, 148.551/s/gpu LR: 0.000184 Logit Scale: 24.841 Contrastive_loss: 0.10771 (0.29922) Loss: 0.10771 (0.29922) 2025-03-19,02:59:51 | INFO | Train Epoch: 5 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.217, 148.045/s, 148.045/s/gpu LR: 0.000184 Logit Scale: 24.825 Contrastive_loss: 0.73535 (0.30152) Loss: 0.73535 (0.30152) 2025-03-19,03:00:12 | INFO | Train Epoch: 5 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.217, 148.934/s, 148.934/s/gpu LR: 0.000184 Logit Scale: 24.830 Contrastive_loss: 0.31944 (0.30161) Loss: 0.31944 (0.30161) 2025-03-19,03:00:34 | INFO | Train Epoch: 5 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.219, 144.455/s, 144.455/s/gpu LR: 0.000184 Logit Scale: 24.853 Contrastive_loss: 0.28735 (0.30154) Loss: 0.28735 (0.30154) 2025-03-19,03:00:57 | INFO | Train Epoch: 5 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.223, 145.594/s, 145.594/s/gpu LR: 0.000184 Logit Scale: 24.859 Contrastive_loss: 0.41443 (0.30212) Loss: 0.41443 (0.30212) 2025-03-19,03:01:19 | INFO | Train Epoch: 5 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.221, 145.327/s, 145.327/s/gpu LR: 0.000184 Logit Scale: 24.864 Contrastive_loss: 0.49119 (0.30310) Loss: 0.49119 (0.30310) 2025-03-19,03:01:41 | INFO | Train Epoch: 5 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.221, 146.066/s, 146.066/s/gpu LR: 0.000184 Logit Scale: 24.891 Contrastive_loss: 0.16793 (0.30240) Loss: 0.16793 (0.30240) 2025-03-19,03:02:03 | INFO | Train Epoch: 5 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 146.912/s, 146.912/s/gpu LR: 0.000184 Logit Scale: 24.816 Contrastive_loss: 0.26569 (0.30222) Loss: 0.26569 (0.30222) 2025-03-19,03:02:25 | INFO | Train Epoch: 5 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.221, 141.401/s, 141.401/s/gpu LR: 0.000184 Logit Scale: 24.885 Contrastive_loss: 0.075143 (0.30106) Loss: 0.075143 (0.30106) 2025-03-19,03:02:47 | INFO | Train Epoch: 5 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.224, 143.912/s, 143.912/s/gpu LR: 0.000184 Logit Scale: 24.861 Contrastive_loss: 0.62762 (0.30271) Loss: 0.62762 (0.30271) 2025-03-19,03:03:09 | INFO | Train Epoch: 5 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.219, 146.730/s, 146.730/s/gpu LR: 0.000184 Logit Scale: 24.866 Contrastive_loss: 0.071941 (0.30155) Loss: 0.071941 (0.30155) 2025-03-19,03:03:31 | INFO | Train Epoch: 5 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.219, 147.104/s, 147.104/s/gpu LR: 0.000184 Logit Scale: 24.878 Contrastive_loss: 0.16115 (0.30085) Loss: 0.16115 (0.30085) 2025-03-19,03:03:53 | INFO | Train Epoch: 5 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.219, 145.971/s, 145.971/s/gpu LR: 0.000184 Logit Scale: 24.915 Contrastive_loss: 0.14069 (0.30005) Loss: 0.14069 (0.30005) 2025-03-19,03:04:15 | INFO | Train Epoch: 5 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.221, 147.182/s, 147.182/s/gpu LR: 0.000184 Logit Scale: 24.911 Contrastive_loss: 0.23450 (0.29973) Loss: 0.23450 (0.29973) 2025-03-19,03:04:37 | INFO | Train Epoch: 5 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.220, 147.309/s, 147.309/s/gpu LR: 0.000184 Logit Scale: 24.882 Contrastive_loss: 0.31494 (0.29981) Loss: 0.31494 (0.29981) 2025-03-19,03:04:59 | INFO | Train Epoch: 5 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.220, 145.786/s, 145.786/s/gpu LR: 0.000184 Logit Scale: 24.885 Contrastive_loss: 0.24852 (0.29955) Loss: 0.24852 (0.29955) 2025-03-19,03:05:20 | INFO | Train Epoch: 5 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.213, 152.172/s, 152.172/s/gpu LR: 0.000184 Logit Scale: 24.893 Contrastive_loss: 0.28921 (0.29950) Loss: 0.28921 (0.29950) 2025-03-19,03:05:41 | INFO | Train Epoch: 5 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 147.821/s, 147.821/s/gpu LR: 0.000184 Logit Scale: 24.891 Contrastive_loss: 0.43772 (0.30017) Loss: 0.43772 (0.30017) 2025-03-19,03:06:03 | INFO | Train Epoch: 5 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.218, 147.997/s, 147.997/s/gpu LR: 0.000184 Logit Scale: 24.902 Contrastive_loss: 0.41319 (0.30072) Loss: 0.41319 (0.30072) 2025-03-19,03:06:25 | INFO | Train Epoch: 5 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 150.651/s, 150.651/s/gpu LR: 0.000184 Logit Scale: 24.906 Contrastive_loss: 0.25276 (0.30049) Loss: 0.25276 (0.30049) 2025-03-19,03:06:47 | INFO | Train Epoch: 5 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.219, 143.252/s, 143.252/s/gpu LR: 0.000184 Logit Scale: 24.870 Contrastive_loss: 0.41629 (0.30104) Loss: 0.41629 (0.30104) 2025-03-19,03:07:09 | INFO | Train Epoch: 5 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.222, 144.789/s, 144.789/s/gpu LR: 0.000184 Logit Scale: 24.848 Contrastive_loss: 0.15586 (0.30035) Loss: 0.15586 (0.30035) 2025-03-19,03:07:31 | INFO | Train Epoch: 5 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.221, 146.203/s, 146.203/s/gpu LR: 0.000184 Logit Scale: 24.870 Contrastive_loss: 0.19034 (0.29983) Loss: 0.19034 (0.29983) 2025-03-19,03:07:53 | INFO | Train Epoch: 5 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.218, 147.514/s, 147.514/s/gpu LR: 0.000184 Logit Scale: 24.889 Contrastive_loss: 0.28104 (0.29974) Loss: 0.28104 (0.29974) 2025-03-19,03:08:15 | INFO | Train Epoch: 5 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 143.349/s, 143.349/s/gpu LR: 0.000184 Logit Scale: 24.913 Contrastive_loss: 0.25866 (0.29955) Loss: 0.25866 (0.29955) 2025-03-19,03:08:37 | INFO | Train Epoch: 5 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 150.277/s, 150.277/s/gpu LR: 0.000184 Logit Scale: 24.874 Contrastive_loss: 0.14233 (0.29881) Loss: 0.14233 (0.29881) 2025-03-19,03:08:58 | INFO | Train Epoch: 5 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 147.709/s, 147.709/s/gpu LR: 0.000184 Logit Scale: 24.869 Contrastive_loss: 0.71303 (0.30074) Loss: 0.71303 (0.30074) 2025-03-19,03:09:20 | INFO | Train Epoch: 5 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.219, 145.417/s, 145.417/s/gpu LR: 0.000184 Logit Scale: 24.848 Contrastive_loss: 0.28235 (0.30066) Loss: 0.28235 (0.30066) 2025-03-19,03:09:42 | INFO | Train Epoch: 5 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.221, 146.811/s, 146.811/s/gpu LR: 0.000184 Logit Scale: 24.869 Contrastive_loss: 0.27347 (0.30053) Loss: 0.27347 (0.30053) 2025-03-19,03:10:04 | INFO | Train Epoch: 5 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.219, 146.462/s, 146.462/s/gpu LR: 0.000183 Logit Scale: 24.877 Contrastive_loss: 0.066490 (0.29946) Loss: 0.066490 (0.29946) 2025-03-19,03:10:26 | INFO | Train Epoch: 5 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.221, 146.181/s, 146.181/s/gpu LR: 0.000183 Logit Scale: 24.875 Contrastive_loss: 0.13841 (0.29872) Loss: 0.13841 (0.29872) 2025-03-19,03:10:48 | INFO | Train Epoch: 5 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.221, 146.058/s, 146.058/s/gpu LR: 0.000183 Logit Scale: 24.872 Contrastive_loss: 0.66246 (0.30038) Loss: 0.66246 (0.30038) 2025-03-19,03:11:10 | INFO | Train Epoch: 5 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 149.955/s, 149.955/s/gpu LR: 0.000183 Logit Scale: 24.860 Contrastive_loss: 0.19491 (0.29990) Loss: 0.19491 (0.29990) 2025-03-19,03:11:31 | INFO | Train Epoch: 5 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.213, 152.215/s, 152.215/s/gpu LR: 0.000183 Logit Scale: 24.862 Contrastive_loss: 0.21067 (0.29950) Loss: 0.21067 (0.29950) 2025-03-19,03:11:52 | INFO | Train Epoch: 5 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.211, 151.795/s, 151.795/s/gpu LR: 0.000183 Logit Scale: 24.860 Contrastive_loss: 0.24616 (0.29926) Loss: 0.24616 (0.29926) 2025-03-19,03:12:13 | INFO | Train Epoch: 5 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.211, 151.881/s, 151.881/s/gpu LR: 0.000183 Logit Scale: 24.885 Contrastive_loss: 0.35481 (0.29950) Loss: 0.35481 (0.29950) 2025-03-19,03:12:35 | INFO | Train Epoch: 5 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 150.132/s, 150.132/s/gpu LR: 0.000183 Logit Scale: 24.873 Contrastive_loss: 0.30728 (0.29954) Loss: 0.30728 (0.29954) 2025-03-19,03:12:56 | INFO | Train Epoch: 5 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 150.597/s, 150.597/s/gpu LR: 0.000183 Logit Scale: 24.888 Contrastive_loss: 0.071993 (0.29853) Loss: 0.071993 (0.29853) 2025-03-19,03:13:18 | INFO | Train Epoch: 5 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 145.091/s, 145.091/s/gpu LR: 0.000183 Logit Scale: 24.882 Contrastive_loss: 0.56744 (0.29972) Loss: 0.56744 (0.29972) 2025-03-19,03:13:40 | INFO | Train Epoch: 5 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.221, 145.590/s, 145.590/s/gpu LR: 0.000183 Logit Scale: 24.881 Contrastive_loss: 0.070609 (0.29871) Loss: 0.070609 (0.29871) 2025-03-19,03:14:02 | INFO | Train Epoch: 5 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.220, 148.965/s, 148.965/s/gpu LR: 0.000183 Logit Scale: 24.850 Contrastive_loss: 0.30694 (0.29875) Loss: 0.30694 (0.29875) 2025-03-19,03:14:24 | INFO | Train Epoch: 5 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.220, 145.199/s, 145.199/s/gpu LR: 0.000183 Logit Scale: 24.856 Contrastive_loss: 0.089124 (0.29784) Loss: 0.089124 (0.29784) 2025-03-19,03:14:46 | INFO | Train Epoch: 5 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.220, 146.822/s, 146.822/s/gpu LR: 0.000183 Logit Scale: 24.855 Contrastive_loss: 0.14393 (0.29717) Loss: 0.14393 (0.29717) 2025-03-19,03:15:08 | INFO | Train Epoch: 5 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.220, 146.140/s, 146.140/s/gpu LR: 0.000183 Logit Scale: 24.808 Contrastive_loss: 0.38281 (0.29754) Loss: 0.38281 (0.29754) 2025-03-19,03:15:30 | INFO | Train Epoch: 5 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.219, 144.610/s, 144.610/s/gpu LR: 0.000183 Logit Scale: 24.859 Contrastive_loss: 0.57552 (0.29873) Loss: 0.57552 (0.29873) 2025-03-19,03:15:52 | INFO | Train Epoch: 5 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.219, 145.666/s, 145.666/s/gpu LR: 0.000183 Logit Scale: 24.844 Contrastive_loss: 0.28576 (0.29868) Loss: 0.28576 (0.29868) 2025-03-19,03:16:14 | INFO | Train Epoch: 5 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 148.836/s, 148.836/s/gpu LR: 0.000183 Logit Scale: 24.869 Contrastive_loss: 0.29805 (0.29867) Loss: 0.29805 (0.29867) 2025-03-19,03:16:35 | INFO | Train Epoch: 5 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.211, 152.028/s, 152.028/s/gpu LR: 0.000183 Logit Scale: 24.893 Contrastive_loss: 0.57561 (0.29985) Loss: 0.57561 (0.29985) 2025-03-19,03:16:57 | INFO | Train Epoch: 5 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 151.002/s, 151.002/s/gpu LR: 0.000183 Logit Scale: 24.868 Contrastive_loss: 0.16237 (0.29927) Loss: 0.16237 (0.29927) 2025-03-19,03:17:18 | INFO | Train Epoch: 5 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.494/s, 149.494/s/gpu LR: 0.000183 Logit Scale: 24.846 Contrastive_loss: 0.29279 (0.29924) Loss: 0.29279 (0.29924) 2025-03-19,03:17:40 | INFO | Train Epoch: 5 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 147.326/s, 147.326/s/gpu LR: 0.000183 Logit Scale: 24.849 Contrastive_loss: 0.54667 (0.30028) Loss: 0.54667 (0.30028) 2025-03-19,03:18:01 | INFO | Train Epoch: 5 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.215, 144.062/s, 144.062/s/gpu LR: 0.000183 Logit Scale: 24.814 Contrastive_loss: 0.19147 (0.29982) Loss: 0.19147 (0.29982) 2025-03-19,03:18:09 | INFO | Train Epoch: 5 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.220, 146.464/s, 146.464/s/gpu LR: 0.000183 Logit Scale: 24.799 Contrastive_loss: 0.17926 (0.29932) Loss: 0.17926 (0.29932) 2025-03-19,03:18:09 | INFO | Eval Epoch: 6 [32 / 7443] Clip Loss: 3.330933 2025-03-19,03:18:15 | INFO | Eval Epoch: 6 [3232 / 7443] Clip Loss: 0.950699 2025-03-19,03:18:21 | INFO | Eval Epoch: 6 [6432 / 7443] Clip Loss: 0.734670 2025-03-19,03:18:24 | INFO | Eval Epoch: 6 image_to_text_mean_rank: 110.7870 image_to_text_median_rank: 9.0000 image_to_text_R@1: 0.1106 image_to_text_R@5: 0.3849 image_to_text_R@10: 0.5543 text_to_image_mean_rank: 74.0846 text_to_image_median_rank: 8.0000 text_to_image_R@1: 0.1146 text_to_image_R@5: 0.3916 text_to_image_R@10: 0.5625 clip_val_loss: 0.6953 epoch: 6.0000 num_samples: 7443.0000 2025-03-19,03:18:56 | INFO | Start epoch 6 2025-03-19,03:18:57 | INFO | Train Epoch: 6 [ 32/766009 (0%)] Data (t): 0.177 Batch (t): 0.383, 83.5437/s, 83.5437/s/gpu LR: 0.000183 Logit Scale: 24.799 Contrastive_loss: 0.22249 (0.22249) Loss: 0.22249 (0.22249) 2025-03-19,03:19:18 | INFO | Train Epoch: 6 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 152.138/s, 152.138/s/gpu LR: 0.000183 Logit Scale: 24.870 Contrastive_loss: 0.41963 (0.32106) Loss: 0.41963 (0.32106) 2025-03-19,03:19:40 | INFO | Train Epoch: 6 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.435/s, 149.435/s/gpu LR: 0.000183 Logit Scale: 24.899 Contrastive_loss: 0.21001 (0.28404) Loss: 0.21001 (0.28404) 2025-03-19,03:20:01 | INFO | Train Epoch: 6 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 151.485/s, 151.485/s/gpu LR: 0.000183 Logit Scale: 24.891 Contrastive_loss: 0.17594 (0.25702) Loss: 0.17594 (0.25702) 2025-03-19,03:20:22 | INFO | Train Epoch: 6 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 150.660/s, 150.660/s/gpu LR: 0.000183 Logit Scale: 24.943 Contrastive_loss: 0.37059 (0.27973) Loss: 0.37059 (0.27973) 2025-03-19,03:20:44 | INFO | Train Epoch: 6 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.213, 151.164/s, 151.164/s/gpu LR: 0.000183 Logit Scale: 24.887 Contrastive_loss: 0.52852 (0.32120) Loss: 0.52852 (0.32120) 2025-03-19,03:21:05 | INFO | Train Epoch: 6 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 150.811/s, 150.811/s/gpu LR: 0.000183 Logit Scale: 24.949 Contrastive_loss: 0.15875 (0.29799) Loss: 0.15875 (0.29799) 2025-03-19,03:21:26 | INFO | Train Epoch: 6 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 151.408/s, 151.408/s/gpu LR: 0.000183 Logit Scale: 24.985 Contrastive_loss: 0.34896 (0.30436) Loss: 0.34896 (0.30436) 2025-03-19,03:21:47 | INFO | Train Epoch: 6 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 149.321/s, 149.321/s/gpu LR: 0.000183 Logit Scale: 24.988 Contrastive_loss: 0.36284 (0.31086) Loss: 0.36284 (0.31086) 2025-03-19,03:22:09 | INFO | Train Epoch: 6 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 146.974/s, 146.974/s/gpu LR: 0.000183 Logit Scale: 24.937 Contrastive_loss: 0.35827 (0.31560) Loss: 0.35827 (0.31560) 2025-03-19,03:22:31 | INFO | Train Epoch: 6 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 149.643/s, 149.643/s/gpu LR: 0.000183 Logit Scale: 24.970 Contrastive_loss: 0.17872 (0.30316) Loss: 0.17872 (0.30316) 2025-03-19,03:22:52 | INFO | Train Epoch: 6 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 151.294/s, 151.294/s/gpu LR: 0.000183 Logit Scale: 24.911 Contrastive_loss: 0.33990 (0.30622) Loss: 0.33990 (0.30622) 2025-03-19,03:23:13 | INFO | Train Epoch: 6 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 148.985/s, 148.985/s/gpu LR: 0.000183 Logit Scale: 24.942 Contrastive_loss: 0.16263 (0.29517) Loss: 0.16263 (0.29517) 2025-03-19,03:23:35 | INFO | Train Epoch: 6 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 149.498/s, 149.498/s/gpu LR: 0.000183 Logit Scale: 24.967 Contrastive_loss: 0.16532 (0.28590) Loss: 0.16532 (0.28590) 2025-03-19,03:23:57 | INFO | Train Epoch: 6 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.000/s, 148.000/s/gpu LR: 0.000183 Logit Scale: 24.974 Contrastive_loss: 0.44368 (0.29642) Loss: 0.44368 (0.29642) 2025-03-19,03:24:18 | INFO | Train Epoch: 6 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.217, 148.332/s, 148.332/s/gpu LR: 0.000183 Logit Scale: 24.961 Contrastive_loss: 0.17050 (0.28855) Loss: 0.17050 (0.28855) 2025-03-19,03:24:40 | INFO | Train Epoch: 6 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 148.629/s, 148.629/s/gpu LR: 0.000183 Logit Scale: 24.997 Contrastive_loss: 0.10863 (0.27796) Loss: 0.10863 (0.27796) 2025-03-19,03:25:02 | INFO | Train Epoch: 6 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 149.216/s, 149.216/s/gpu LR: 0.000183 Logit Scale: 24.991 Contrastive_loss: 0.26095 (0.27702) Loss: 0.26095 (0.27702) 2025-03-19,03:25:23 | INFO | Train Epoch: 6 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 145.280/s, 145.280/s/gpu LR: 0.000182 Logit Scale: 24.997 Contrastive_loss: 0.073850 (0.26632) Loss: 0.073850 (0.26632) 2025-03-19,03:25:45 | INFO | Train Epoch: 6 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 149.567/s, 149.567/s/gpu LR: 0.000182 Logit Scale: 25.012 Contrastive_loss: 0.12013 (0.25901) Loss: 0.12013 (0.25901) 2025-03-19,03:26:06 | INFO | Train Epoch: 6 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.552/s, 149.552/s/gpu LR: 0.000182 Logit Scale: 24.968 Contrastive_loss: 0.25847 (0.25899) Loss: 0.25847 (0.25899) 2025-03-19,03:26:28 | INFO | Train Epoch: 6 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.217, 148.756/s, 148.756/s/gpu LR: 0.000182 Logit Scale: 24.938 Contrastive_loss: 0.13335 (0.25328) Loss: 0.13335 (0.25328) 2025-03-19,03:26:50 | INFO | Train Epoch: 6 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 145.280/s, 145.280/s/gpu LR: 0.000182 Logit Scale: 24.949 Contrastive_loss: 0.50269 (0.26412) Loss: 0.50269 (0.26412) 2025-03-19,03:27:11 | INFO | Train Epoch: 6 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 150.118/s, 150.118/s/gpu LR: 0.000182 Logit Scale: 24.947 Contrastive_loss: 0.10279 (0.25740) Loss: 0.10279 (0.25740) 2025-03-19,03:27:33 | INFO | Train Epoch: 6 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.294/s, 149.294/s/gpu LR: 0.000182 Logit Scale: 24.943 Contrastive_loss: 0.23514 (0.25651) Loss: 0.23514 (0.25651) 2025-03-19,03:27:54 | INFO | Train Epoch: 6 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 146.149/s, 146.149/s/gpu LR: 0.000182 Logit Scale: 24.983 Contrastive_loss: 0.16836 (0.25312) Loss: 0.16836 (0.25312) 2025-03-19,03:28:16 | INFO | Train Epoch: 6 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.220, 144.248/s, 144.248/s/gpu LR: 0.000182 Logit Scale: 24.993 Contrastive_loss: 0.26271 (0.25347) Loss: 0.26271 (0.25347) 2025-03-19,03:28:38 | INFO | Train Epoch: 6 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.220, 143.049/s, 143.049/s/gpu LR: 0.000182 Logit Scale: 25.006 Contrastive_loss: 0.44666 (0.26037) Loss: 0.44666 (0.26037) 2025-03-19,03:29:00 | INFO | Train Epoch: 6 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.220, 145.854/s, 145.854/s/gpu LR: 0.000182 Logit Scale: 25.026 Contrastive_loss: 0.13846 (0.25617) Loss: 0.13846 (0.25617) 2025-03-19,03:29:22 | INFO | Train Epoch: 6 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.221, 146.485/s, 146.485/s/gpu LR: 0.000182 Logit Scale: 24.985 Contrastive_loss: 0.19652 (0.25418) Loss: 0.19652 (0.25418) 2025-03-19,03:29:44 | INFO | Train Epoch: 6 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.220, 145.976/s, 145.976/s/gpu LR: 0.000182 Logit Scale: 24.980 Contrastive_loss: 0.51509 (0.26260) Loss: 0.51509 (0.26260) 2025-03-19,03:30:06 | INFO | Train Epoch: 6 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 146.429/s, 146.429/s/gpu LR: 0.000182 Logit Scale: 24.929 Contrastive_loss: 0.35697 (0.26555) Loss: 0.35697 (0.26555) 2025-03-19,03:30:28 | INFO | Train Epoch: 6 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 149.631/s, 149.631/s/gpu LR: 0.000182 Logit Scale: 25.002 Contrastive_loss: 0.18802 (0.26320) Loss: 0.18802 (0.26320) 2025-03-19,03:30:49 | INFO | Train Epoch: 6 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 147.946/s, 147.946/s/gpu LR: 0.000182 Logit Scale: 25.007 Contrastive_loss: 0.18301 (0.26084) Loss: 0.18301 (0.26084) 2025-03-19,03:31:11 | INFO | Train Epoch: 6 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 148.557/s, 148.557/s/gpu LR: 0.000182 Logit Scale: 24.982 Contrastive_loss: 0.55381 (0.26921) Loss: 0.55381 (0.26921) 2025-03-19,03:31:33 | INFO | Train Epoch: 6 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 147.169/s, 147.169/s/gpu LR: 0.000182 Logit Scale: 24.955 Contrastive_loss: 0.14993 (0.26590) Loss: 0.14993 (0.26590) 2025-03-19,03:31:54 | INFO | Train Epoch: 6 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.803/s, 148.803/s/gpu LR: 0.000182 Logit Scale: 24.989 Contrastive_loss: 0.34753 (0.26810) Loss: 0.34753 (0.26810) 2025-03-19,03:32:16 | INFO | Train Epoch: 6 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 147.885/s, 147.885/s/gpu LR: 0.000182 Logit Scale: 24.989 Contrastive_loss: 0.26871 (0.26812) Loss: 0.26871 (0.26812) 2025-03-19,03:32:37 | INFO | Train Epoch: 6 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.218, 147.006/s, 147.006/s/gpu LR: 0.000182 Logit Scale: 24.967 Contrastive_loss: 0.48663 (0.27372) Loss: 0.48663 (0.27372) 2025-03-19,03:32:59 | INFO | Train Epoch: 6 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.220, 146.854/s, 146.854/s/gpu LR: 0.000182 Logit Scale: 24.996 Contrastive_loss: 0.14500 (0.27050) Loss: 0.14500 (0.27050) 2025-03-19,03:33:21 | INFO | Train Epoch: 6 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 151.383/s, 151.383/s/gpu LR: 0.000182 Logit Scale: 24.962 Contrastive_loss: 0.51440 (0.27645) Loss: 0.51440 (0.27645) 2025-03-19,03:33:42 | INFO | Train Epoch: 6 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.212, 147.752/s, 147.752/s/gpu LR: 0.000182 Logit Scale: 24.920 Contrastive_loss: 0.23048 (0.27536) Loss: 0.23048 (0.27536) 2025-03-19,03:34:04 | INFO | Train Epoch: 6 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 152.977/s, 152.977/s/gpu LR: 0.000182 Logit Scale: 24.982 Contrastive_loss: 0.34617 (0.27701) Loss: 0.34617 (0.27701) 2025-03-19,03:34:26 | INFO | Train Epoch: 6 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.936/s, 147.936/s/gpu LR: 0.000182 Logit Scale: 24.975 Contrastive_loss: 0.038984 (0.27160) Loss: 0.038984 (0.27160) 2025-03-19,03:34:47 | INFO | Train Epoch: 6 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 150.101/s, 150.101/s/gpu LR: 0.000182 Logit Scale: 24.959 Contrastive_loss: 0.28689 (0.27194) Loss: 0.28689 (0.27194) 2025-03-19,03:35:08 | INFO | Train Epoch: 6 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.212, 150.558/s, 150.558/s/gpu LR: 0.000182 Logit Scale: 24.937 Contrastive_loss: 0.057978 (0.26728) Loss: 0.057978 (0.26728) 2025-03-19,03:35:30 | INFO | Train Epoch: 6 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.217, 146.161/s, 146.161/s/gpu LR: 0.000182 Logit Scale: 25.012 Contrastive_loss: 0.56539 (0.27363) Loss: 0.56539 (0.27363) 2025-03-19,03:35:52 | INFO | Train Epoch: 6 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.220, 139.983/s, 139.983/s/gpu LR: 0.000182 Logit Scale: 25.028 Contrastive_loss: 0.44726 (0.27724) Loss: 0.44726 (0.27724) 2025-03-19,03:36:14 | INFO | Train Epoch: 6 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.220, 149.187/s, 149.187/s/gpu LR: 0.000182 Logit Scale: 25.029 Contrastive_loss: 0.30943 (0.27790) Loss: 0.30943 (0.27790) 2025-03-19,03:36:35 | INFO | Train Epoch: 6 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 146.769/s, 146.769/s/gpu LR: 0.000182 Logit Scale: 25.011 Contrastive_loss: 0.066525 (0.27367) Loss: 0.066525 (0.27367) 2025-03-19,03:36:57 | INFO | Train Epoch: 6 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 142.468/s, 142.468/s/gpu LR: 0.000182 Logit Scale: 24.967 Contrastive_loss: 0.060534 (0.26949) Loss: 0.060534 (0.26949) 2025-03-19,03:37:19 | INFO | Train Epoch: 6 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 148.459/s, 148.459/s/gpu LR: 0.000182 Logit Scale: 24.960 Contrastive_loss: 0.36750 (0.27138) Loss: 0.36750 (0.27138) 2025-03-19,03:37:40 | INFO | Train Epoch: 6 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 147.050/s, 147.050/s/gpu LR: 0.000182 Logit Scale: 24.955 Contrastive_loss: 0.47629 (0.27525) Loss: 0.47629 (0.27525) 2025-03-19,03:38:02 | INFO | Train Epoch: 6 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.217, 147.577/s, 147.577/s/gpu LR: 0.000182 Logit Scale: 24.976 Contrastive_loss: 0.25443 (0.27486) Loss: 0.25443 (0.27486) 2025-03-19,03:38:24 | INFO | Train Epoch: 6 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 151.567/s, 151.567/s/gpu LR: 0.000182 Logit Scale: 24.992 Contrastive_loss: 0.53802 (0.27964) Loss: 0.53802 (0.27964) 2025-03-19,03:38:45 | INFO | Train Epoch: 6 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.635/s, 149.635/s/gpu LR: 0.000182 Logit Scale: 25.003 Contrastive_loss: 0.11058 (0.27663) Loss: 0.11058 (0.27663) 2025-03-19,03:39:07 | INFO | Train Epoch: 6 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.220, 149.205/s, 149.205/s/gpu LR: 0.000182 Logit Scale: 25.021 Contrastive_loss: 0.63599 (0.28293) Loss: 0.63599 (0.28293) 2025-03-19,03:39:29 | INFO | Train Epoch: 6 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 147.139/s, 147.139/s/gpu LR: 0.000181 Logit Scale: 25.025 Contrastive_loss: 0.35708 (0.28421) Loss: 0.35708 (0.28421) 2025-03-19,03:39:50 | INFO | Train Epoch: 6 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.868/s, 149.868/s/gpu LR: 0.000181 Logit Scale: 25.043 Contrastive_loss: 0.15487 (0.28202) Loss: 0.15487 (0.28202) 2025-03-19,03:40:11 | INFO | Train Epoch: 6 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 151.159/s, 151.159/s/gpu LR: 0.000181 Logit Scale: 25.080 Contrastive_loss: 0.30144 (0.28234) Loss: 0.30144 (0.28234) 2025-03-19,03:40:33 | INFO | Train Epoch: 6 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 148.472/s, 148.472/s/gpu LR: 0.000181 Logit Scale: 25.060 Contrastive_loss: 0.33805 (0.28325) Loss: 0.33805 (0.28325) 2025-03-19,03:40:54 | INFO | Train Epoch: 6 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 148.307/s, 148.307/s/gpu LR: 0.000181 Logit Scale: 25.027 Contrastive_loss: 0.14199 (0.28098) Loss: 0.14199 (0.28098) 2025-03-19,03:41:16 | INFO | Train Epoch: 6 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.213, 149.857/s, 149.857/s/gpu LR: 0.000181 Logit Scale: 25.013 Contrastive_loss: 0.54345 (0.28514) Loss: 0.54345 (0.28514) 2025-03-19,03:41:37 | INFO | Train Epoch: 6 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 151.061/s, 151.061/s/gpu LR: 0.000181 Logit Scale: 25.008 Contrastive_loss: 0.26295 (0.28479) Loss: 0.26295 (0.28479) 2025-03-19,03:41:58 | INFO | Train Epoch: 6 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 149.912/s, 149.912/s/gpu LR: 0.000181 Logit Scale: 25.013 Contrastive_loss: 0.37869 (0.28624) Loss: 0.37869 (0.28624) 2025-03-19,03:42:20 | INFO | Train Epoch: 6 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.217, 147.120/s, 147.120/s/gpu LR: 0.000181 Logit Scale: 24.985 Contrastive_loss: 0.26137 (0.28586) Loss: 0.26137 (0.28586) 2025-03-19,03:42:42 | INFO | Train Epoch: 6 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.394/s, 149.394/s/gpu LR: 0.000181 Logit Scale: 24.921 Contrastive_loss: 0.26129 (0.28550) Loss: 0.26129 (0.28550) 2025-03-19,03:43:03 | INFO | Train Epoch: 6 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 145.418/s, 145.418/s/gpu LR: 0.000181 Logit Scale: 24.940 Contrastive_loss: 0.31180 (0.28588) Loss: 0.31180 (0.28588) 2025-03-19,03:43:25 | INFO | Train Epoch: 6 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.219, 149.320/s, 149.320/s/gpu LR: 0.000181 Logit Scale: 24.939 Contrastive_loss: 0.33488 (0.28659) Loss: 0.33488 (0.28659) 2025-03-19,03:43:47 | INFO | Train Epoch: 6 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.217, 147.590/s, 147.590/s/gpu LR: 0.000181 Logit Scale: 24.884 Contrastive_loss: 0.17497 (0.28500) Loss: 0.17497 (0.28500) 2025-03-19,03:44:08 | INFO | Train Epoch: 6 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.213, 148.410/s, 148.410/s/gpu LR: 0.000181 Logit Scale: 24.928 Contrastive_loss: 0.17324 (0.28342) Loss: 0.17324 (0.28342) 2025-03-19,03:44:30 | INFO | Train Epoch: 6 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.218, 145.681/s, 145.681/s/gpu LR: 0.000181 Logit Scale: 24.906 Contrastive_loss: 0.50299 (0.28647) Loss: 0.50299 (0.28647) 2025-03-19,03:44:52 | INFO | Train Epoch: 6 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 147.675/s, 147.675/s/gpu LR: 0.000181 Logit Scale: 24.923 Contrastive_loss: 0.35113 (0.28736) Loss: 0.35113 (0.28736) 2025-03-19,03:45:13 | INFO | Train Epoch: 6 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 148.187/s, 148.187/s/gpu LR: 0.000181 Logit Scale: 24.933 Contrastive_loss: 0.28400 (0.28731) Loss: 0.28400 (0.28731) 2025-03-19,03:45:35 | INFO | Train Epoch: 6 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 149.776/s, 149.776/s/gpu LR: 0.000181 Logit Scale: 24.925 Contrastive_loss: 0.50785 (0.29025) Loss: 0.50785 (0.29025) 2025-03-19,03:45:57 | INFO | Train Epoch: 6 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.397/s, 149.397/s/gpu LR: 0.000181 Logit Scale: 24.913 Contrastive_loss: 0.26183 (0.28988) Loss: 0.26183 (0.28988) 2025-03-19,03:46:18 | INFO | Train Epoch: 6 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 149.488/s, 149.488/s/gpu LR: 0.000181 Logit Scale: 24.900 Contrastive_loss: 0.27600 (0.28970) Loss: 0.27600 (0.28970) 2025-03-19,03:46:40 | INFO | Train Epoch: 6 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 148.882/s, 148.882/s/gpu LR: 0.000181 Logit Scale: 24.923 Contrastive_loss: 0.49834 (0.29237) Loss: 0.49834 (0.29237) 2025-03-19,03:47:01 | INFO | Train Epoch: 6 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.472/s, 150.472/s/gpu LR: 0.000181 Logit Scale: 24.916 Contrastive_loss: 0.44168 (0.29426) Loss: 0.44168 (0.29426) 2025-03-19,03:47:22 | INFO | Train Epoch: 6 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.212, 150.325/s, 150.325/s/gpu LR: 0.000181 Logit Scale: 24.914 Contrastive_loss: 0.072067 (0.29149) Loss: 0.072067 (0.29149) 2025-03-19,03:47:44 | INFO | Train Epoch: 6 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.657/s, 147.657/s/gpu LR: 0.000181 Logit Scale: 24.932 Contrastive_loss: 0.20433 (0.29041) Loss: 0.20433 (0.29041) 2025-03-19,03:48:05 | INFO | Train Epoch: 6 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.794/s, 149.794/s/gpu LR: 0.000181 Logit Scale: 24.937 Contrastive_loss: 0.20021 (0.28931) Loss: 0.20021 (0.28931) 2025-03-19,03:48:27 | INFO | Train Epoch: 6 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 149.911/s, 149.911/s/gpu LR: 0.000181 Logit Scale: 24.920 Contrastive_loss: 0.37598 (0.29036) Loss: 0.37598 (0.29036) 2025-03-19,03:48:48 | INFO | Train Epoch: 6 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 149.817/s, 149.817/s/gpu LR: 0.000181 Logit Scale: 24.941 Contrastive_loss: 0.17075 (0.28893) Loss: 0.17075 (0.28893) 2025-03-19,03:49:10 | INFO | Train Epoch: 6 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 150.443/s, 150.443/s/gpu LR: 0.000181 Logit Scale: 24.967 Contrastive_loss: 0.22946 (0.28823) Loss: 0.22946 (0.28823) 2025-03-19,03:49:31 | INFO | Train Epoch: 6 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.977/s, 148.977/s/gpu LR: 0.000181 Logit Scale: 24.990 Contrastive_loss: 0.41504 (0.28971) Loss: 0.41504 (0.28971) 2025-03-19,03:49:53 | INFO | Train Epoch: 6 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 146.462/s, 146.462/s/gpu LR: 0.000181 Logit Scale: 24.977 Contrastive_loss: 0.22561 (0.28897) Loss: 0.22561 (0.28897) 2025-03-19,03:50:15 | INFO | Train Epoch: 6 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.219, 150.481/s, 150.481/s/gpu LR: 0.000181 Logit Scale: 24.932 Contrastive_loss: 0.40050 (0.29024) Loss: 0.40050 (0.29024) 2025-03-19,03:50:36 | INFO | Train Epoch: 6 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 146.022/s, 146.022/s/gpu LR: 0.000181 Logit Scale: 24.905 Contrastive_loss: 0.27850 (0.29011) Loss: 0.27850 (0.29011) 2025-03-19,03:50:58 | INFO | Train Epoch: 6 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 149.508/s, 149.508/s/gpu LR: 0.000181 Logit Scale: 24.942 Contrastive_loss: 0.24904 (0.28965) Loss: 0.24904 (0.28965) 2025-03-19,03:51:20 | INFO | Train Epoch: 6 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.947/s, 148.947/s/gpu LR: 0.000181 Logit Scale: 24.959 Contrastive_loss: 0.29298 (0.28969) Loss: 0.29298 (0.28969) 2025-03-19,03:51:42 | INFO | Train Epoch: 6 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.219, 146.600/s, 146.600/s/gpu LR: 0.000181 Logit Scale: 24.975 Contrastive_loss: 0.25919 (0.28935) Loss: 0.25919 (0.28935) 2025-03-19,03:52:04 | INFO | Train Epoch: 6 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.220, 143.290/s, 143.290/s/gpu LR: 0.000181 Logit Scale: 24.963 Contrastive_loss: 0.22177 (0.28863) Loss: 0.22177 (0.28863) 2025-03-19,03:52:26 | INFO | Train Epoch: 6 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.220, 145.925/s, 145.925/s/gpu LR: 0.000181 Logit Scale: 24.942 Contrastive_loss: 0.32129 (0.28898) Loss: 0.32129 (0.28898) 2025-03-19,03:52:47 | INFO | Train Epoch: 6 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.219, 143.106/s, 143.106/s/gpu LR: 0.000181 Logit Scale: 24.925 Contrastive_loss: 0.34516 (0.28957) Loss: 0.34516 (0.28957) 2025-03-19,03:53:09 | INFO | Train Epoch: 6 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.926/s, 149.926/s/gpu LR: 0.000181 Logit Scale: 24.898 Contrastive_loss: 0.33795 (0.29007) Loss: 0.33795 (0.29007) 2025-03-19,03:53:31 | INFO | Train Epoch: 6 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.217, 151.483/s, 151.483/s/gpu LR: 0.000180 Logit Scale: 24.911 Contrastive_loss: 0.16748 (0.28881) Loss: 0.16748 (0.28881) 2025-03-19,03:53:52 | INFO | Train Epoch: 6 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.051/s, 149.051/s/gpu LR: 0.000180 Logit Scale: 24.972 Contrastive_loss: 0.34192 (0.28935) Loss: 0.34192 (0.28935) 2025-03-19,03:54:14 | INFO | Train Epoch: 6 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 147.831/s, 147.831/s/gpu LR: 0.000180 Logit Scale: 24.977 Contrastive_loss: 0.24395 (0.28889) Loss: 0.24395 (0.28889) 2025-03-19,03:54:35 | INFO | Train Epoch: 6 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.912/s, 148.912/s/gpu LR: 0.000180 Logit Scale: 24.956 Contrastive_loss: 0.49313 (0.29093) Loss: 0.49313 (0.29093) 2025-03-19,03:54:57 | INFO | Train Epoch: 6 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.221, 144.323/s, 144.323/s/gpu LR: 0.000180 Logit Scale: 24.947 Contrastive_loss: 0.24521 (0.29048) Loss: 0.24521 (0.29048) 2025-03-19,03:55:19 | INFO | Train Epoch: 6 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 145.628/s, 145.628/s/gpu LR: 0.000180 Logit Scale: 24.964 Contrastive_loss: 0.34143 (0.29098) Loss: 0.34143 (0.29098) 2025-03-19,03:55:41 | INFO | Train Epoch: 6 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 146.615/s, 146.615/s/gpu LR: 0.000180 Logit Scale: 24.935 Contrastive_loss: 0.21162 (0.29021) Loss: 0.21162 (0.29021) 2025-03-19,03:56:03 | INFO | Train Epoch: 6 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.219, 145.624/s, 145.624/s/gpu LR: 0.000180 Logit Scale: 24.983 Contrastive_loss: 0.19691 (0.28931) Loss: 0.19691 (0.28931) 2025-03-19,03:56:25 | INFO | Train Epoch: 6 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.221, 143.262/s, 143.262/s/gpu LR: 0.000180 Logit Scale: 25.002 Contrastive_loss: 0.47251 (0.29106) Loss: 0.47251 (0.29106) 2025-03-19,03:56:47 | INFO | Train Epoch: 6 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.219, 145.443/s, 145.443/s/gpu LR: 0.000180 Logit Scale: 25.004 Contrastive_loss: 0.22223 (0.29041) Loss: 0.22223 (0.29041) 2025-03-19,03:57:09 | INFO | Train Epoch: 6 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.221, 144.766/s, 144.766/s/gpu LR: 0.000180 Logit Scale: 25.015 Contrastive_loss: 0.13001 (0.28891) Loss: 0.13001 (0.28891) 2025-03-19,03:57:31 | INFO | Train Epoch: 6 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.221, 145.759/s, 145.759/s/gpu LR: 0.000180 Logit Scale: 24.974 Contrastive_loss: 0.33986 (0.28938) Loss: 0.33986 (0.28938) 2025-03-19,03:57:53 | INFO | Train Epoch: 6 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.220, 146.357/s, 146.357/s/gpu LR: 0.000180 Logit Scale: 24.988 Contrastive_loss: 0.29700 (0.28945) Loss: 0.29700 (0.28945) 2025-03-19,03:58:15 | INFO | Train Epoch: 6 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.222, 144.507/s, 144.507/s/gpu LR: 0.000180 Logit Scale: 25.006 Contrastive_loss: 0.34873 (0.28999) Loss: 0.34873 (0.28999) 2025-03-19,03:58:37 | INFO | Train Epoch: 6 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 148.935/s, 148.935/s/gpu LR: 0.000180 Logit Scale: 24.918 Contrastive_loss: 0.38180 (0.29082) Loss: 0.38180 (0.29082) 2025-03-19,03:58:58 | INFO | Train Epoch: 6 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.653/s, 149.653/s/gpu LR: 0.000180 Logit Scale: 24.921 Contrastive_loss: 0.45220 (0.29226) Loss: 0.45220 (0.29226) 2025-03-19,03:59:20 | INFO | Train Epoch: 6 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 149.531/s, 149.531/s/gpu LR: 0.000180 Logit Scale: 24.877 Contrastive_loss: 0.24021 (0.29180) Loss: 0.24021 (0.29180) 2025-03-19,03:59:41 | INFO | Train Epoch: 6 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 149.681/s, 149.681/s/gpu LR: 0.000180 Logit Scale: 24.957 Contrastive_loss: 0.32305 (0.29207) Loss: 0.32305 (0.29207) 2025-03-19,04:00:02 | INFO | Train Epoch: 6 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 149.319/s, 149.319/s/gpu LR: 0.000180 Logit Scale: 24.926 Contrastive_loss: 0.30822 (0.29221) Loss: 0.30822 (0.29221) 2025-03-19,04:00:24 | INFO | Train Epoch: 6 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 147.329/s, 147.329/s/gpu LR: 0.000180 Logit Scale: 24.939 Contrastive_loss: 0.10548 (0.29060) Loss: 0.10548 (0.29060) 2025-03-19,04:00:46 | INFO | Train Epoch: 6 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.046/s, 148.046/s/gpu LR: 0.000180 Logit Scale: 25.042 Contrastive_loss: 0.47716 (0.29220) Loss: 0.47716 (0.29220) 2025-03-19,04:01:07 | INFO | Train Epoch: 6 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 149.662/s, 149.662/s/gpu LR: 0.000180 Logit Scale: 24.982 Contrastive_loss: 0.18546 (0.29129) Loss: 0.18546 (0.29129) 2025-03-19,04:01:29 | INFO | Train Epoch: 6 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 151.565/s, 151.565/s/gpu LR: 0.000180 Logit Scale: 24.982 Contrastive_loss: 0.11061 (0.28977) Loss: 0.11061 (0.28977) 2025-03-19,04:01:50 | INFO | Train Epoch: 6 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 147.975/s, 147.975/s/gpu LR: 0.000180 Logit Scale: 25.031 Contrastive_loss: 0.091803 (0.28812) Loss: 0.091803 (0.28812) 2025-03-19,04:02:12 | INFO | Train Epoch: 6 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.217, 149.754/s, 149.754/s/gpu LR: 0.000180 Logit Scale: 25.011 Contrastive_loss: 0.14781 (0.28696) Loss: 0.14781 (0.28696) 2025-03-19,04:02:33 | INFO | Train Epoch: 6 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 147.548/s, 147.548/s/gpu LR: 0.000180 Logit Scale: 25.014 Contrastive_loss: 0.40085 (0.28790) Loss: 0.40085 (0.28790) 2025-03-19,04:02:55 | INFO | Train Epoch: 6 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 148.990/s, 148.990/s/gpu LR: 0.000180 Logit Scale: 25.017 Contrastive_loss: 0.42159 (0.28898) Loss: 0.42159 (0.28898) 2025-03-19,04:03:16 | INFO | Train Epoch: 6 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 148.308/s, 148.308/s/gpu LR: 0.000180 Logit Scale: 25.020 Contrastive_loss: 0.35121 (0.28949) Loss: 0.35121 (0.28949) 2025-03-19,04:03:38 | INFO | Train Epoch: 6 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.813/s, 148.813/s/gpu LR: 0.000180 Logit Scale: 25.006 Contrastive_loss: 0.60853 (0.29204) Loss: 0.60853 (0.29204) 2025-03-19,04:03:59 | INFO | Train Epoch: 6 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 149.741/s, 149.741/s/gpu LR: 0.000180 Logit Scale: 25.009 Contrastive_loss: 0.15854 (0.29098) Loss: 0.15854 (0.29098) 2025-03-19,04:04:21 | INFO | Train Epoch: 6 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 147.314/s, 147.314/s/gpu LR: 0.000180 Logit Scale: 25.022 Contrastive_loss: 0.37604 (0.29165) Loss: 0.37604 (0.29165) 2025-03-19,04:04:43 | INFO | Train Epoch: 6 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.220, 147.159/s, 147.159/s/gpu LR: 0.000180 Logit Scale: 25.029 Contrastive_loss: 0.38394 (0.29237) Loss: 0.38394 (0.29237) 2025-03-19,04:05:05 | INFO | Train Epoch: 6 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.220, 146.729/s, 146.729/s/gpu LR: 0.000180 Logit Scale: 25.033 Contrastive_loss: 0.34499 (0.29278) Loss: 0.34499 (0.29278) 2025-03-19,04:05:27 | INFO | Train Epoch: 6 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.221, 145.444/s, 145.444/s/gpu LR: 0.000180 Logit Scale: 25.065 Contrastive_loss: 0.27109 (0.29261) Loss: 0.27109 (0.29261) 2025-03-19,04:05:49 | INFO | Train Epoch: 6 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.218, 146.514/s, 146.514/s/gpu LR: 0.000180 Logit Scale: 25.081 Contrastive_loss: 0.38065 (0.29328) Loss: 0.38065 (0.29328) 2025-03-19,04:06:11 | INFO | Train Epoch: 6 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 146.375/s, 146.375/s/gpu LR: 0.000180 Logit Scale: 25.077 Contrastive_loss: 0.17495 (0.29239) Loss: 0.17495 (0.29239) 2025-03-19,04:06:32 | INFO | Train Epoch: 6 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 149.858/s, 149.858/s/gpu LR: 0.000180 Logit Scale: 25.067 Contrastive_loss: 0.21469 (0.29180) Loss: 0.21469 (0.29180) 2025-03-19,04:06:54 | INFO | Train Epoch: 6 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 149.415/s, 149.415/s/gpu LR: 0.000179 Logit Scale: 25.075 Contrastive_loss: 0.34600 (0.29221) Loss: 0.34600 (0.29221) 2025-03-19,04:07:16 | INFO | Train Epoch: 6 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 150.438/s, 150.438/s/gpu LR: 0.000179 Logit Scale: 25.071 Contrastive_loss: 0.16901 (0.29129) Loss: 0.16901 (0.29129) 2025-03-19,04:07:37 | INFO | Train Epoch: 6 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 149.135/s, 149.135/s/gpu LR: 0.000179 Logit Scale: 25.082 Contrastive_loss: 0.24294 (0.29094) Loss: 0.24294 (0.29094) 2025-03-19,04:07:59 | INFO | Train Epoch: 6 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.217, 150.898/s, 150.898/s/gpu LR: 0.000179 Logit Scale: 25.047 Contrastive_loss: 0.42560 (0.29192) Loss: 0.42560 (0.29192) 2025-03-19,04:08:21 | INFO | Train Epoch: 6 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.218, 144.685/s, 144.685/s/gpu LR: 0.000179 Logit Scale: 25.005 Contrastive_loss: 0.15551 (0.29093) Loss: 0.15551 (0.29093) 2025-03-19,04:08:43 | INFO | Train Epoch: 6 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.221, 145.640/s, 145.640/s/gpu LR: 0.000179 Logit Scale: 25.018 Contrastive_loss: 0.23916 (0.29056) Loss: 0.23916 (0.29056) 2025-03-19,04:09:04 | INFO | Train Epoch: 6 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 148.985/s, 148.985/s/gpu LR: 0.000179 Logit Scale: 25.003 Contrastive_loss: 0.22183 (0.29007) Loss: 0.22183 (0.29007) 2025-03-19,04:09:26 | INFO | Train Epoch: 6 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.218, 150.161/s, 150.161/s/gpu LR: 0.000179 Logit Scale: 24.996 Contrastive_loss: 0.17364 (0.28924) Loss: 0.17364 (0.28924) 2025-03-19,04:09:47 | INFO | Train Epoch: 6 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.213, 152.339/s, 152.339/s/gpu LR: 0.000179 Logit Scale: 24.961 Contrastive_loss: 0.40626 (0.29007) Loss: 0.40626 (0.29007) 2025-03-19,04:10:09 | INFO | Train Epoch: 6 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.213, 149.304/s, 149.304/s/gpu LR: 0.000179 Logit Scale: 24.943 Contrastive_loss: 0.090242 (0.28867) Loss: 0.090242 (0.28867) 2025-03-19,04:10:30 | INFO | Train Epoch: 6 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 149.501/s, 149.501/s/gpu LR: 0.000179 Logit Scale: 24.962 Contrastive_loss: 0.13937 (0.28763) Loss: 0.13937 (0.28763) 2025-03-19,04:10:52 | INFO | Train Epoch: 6 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.726/s, 149.726/s/gpu LR: 0.000179 Logit Scale: 24.971 Contrastive_loss: 0.39599 (0.28838) Loss: 0.39599 (0.28838) 2025-03-19,04:11:13 | INFO | Train Epoch: 6 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.445/s, 148.445/s/gpu LR: 0.000179 Logit Scale: 25.010 Contrastive_loss: 0.35379 (0.28883) Loss: 0.35379 (0.28883) 2025-03-19,04:11:35 | INFO | Train Epoch: 6 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.811/s, 149.811/s/gpu LR: 0.000179 Logit Scale: 24.972 Contrastive_loss: 0.60943 (0.29101) Loss: 0.60943 (0.29101) 2025-03-19,04:11:56 | INFO | Train Epoch: 6 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 151.029/s, 151.029/s/gpu LR: 0.000179 Logit Scale: 25.010 Contrastive_loss: 0.34223 (0.29136) Loss: 0.34223 (0.29136) 2025-03-19,04:12:18 | INFO | Train Epoch: 6 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 149.980/s, 149.980/s/gpu LR: 0.000179 Logit Scale: 25.063 Contrastive_loss: 0.18084 (0.29061) Loss: 0.18084 (0.29061) 2025-03-19,04:12:39 | INFO | Train Epoch: 6 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.388/s, 149.388/s/gpu LR: 0.000179 Logit Scale: 25.057 Contrastive_loss: 0.37643 (0.29119) Loss: 0.37643 (0.29119) 2025-03-19,04:13:01 | INFO | Train Epoch: 6 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 150.216/s, 150.216/s/gpu LR: 0.000179 Logit Scale: 25.025 Contrastive_loss: 0.51177 (0.29265) Loss: 0.51177 (0.29265) 2025-03-19,04:13:22 | INFO | Train Epoch: 6 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.805/s, 149.805/s/gpu LR: 0.000179 Logit Scale: 25.000 Contrastive_loss: 0.39797 (0.29334) Loss: 0.39797 (0.29334) 2025-03-19,04:13:44 | INFO | Train Epoch: 6 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 149.840/s, 149.840/s/gpu LR: 0.000179 Logit Scale: 25.023 Contrastive_loss: 0.20227 (0.29274) Loss: 0.20227 (0.29274) 2025-03-19,04:14:05 | INFO | Train Epoch: 6 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 149.259/s, 149.259/s/gpu LR: 0.000179 Logit Scale: 24.976 Contrastive_loss: 0.56660 (0.29452) Loss: 0.56660 (0.29452) 2025-03-19,04:14:26 | INFO | Train Epoch: 6 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.341/s, 149.341/s/gpu LR: 0.000179 Logit Scale: 25.025 Contrastive_loss: 0.24460 (0.29420) Loss: 0.24460 (0.29420) 2025-03-19,04:14:48 | INFO | Train Epoch: 6 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 148.863/s, 148.863/s/gpu LR: 0.000179 Logit Scale: 25.008 Contrastive_loss: 0.19718 (0.29358) Loss: 0.19718 (0.29358) 2025-03-19,04:15:09 | INFO | Train Epoch: 6 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 149.971/s, 149.971/s/gpu LR: 0.000179 Logit Scale: 25.020 Contrastive_loss: 0.81762 (0.29692) Loss: 0.81762 (0.29692) 2025-03-19,04:15:31 | INFO | Train Epoch: 6 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.154/s, 150.154/s/gpu LR: 0.000179 Logit Scale: 25.014 Contrastive_loss: 0.43235 (0.29777) Loss: 0.43235 (0.29777) 2025-03-19,04:15:52 | INFO | Train Epoch: 6 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 148.303/s, 148.303/s/gpu LR: 0.000179 Logit Scale: 25.056 Contrastive_loss: 0.48214 (0.29893) Loss: 0.48214 (0.29893) 2025-03-19,04:16:13 | INFO | Train Epoch: 6 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.213, 151.159/s, 151.159/s/gpu LR: 0.000179 Logit Scale: 25.054 Contrastive_loss: 0.45405 (0.29990) Loss: 0.45405 (0.29990) 2025-03-19,04:16:35 | INFO | Train Epoch: 6 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.213, 150.808/s, 150.808/s/gpu LR: 0.000179 Logit Scale: 25.076 Contrastive_loss: 0.18029 (0.29916) Loss: 0.18029 (0.29916) 2025-03-19,04:16:56 | INFO | Train Epoch: 6 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 149.818/s, 149.818/s/gpu LR: 0.000179 Logit Scale: 25.074 Contrastive_loss: 0.29122 (0.29911) Loss: 0.29122 (0.29911) 2025-03-19,04:17:18 | INFO | Train Epoch: 6 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 148.873/s, 148.873/s/gpu LR: 0.000179 Logit Scale: 25.068 Contrastive_loss: 0.023738 (0.29742) Loss: 0.023738 (0.29742) 2025-03-19,04:17:39 | INFO | Train Epoch: 6 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 146.675/s, 146.675/s/gpu LR: 0.000179 Logit Scale: 25.062 Contrastive_loss: 0.22081 (0.29695) Loss: 0.22081 (0.29695) 2025-03-19,04:18:01 | INFO | Train Epoch: 6 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 144.890/s, 144.890/s/gpu LR: 0.000179 Logit Scale: 25.065 Contrastive_loss: 0.37399 (0.29742) Loss: 0.37399 (0.29742) 2025-03-19,04:18:23 | INFO | Train Epoch: 6 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 145.547/s, 145.547/s/gpu LR: 0.000179 Logit Scale: 25.070 Contrastive_loss: 0.49251 (0.29860) Loss: 0.49251 (0.29860) 2025-03-19,04:18:45 | INFO | Train Epoch: 6 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 148.665/s, 148.665/s/gpu LR: 0.000179 Logit Scale: 25.054 Contrastive_loss: 0.42715 (0.29937) Loss: 0.42715 (0.29937) 2025-03-19,04:19:06 | INFO | Train Epoch: 6 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 149.453/s, 149.453/s/gpu LR: 0.000179 Logit Scale: 25.070 Contrastive_loss: 0.47384 (0.30040) Loss: 0.47384 (0.30040) 2025-03-19,04:19:28 | INFO | Train Epoch: 6 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 148.830/s, 148.830/s/gpu LR: 0.000179 Logit Scale: 25.051 Contrastive_loss: 0.54491 (0.30185) Loss: 0.54491 (0.30185) 2025-03-19,04:19:49 | INFO | Train Epoch: 6 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 147.730/s, 147.730/s/gpu LR: 0.000179 Logit Scale: 25.079 Contrastive_loss: 0.29055 (0.30179) Loss: 0.29055 (0.30179) 2025-03-19,04:20:11 | INFO | Train Epoch: 6 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 150.706/s, 150.706/s/gpu LR: 0.000178 Logit Scale: 25.099 Contrastive_loss: 0.098950 (0.30060) Loss: 0.098950 (0.30060) 2025-03-19,04:20:32 | INFO | Train Epoch: 6 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 149.913/s, 149.913/s/gpu LR: 0.000178 Logit Scale: 25.123 Contrastive_loss: 0.087173 (0.29936) Loss: 0.087173 (0.29936) 2025-03-19,04:20:54 | INFO | Train Epoch: 6 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 149.169/s, 149.169/s/gpu LR: 0.000178 Logit Scale: 25.072 Contrastive_loss: 0.21584 (0.29888) Loss: 0.21584 (0.29888) 2025-03-19,04:21:15 | INFO | Train Epoch: 6 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 149.304/s, 149.304/s/gpu LR: 0.000178 Logit Scale: 25.096 Contrastive_loss: 0.23451 (0.29851) Loss: 0.23451 (0.29851) 2025-03-19,04:21:37 | INFO | Train Epoch: 6 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.213, 149.935/s, 149.935/s/gpu LR: 0.000178 Logit Scale: 25.080 Contrastive_loss: 0.19308 (0.29790) Loss: 0.19308 (0.29790) 2025-03-19,04:21:58 | INFO | Train Epoch: 6 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.214, 150.023/s, 150.023/s/gpu LR: 0.000178 Logit Scale: 25.051 Contrastive_loss: 0.28118 (0.29781) Loss: 0.28118 (0.29781) 2025-03-19,04:22:19 | INFO | Train Epoch: 6 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 149.145/s, 149.145/s/gpu LR: 0.000178 Logit Scale: 25.080 Contrastive_loss: 0.59371 (0.29948) Loss: 0.59371 (0.29948) 2025-03-19,04:22:41 | INFO | Train Epoch: 6 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 148.769/s, 148.769/s/gpu LR: 0.000178 Logit Scale: 25.129 Contrastive_loss: 0.37271 (0.29989) Loss: 0.37271 (0.29989) 2025-03-19,04:23:03 | INFO | Train Epoch: 6 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 148.275/s, 148.275/s/gpu LR: 0.000178 Logit Scale: 25.154 Contrastive_loss: 0.12147 (0.29889) Loss: 0.12147 (0.29889) 2025-03-19,04:23:24 | INFO | Train Epoch: 6 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 149.766/s, 149.766/s/gpu LR: 0.000178 Logit Scale: 25.109 Contrastive_loss: 0.26418 (0.29870) Loss: 0.26418 (0.29870) 2025-03-19,04:23:46 | INFO | Train Epoch: 6 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 149.195/s, 149.195/s/gpu LR: 0.000178 Logit Scale: 25.082 Contrastive_loss: 0.19888 (0.29815) Loss: 0.19888 (0.29815) 2025-03-19,04:24:07 | INFO | Train Epoch: 6 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 149.267/s, 149.267/s/gpu LR: 0.000178 Logit Scale: 25.073 Contrastive_loss: 0.14600 (0.29731) Loss: 0.14600 (0.29731) 2025-03-19,04:24:29 | INFO | Train Epoch: 6 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.517/s, 148.517/s/gpu LR: 0.000178 Logit Scale: 25.074 Contrastive_loss: 0.45440 (0.29817) Loss: 0.45440 (0.29817) 2025-03-19,04:24:50 | INFO | Train Epoch: 6 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.793/s, 148.793/s/gpu LR: 0.000178 Logit Scale: 25.048 Contrastive_loss: 0.20020 (0.29764) Loss: 0.20020 (0.29764) 2025-03-19,04:25:12 | INFO | Train Epoch: 6 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.219, 145.462/s, 145.462/s/gpu LR: 0.000178 Logit Scale: 25.072 Contrastive_loss: 0.13972 (0.29679) Loss: 0.13972 (0.29679) 2025-03-19,04:25:34 | INFO | Train Epoch: 6 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.220, 144.958/s, 144.958/s/gpu LR: 0.000178 Logit Scale: 25.003 Contrastive_loss: 0.052967 (0.29548) Loss: 0.052967 (0.29548) 2025-03-19,04:25:56 | INFO | Train Epoch: 6 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.220, 147.542/s, 147.542/s/gpu LR: 0.000178 Logit Scale: 25.032 Contrastive_loss: 0.19204 (0.29492) Loss: 0.19204 (0.29492) 2025-03-19,04:26:18 | INFO | Train Epoch: 6 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.217, 149.530/s, 149.530/s/gpu LR: 0.000178 Logit Scale: 25.001 Contrastive_loss: 0.029151 (0.29351) Loss: 0.029151 (0.29351) 2025-03-19,04:26:39 | INFO | Train Epoch: 6 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.172/s, 148.172/s/gpu LR: 0.000178 Logit Scale: 25.018 Contrastive_loss: 0.31489 (0.29362) Loss: 0.31489 (0.29362) 2025-03-19,04:27:01 | INFO | Train Epoch: 6 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.218, 148.201/s, 148.201/s/gpu LR: 0.000178 Logit Scale: 25.052 Contrastive_loss: 0.33122 (0.29382) Loss: 0.33122 (0.29382) 2025-03-19,04:27:23 | INFO | Train Epoch: 6 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 146.473/s, 146.473/s/gpu LR: 0.000178 Logit Scale: 24.997 Contrastive_loss: 0.086851 (0.29274) Loss: 0.086851 (0.29274) 2025-03-19,04:27:44 | INFO | Train Epoch: 6 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 148.637/s, 148.637/s/gpu LR: 0.000178 Logit Scale: 25.000 Contrastive_loss: 0.15573 (0.29202) Loss: 0.15573 (0.29202) 2025-03-19,04:28:06 | INFO | Train Epoch: 6 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 149.403/s, 149.403/s/gpu LR: 0.000178 Logit Scale: 25.019 Contrastive_loss: 0.42823 (0.29273) Loss: 0.42823 (0.29273) 2025-03-19,04:28:27 | INFO | Train Epoch: 6 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.725/s, 149.725/s/gpu LR: 0.000178 Logit Scale: 25.003 Contrastive_loss: 0.31200 (0.29283) Loss: 0.31200 (0.29283) 2025-03-19,04:28:49 | INFO | Train Epoch: 6 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.837/s, 148.837/s/gpu LR: 0.000178 Logit Scale: 25.043 Contrastive_loss: 0.11892 (0.29194) Loss: 0.11892 (0.29194) 2025-03-19,04:29:10 | INFO | Train Epoch: 6 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.489/s, 148.489/s/gpu LR: 0.000178 Logit Scale: 25.043 Contrastive_loss: 0.17367 (0.29133) Loss: 0.17367 (0.29133) 2025-03-19,04:29:32 | INFO | Train Epoch: 6 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 148.490/s, 148.490/s/gpu LR: 0.000178 Logit Scale: 25.020 Contrastive_loss: 0.53641 (0.29258) Loss: 0.53641 (0.29258) 2025-03-19,04:29:54 | INFO | Train Epoch: 6 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 148.177/s, 148.177/s/gpu LR: 0.000178 Logit Scale: 25.020 Contrastive_loss: 0.31889 (0.29271) Loss: 0.31889 (0.29271) 2025-03-19,04:30:15 | INFO | Train Epoch: 6 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 148.493/s, 148.493/s/gpu LR: 0.000178 Logit Scale: 25.043 Contrastive_loss: 0.16669 (0.29208) Loss: 0.16669 (0.29208) 2025-03-19,04:30:37 | INFO | Train Epoch: 6 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 148.881/s, 148.881/s/gpu LR: 0.000178 Logit Scale: 25.055 Contrastive_loss: 0.18029 (0.29152) Loss: 0.18029 (0.29152) 2025-03-19,04:30:58 | INFO | Train Epoch: 6 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.358/s, 148.358/s/gpu LR: 0.000178 Logit Scale: 25.041 Contrastive_loss: 0.21322 (0.29113) Loss: 0.21322 (0.29113) 2025-03-19,04:31:20 | INFO | Train Epoch: 6 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 149.097/s, 149.097/s/gpu LR: 0.000178 Logit Scale: 25.044 Contrastive_loss: 0.35383 (0.29144) Loss: 0.35383 (0.29144) 2025-03-19,04:31:41 | INFO | Train Epoch: 6 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 148.132/s, 148.132/s/gpu LR: 0.000178 Logit Scale: 25.025 Contrastive_loss: 0.38871 (0.29192) Loss: 0.38871 (0.29192) 2025-03-19,04:32:03 | INFO | Train Epoch: 6 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 148.881/s, 148.881/s/gpu LR: 0.000178 Logit Scale: 25.060 Contrastive_loss: 0.066604 (0.29081) Loss: 0.066604 (0.29081) 2025-03-19,04:32:25 | INFO | Train Epoch: 6 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 146.888/s, 146.888/s/gpu LR: 0.000178 Logit Scale: 25.026 Contrastive_loss: 0.42731 (0.29148) Loss: 0.42731 (0.29148) 2025-03-19,04:32:46 | INFO | Train Epoch: 6 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.856/s, 148.856/s/gpu LR: 0.000178 Logit Scale: 25.023 Contrastive_loss: 0.26022 (0.29133) Loss: 0.26022 (0.29133) 2025-03-19,04:33:08 | INFO | Train Epoch: 6 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.963/s, 148.963/s/gpu LR: 0.000177 Logit Scale: 25.069 Contrastive_loss: 0.29717 (0.29135) Loss: 0.29717 (0.29135) 2025-03-19,04:33:30 | INFO | Train Epoch: 6 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 147.994/s, 147.994/s/gpu LR: 0.000177 Logit Scale: 25.072 Contrastive_loss: 0.22789 (0.29105) Loss: 0.22789 (0.29105) 2025-03-19,04:33:51 | INFO | Train Epoch: 6 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 147.420/s, 147.420/s/gpu LR: 0.000177 Logit Scale: 25.077 Contrastive_loss: 0.10021 (0.29014) Loss: 0.10021 (0.29014) 2025-03-19,04:34:13 | INFO | Train Epoch: 6 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 147.862/s, 147.862/s/gpu LR: 0.000177 Logit Scale: 25.045 Contrastive_loss: 0.40202 (0.29067) Loss: 0.40202 (0.29067) 2025-03-19,04:34:34 | INFO | Train Epoch: 6 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 148.742/s, 148.742/s/gpu LR: 0.000177 Logit Scale: 25.058 Contrastive_loss: 0.20680 (0.29027) Loss: 0.20680 (0.29027) 2025-03-19,04:34:56 | INFO | Train Epoch: 6 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 150.017/s, 150.017/s/gpu LR: 0.000177 Logit Scale: 25.042 Contrastive_loss: 0.36371 (0.29062) Loss: 0.36371 (0.29062) 2025-03-19,04:35:17 | INFO | Train Epoch: 6 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 146.694/s, 146.694/s/gpu LR: 0.000177 Logit Scale: 25.103 Contrastive_loss: 0.17011 (0.29005) Loss: 0.17011 (0.29005) 2025-03-19,04:35:39 | INFO | Train Epoch: 6 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 149.376/s, 149.376/s/gpu LR: 0.000177 Logit Scale: 25.088 Contrastive_loss: 0.28096 (0.29001) Loss: 0.28096 (0.29001) 2025-03-19,04:36:00 | INFO | Train Epoch: 6 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.213, 147.596/s, 147.596/s/gpu LR: 0.000177 Logit Scale: 25.063 Contrastive_loss: 0.34973 (0.29029) Loss: 0.34973 (0.29029) 2025-03-19,04:36:21 | INFO | Train Epoch: 6 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.212, 149.271/s, 149.271/s/gpu LR: 0.000177 Logit Scale: 25.080 Contrastive_loss: 0.24212 (0.29006) Loss: 0.24212 (0.29006) 2025-03-19,04:36:43 | INFO | Train Epoch: 6 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 148.809/s, 148.809/s/gpu LR: 0.000177 Logit Scale: 25.094 Contrastive_loss: 0.38499 (0.29050) Loss: 0.38499 (0.29050) 2025-03-19,04:37:04 | INFO | Train Epoch: 6 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 147.288/s, 147.288/s/gpu LR: 0.000177 Logit Scale: 25.101 Contrastive_loss: 0.14049 (0.28981) Loss: 0.14049 (0.28981) 2025-03-19,04:37:26 | INFO | Train Epoch: 6 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.065/s, 149.065/s/gpu LR: 0.000177 Logit Scale: 25.115 Contrastive_loss: 0.20751 (0.28944) Loss: 0.20751 (0.28944) 2025-03-19,04:37:47 | INFO | Train Epoch: 6 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.089/s, 149.089/s/gpu LR: 0.000177 Logit Scale: 25.130 Contrastive_loss: 0.38007 (0.28985) Loss: 0.38007 (0.28985) 2025-03-19,04:38:09 | INFO | Train Epoch: 6 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 148.013/s, 148.013/s/gpu LR: 0.000177 Logit Scale: 25.099 Contrastive_loss: 0.60810 (0.29129) Loss: 0.60810 (0.29129) 2025-03-19,04:38:31 | INFO | Train Epoch: 6 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 152.076/s, 152.076/s/gpu LR: 0.000177 Logit Scale: 25.092 Contrastive_loss: 0.054777 (0.29023) Loss: 0.054777 (0.29023) 2025-03-19,04:38:52 | INFO | Train Epoch: 6 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.952/s, 150.952/s/gpu LR: 0.000177 Logit Scale: 25.149 Contrastive_loss: 0.25941 (0.29009) Loss: 0.25941 (0.29009) 2025-03-19,04:39:13 | INFO | Train Epoch: 6 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.212, 151.315/s, 151.315/s/gpu LR: 0.000177 Logit Scale: 25.136 Contrastive_loss: 0.26243 (0.28996) Loss: 0.26243 (0.28996) 2025-03-19,04:39:34 | INFO | Train Epoch: 6 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.212, 151.433/s, 151.433/s/gpu LR: 0.000177 Logit Scale: 25.152 Contrastive_loss: 0.21694 (0.28964) Loss: 0.21694 (0.28964) 2025-03-19,04:39:56 | INFO | Train Epoch: 6 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 149.721/s, 149.721/s/gpu LR: 0.000177 Logit Scale: 25.096 Contrastive_loss: 0.20645 (0.28927) Loss: 0.20645 (0.28927) 2025-03-19,04:40:17 | INFO | Train Epoch: 6 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 149.512/s, 149.512/s/gpu LR: 0.000177 Logit Scale: 25.076 Contrastive_loss: 0.31863 (0.28940) Loss: 0.31863 (0.28940) 2025-03-19,04:40:38 | INFO | Train Epoch: 6 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 149.731/s, 149.731/s/gpu LR: 0.000177 Logit Scale: 25.077 Contrastive_loss: 0.29677 (0.28943) Loss: 0.29677 (0.28943) 2025-03-19,04:41:00 | INFO | Train Epoch: 6 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 144.359/s, 144.359/s/gpu LR: 0.000177 Logit Scale: 25.058 Contrastive_loss: 0.40996 (0.28996) Loss: 0.40996 (0.28996) 2025-03-19,04:41:21 | INFO | Train Epoch: 6 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 151.418/s, 151.418/s/gpu LR: 0.000177 Logit Scale: 25.047 Contrastive_loss: 0.39411 (0.29041) Loss: 0.39411 (0.29041) 2025-03-19,04:41:43 | INFO | Train Epoch: 6 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 150.445/s, 150.445/s/gpu LR: 0.000177 Logit Scale: 25.086 Contrastive_loss: 0.33679 (0.29061) Loss: 0.33679 (0.29061) 2025-03-19,04:42:05 | INFO | Train Epoch: 6 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.218, 151.772/s, 151.772/s/gpu LR: 0.000177 Logit Scale: 25.126 Contrastive_loss: 0.15350 (0.29002) Loss: 0.15350 (0.29002) 2025-03-19,04:42:26 | INFO | Train Epoch: 6 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 146.599/s, 146.599/s/gpu LR: 0.000177 Logit Scale: 25.076 Contrastive_loss: 0.49127 (0.29089) Loss: 0.49127 (0.29089) 2025-03-19,04:42:48 | INFO | Train Epoch: 6 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.221, 140.575/s, 140.575/s/gpu LR: 0.000177 Logit Scale: 25.045 Contrastive_loss: 0.25794 (0.29074) Loss: 0.25794 (0.29074) 2025-03-19,04:43:10 | INFO | Train Epoch: 6 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 148.673/s, 148.673/s/gpu LR: 0.000177 Logit Scale: 25.070 Contrastive_loss: 0.27587 (0.29068) Loss: 0.27587 (0.29068) 2025-03-19,04:43:31 | INFO | Train Epoch: 6 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 147.736/s, 147.736/s/gpu LR: 0.000177 Logit Scale: 25.094 Contrastive_loss: 0.20141 (0.29030) Loss: 0.20141 (0.29030) 2025-03-19,04:43:53 | INFO | Train Epoch: 6 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 149.354/s, 149.354/s/gpu LR: 0.000177 Logit Scale: 25.121 Contrastive_loss: 0.22497 (0.29003) Loss: 0.22497 (0.29003) 2025-03-19,04:44:14 | INFO | Train Epoch: 6 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.186/s, 149.186/s/gpu LR: 0.000177 Logit Scale: 25.150 Contrastive_loss: 0.13275 (0.28937) Loss: 0.13275 (0.28937) 2025-03-19,04:44:36 | INFO | Train Epoch: 6 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.267/s, 149.267/s/gpu LR: 0.000177 Logit Scale: 25.159 Contrastive_loss: 0.49883 (0.29024) Loss: 0.49883 (0.29024) 2025-03-19,04:44:57 | INFO | Train Epoch: 6 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.216, 152.351/s, 152.351/s/gpu LR: 0.000177 Logit Scale: 25.183 Contrastive_loss: 0.18729 (0.28981) Loss: 0.18729 (0.28981) 2025-03-19,04:45:05 | INFO | Train Epoch: 6 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.214, 149.997/s, 149.997/s/gpu LR: 0.000177 Logit Scale: 25.183 Contrastive_loss: 0.17326 (0.28933) Loss: 0.17326 (0.28933) 2025-03-19,04:45:05 | INFO | Eval Epoch: 7 [32 / 7443] Clip Loss: 3.189789 2025-03-19,04:45:11 | INFO | Eval Epoch: 7 [3232 / 7443] Clip Loss: 0.926114 2025-03-19,04:45:17 | INFO | Eval Epoch: 7 [6432 / 7443] Clip Loss: 0.719118 2025-03-19,04:45:19 | INFO | Eval Epoch: 7 image_to_text_mean_rank: 108.2848 image_to_text_median_rank: 8.0000 image_to_text_R@1: 0.1116 image_to_text_R@5: 0.3945 image_to_text_R@10: 0.5699 text_to_image_mean_rank: 71.4835 text_to_image_median_rank: 8.0000 text_to_image_R@1: 0.1161 text_to_image_R@5: 0.4004 text_to_image_R@10: 0.5671 clip_val_loss: 0.6808 epoch: 7.0000 num_samples: 7443.0000 2025-03-19,04:45:52 | INFO | Start epoch 7 2025-03-19,04:45:52 | INFO | Train Epoch: 7 [ 32/766009 (0%)] Data (t): 0.167 Batch (t): 0.376, 85.0146/s, 85.0146/s/gpu LR: 0.000177 Logit Scale: 25.182 Contrastive_loss: 0.58029 (0.58029) Loss: 0.58029 (0.58029) 2025-03-19,04:46:14 | INFO | Train Epoch: 7 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.217, 151.730/s, 151.730/s/gpu LR: 0.000177 Logit Scale: 25.208 Contrastive_loss: 0.21605 (0.39817) Loss: 0.21605 (0.39817) 2025-03-19,04:46:35 | INFO | Train Epoch: 7 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 151.664/s, 151.664/s/gpu LR: 0.000176 Logit Scale: 25.256 Contrastive_loss: 0.27397 (0.35677) Loss: 0.27397 (0.35677) 2025-03-19,04:46:57 | INFO | Train Epoch: 7 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 151.674/s, 151.674/s/gpu LR: 0.000176 Logit Scale: 25.269 Contrastive_loss: 0.28603 (0.33909) Loss: 0.28603 (0.33909) 2025-03-19,04:47:18 | INFO | Train Epoch: 7 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 149.242/s, 149.242/s/gpu LR: 0.000176 Logit Scale: 25.285 Contrastive_loss: 0.15523 (0.30232) Loss: 0.15523 (0.30232) 2025-03-19,04:47:40 | INFO | Train Epoch: 7 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 148.550/s, 148.550/s/gpu LR: 0.000176 Logit Scale: 25.218 Contrastive_loss: 0.064431 (0.26267) Loss: 0.064431 (0.26267) 2025-03-19,04:48:01 | INFO | Train Epoch: 7 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.290/s, 149.290/s/gpu LR: 0.000176 Logit Scale: 25.247 Contrastive_loss: 0.50507 (0.29730) Loss: 0.50507 (0.29730) 2025-03-19,04:48:23 | INFO | Train Epoch: 7 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 148.886/s, 148.886/s/gpu LR: 0.000176 Logit Scale: 25.275 Contrastive_loss: 0.20677 (0.28598) Loss: 0.20677 (0.28598) 2025-03-19,04:48:44 | INFO | Train Epoch: 7 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 152.027/s, 152.027/s/gpu LR: 0.000176 Logit Scale: 25.275 Contrastive_loss: 0.20171 (0.27662) Loss: 0.20171 (0.27662) 2025-03-19,04:49:05 | INFO | Train Epoch: 7 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.212, 151.943/s, 151.943/s/gpu LR: 0.000176 Logit Scale: 25.328 Contrastive_loss: 0.19458 (0.26841) Loss: 0.19458 (0.26841) 2025-03-19,04:49:26 | INFO | Train Epoch: 7 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.212, 151.225/s, 151.225/s/gpu LR: 0.000176 Logit Scale: 25.337 Contrastive_loss: 0.26023 (0.26767) Loss: 0.26023 (0.26767) 2025-03-19,04:49:48 | INFO | Train Epoch: 7 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.451/s, 149.451/s/gpu LR: 0.000176 Logit Scale: 25.299 Contrastive_loss: 0.17658 (0.26008) Loss: 0.17658 (0.26008) 2025-03-19,04:50:09 | INFO | Train Epoch: 7 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 148.982/s, 148.982/s/gpu LR: 0.000176 Logit Scale: 25.314 Contrastive_loss: 0.28848 (0.26226) Loss: 0.28848 (0.26226) 2025-03-19,04:50:31 | INFO | Train Epoch: 7 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 149.539/s, 149.539/s/gpu LR: 0.000176 Logit Scale: 25.347 Contrastive_loss: 0.15117 (0.25433) Loss: 0.15117 (0.25433) 2025-03-19,04:50:53 | INFO | Train Epoch: 7 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 149.334/s, 149.334/s/gpu LR: 0.000176 Logit Scale: 25.306 Contrastive_loss: 0.42786 (0.26590) Loss: 0.42786 (0.26590) 2025-03-19,04:51:14 | INFO | Train Epoch: 7 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 147.695/s, 147.695/s/gpu LR: 0.000176 Logit Scale: 25.332 Contrastive_loss: 0.17704 (0.26034) Loss: 0.17704 (0.26034) 2025-03-19,04:51:36 | INFO | Train Epoch: 7 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 150.540/s, 150.540/s/gpu LR: 0.000176 Logit Scale: 25.345 Contrastive_loss: 0.51262 (0.27518) Loss: 0.51262 (0.27518) 2025-03-19,04:51:57 | INFO | Train Epoch: 7 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 146.372/s, 146.372/s/gpu LR: 0.000176 Logit Scale: 25.368 Contrastive_loss: 0.14338 (0.26786) Loss: 0.14338 (0.26786) 2025-03-19,04:52:19 | INFO | Train Epoch: 7 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.792/s, 149.792/s/gpu LR: 0.000176 Logit Scale: 25.342 Contrastive_loss: 0.31036 (0.27010) Loss: 0.31036 (0.27010) 2025-03-19,04:52:40 | INFO | Train Epoch: 7 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 147.573/s, 147.573/s/gpu LR: 0.000176 Logit Scale: 25.334 Contrastive_loss: 0.14862 (0.26402) Loss: 0.14862 (0.26402) 2025-03-19,04:53:02 | INFO | Train Epoch: 7 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 146.033/s, 146.033/s/gpu LR: 0.000176 Logit Scale: 25.311 Contrastive_loss: 0.37558 (0.26934) Loss: 0.37558 (0.26934) 2025-03-19,04:53:24 | INFO | Train Epoch: 7 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 147.934/s, 147.934/s/gpu LR: 0.000176 Logit Scale: 25.310 Contrastive_loss: 0.19905 (0.26614) Loss: 0.19905 (0.26614) 2025-03-19,04:53:45 | INFO | Train Epoch: 7 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.218, 144.494/s, 144.494/s/gpu LR: 0.000176 Logit Scale: 25.299 Contrastive_loss: 0.30498 (0.26783) Loss: 0.30498 (0.26783) 2025-03-19,04:54:07 | INFO | Train Epoch: 7 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 145.026/s, 145.026/s/gpu LR: 0.000176 Logit Scale: 25.320 Contrastive_loss: 0.24872 (0.26703) Loss: 0.24872 (0.26703) 2025-03-19,04:54:29 | INFO | Train Epoch: 7 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 149.895/s, 149.895/s/gpu LR: 0.000176 Logit Scale: 25.314 Contrastive_loss: 0.71327 (0.28488) Loss: 0.71327 (0.28488) 2025-03-19,04:54:51 | INFO | Train Epoch: 7 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 145.871/s, 145.871/s/gpu LR: 0.000176 Logit Scale: 25.261 Contrastive_loss: 0.17763 (0.28076) Loss: 0.17763 (0.28076) 2025-03-19,04:55:13 | INFO | Train Epoch: 7 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 149.450/s, 149.450/s/gpu LR: 0.000176 Logit Scale: 25.246 Contrastive_loss: 0.16529 (0.27648) Loss: 0.16529 (0.27648) 2025-03-19,04:55:34 | INFO | Train Epoch: 7 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.213, 150.602/s, 150.602/s/gpu LR: 0.000176 Logit Scale: 25.200 Contrastive_loss: 0.18896 (0.27336) Loss: 0.18896 (0.27336) 2025-03-19,04:55:55 | INFO | Train Epoch: 7 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.213, 150.904/s, 150.904/s/gpu LR: 0.000176 Logit Scale: 25.238 Contrastive_loss: 0.20870 (0.27113) Loss: 0.20870 (0.27113) 2025-03-19,04:56:17 | INFO | Train Epoch: 7 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 147.593/s, 147.593/s/gpu LR: 0.000176 Logit Scale: 25.306 Contrastive_loss: 0.35043 (0.27377) Loss: 0.35043 (0.27377) 2025-03-19,04:56:38 | INFO | Train Epoch: 7 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 148.266/s, 148.266/s/gpu LR: 0.000176 Logit Scale: 25.275 Contrastive_loss: 0.15979 (0.27009) Loss: 0.15979 (0.27009) 2025-03-19,04:57:00 | INFO | Train Epoch: 7 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 151.571/s, 151.571/s/gpu LR: 0.000176 Logit Scale: 25.252 Contrastive_loss: 0.39416 (0.27397) Loss: 0.39416 (0.27397) 2025-03-19,04:57:21 | INFO | Train Epoch: 7 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.213, 151.041/s, 151.041/s/gpu LR: 0.000176 Logit Scale: 25.248 Contrastive_loss: 0.73162 (0.28784) Loss: 0.73162 (0.28784) 2025-03-19,04:57:43 | INFO | Train Epoch: 7 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.277/s, 149.277/s/gpu LR: 0.000176 Logit Scale: 25.223 Contrastive_loss: 0.18445 (0.28480) Loss: 0.18445 (0.28480) 2025-03-19,04:58:04 | INFO | Train Epoch: 7 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 148.385/s, 148.385/s/gpu LR: 0.000176 Logit Scale: 25.208 Contrastive_loss: 0.13497 (0.28052) Loss: 0.13497 (0.28052) 2025-03-19,04:58:26 | INFO | Train Epoch: 7 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.735/s, 148.735/s/gpu LR: 0.000176 Logit Scale: 25.234 Contrastive_loss: 0.31686 (0.28153) Loss: 0.31686 (0.28153) 2025-03-19,04:58:48 | INFO | Train Epoch: 7 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 147.975/s, 147.975/s/gpu LR: 0.000176 Logit Scale: 25.200 Contrastive_loss: 0.24806 (0.28062) Loss: 0.24806 (0.28062) 2025-03-19,04:59:09 | INFO | Train Epoch: 7 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 143.578/s, 143.578/s/gpu LR: 0.000175 Logit Scale: 25.221 Contrastive_loss: 0.16067 (0.27747) Loss: 0.16067 (0.27747) 2025-03-19,04:59:31 | INFO | Train Epoch: 7 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 148.532/s, 148.532/s/gpu LR: 0.000175 Logit Scale: 25.292 Contrastive_loss: 0.11806 (0.27338) Loss: 0.11806 (0.27338) 2025-03-19,04:59:52 | INFO | Train Epoch: 7 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 148.405/s, 148.405/s/gpu LR: 0.000175 Logit Scale: 25.318 Contrastive_loss: 0.30407 (0.27415) Loss: 0.30407 (0.27415) 2025-03-19,05:00:14 | INFO | Train Epoch: 7 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 147.104/s, 147.104/s/gpu LR: 0.000175 Logit Scale: 25.275 Contrastive_loss: 0.12819 (0.27059) Loss: 0.12819 (0.27059) 2025-03-19,05:00:36 | INFO | Train Epoch: 7 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 147.812/s, 147.812/s/gpu LR: 0.000175 Logit Scale: 25.267 Contrastive_loss: 0.44328 (0.27470) Loss: 0.44328 (0.27470) 2025-03-19,05:00:57 | INFO | Train Epoch: 7 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 147.913/s, 147.913/s/gpu LR: 0.000175 Logit Scale: 25.226 Contrastive_loss: 0.27087 (0.27461) Loss: 0.27087 (0.27461) 2025-03-19,05:01:19 | INFO | Train Epoch: 7 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 149.149/s, 149.149/s/gpu LR: 0.000175 Logit Scale: 25.269 Contrastive_loss: 0.31279 (0.27548) Loss: 0.31279 (0.27548) 2025-03-19,05:01:41 | INFO | Train Epoch: 7 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 148.684/s, 148.684/s/gpu LR: 0.000175 Logit Scale: 25.282 Contrastive_loss: 0.35343 (0.27721) Loss: 0.35343 (0.27721) 2025-03-19,05:02:02 | INFO | Train Epoch: 7 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 153.767/s, 153.767/s/gpu LR: 0.000175 Logit Scale: 25.276 Contrastive_loss: 0.33831 (0.27854) Loss: 0.33831 (0.27854) 2025-03-19,05:02:24 | INFO | Train Epoch: 7 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 148.917/s, 148.917/s/gpu LR: 0.000175 Logit Scale: 25.273 Contrastive_loss: 0.18155 (0.27647) Loss: 0.18155 (0.27647) 2025-03-19,05:02:45 | INFO | Train Epoch: 7 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 141.080/s, 141.080/s/gpu LR: 0.000175 Logit Scale: 25.278 Contrastive_loss: 0.090829 (0.27261) Loss: 0.090829 (0.27261) 2025-03-19,05:03:07 | INFO | Train Epoch: 7 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 148.058/s, 148.058/s/gpu LR: 0.000175 Logit Scale: 25.262 Contrastive_loss: 0.040027 (0.26786) Loss: 0.040027 (0.26786) 2025-03-19,05:03:29 | INFO | Train Epoch: 7 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 148.394/s, 148.394/s/gpu LR: 0.000175 Logit Scale: 25.245 Contrastive_loss: 0.12716 (0.26504) Loss: 0.12716 (0.26504) 2025-03-19,05:03:51 | INFO | Train Epoch: 7 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.221, 147.410/s, 147.410/s/gpu LR: 0.000175 Logit Scale: 25.264 Contrastive_loss: 0.19266 (0.26363) Loss: 0.19266 (0.26363) 2025-03-19,05:04:13 | INFO | Train Epoch: 7 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.218, 148.217/s, 148.217/s/gpu LR: 0.000175 Logit Scale: 25.271 Contrastive_loss: 0.10276 (0.26053) Loss: 0.10276 (0.26053) 2025-03-19,05:04:34 | INFO | Train Epoch: 7 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.217, 148.161/s, 148.161/s/gpu LR: 0.000175 Logit Scale: 25.301 Contrastive_loss: 0.26700 (0.26065) Loss: 0.26700 (0.26065) 2025-03-19,05:04:56 | INFO | Train Epoch: 7 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.218, 144.537/s, 144.537/s/gpu LR: 0.000175 Logit Scale: 25.292 Contrastive_loss: 0.48475 (0.26480) Loss: 0.48475 (0.26480) 2025-03-19,05:05:18 | INFO | Train Epoch: 7 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 149.206/s, 149.206/s/gpu LR: 0.000175 Logit Scale: 25.246 Contrastive_loss: 0.27699 (0.26503) Loss: 0.27699 (0.26503) 2025-03-19,05:05:39 | INFO | Train Epoch: 7 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.355/s, 149.355/s/gpu LR: 0.000175 Logit Scale: 25.249 Contrastive_loss: 0.27884 (0.26527) Loss: 0.27884 (0.26527) 2025-03-19,05:06:01 | INFO | Train Epoch: 7 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 147.867/s, 147.867/s/gpu LR: 0.000175 Logit Scale: 25.222 Contrastive_loss: 0.17766 (0.26374) Loss: 0.17766 (0.26374) 2025-03-19,05:06:22 | INFO | Train Epoch: 7 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 151.207/s, 151.207/s/gpu LR: 0.000175 Logit Scale: 25.226 Contrastive_loss: 0.013554 (0.25942) Loss: 0.013554 (0.25942) 2025-03-19,05:06:44 | INFO | Train Epoch: 7 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.212, 146.412/s, 146.412/s/gpu LR: 0.000175 Logit Scale: 25.230 Contrastive_loss: 0.14308 (0.25745) Loss: 0.14308 (0.25745) 2025-03-19,05:07:06 | INFO | Train Epoch: 7 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.220, 142.588/s, 142.588/s/gpu LR: 0.000175 Logit Scale: 25.252 Contrastive_loss: 0.39265 (0.25970) Loss: 0.39265 (0.25970) 2025-03-19,05:07:27 | INFO | Train Epoch: 7 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 148.900/s, 148.900/s/gpu LR: 0.000175 Logit Scale: 25.237 Contrastive_loss: 0.52313 (0.26402) Loss: 0.52313 (0.26402) 2025-03-19,05:07:49 | INFO | Train Epoch: 7 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 146.541/s, 146.541/s/gpu LR: 0.000175 Logit Scale: 25.222 Contrastive_loss: 0.49662 (0.26777) Loss: 0.49662 (0.26777) 2025-03-19,05:08:11 | INFO | Train Epoch: 7 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.219, 146.127/s, 146.127/s/gpu LR: 0.000175 Logit Scale: 25.253 Contrastive_loss: 0.38710 (0.26967) Loss: 0.38710 (0.26967) 2025-03-19,05:08:33 | INFO | Train Epoch: 7 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.219, 145.715/s, 145.715/s/gpu LR: 0.000175 Logit Scale: 25.255 Contrastive_loss: 0.31985 (0.27045) Loss: 0.31985 (0.27045) 2025-03-19,05:08:55 | INFO | Train Epoch: 7 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.221, 149.634/s, 149.634/s/gpu LR: 0.000175 Logit Scale: 25.220 Contrastive_loss: 0.25380 (0.27020) Loss: 0.25380 (0.27020) 2025-03-19,05:09:17 | INFO | Train Epoch: 7 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.218, 149.381/s, 149.381/s/gpu LR: 0.000175 Logit Scale: 25.196 Contrastive_loss: 0.36743 (0.27167) Loss: 0.36743 (0.27167) 2025-03-19,05:09:38 | INFO | Train Epoch: 7 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 151.067/s, 151.067/s/gpu LR: 0.000175 Logit Scale: 25.196 Contrastive_loss: 0.27878 (0.27177) Loss: 0.27878 (0.27177) 2025-03-19,05:10:00 | INFO | Train Epoch: 7 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.103/s, 148.103/s/gpu LR: 0.000175 Logit Scale: 25.176 Contrastive_loss: 0.23861 (0.27129) Loss: 0.23861 (0.27129) 2025-03-19,05:10:22 | INFO | Train Epoch: 7 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.217, 150.353/s, 150.353/s/gpu LR: 0.000175 Logit Scale: 25.205 Contrastive_loss: 0.21265 (0.27044) Loss: 0.21265 (0.27044) 2025-03-19,05:10:43 | INFO | Train Epoch: 7 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 150.149/s, 150.149/s/gpu LR: 0.000175 Logit Scale: 25.223 Contrastive_loss: 0.066703 (0.26753) Loss: 0.066703 (0.26753) 2025-03-19,05:11:05 | INFO | Train Epoch: 7 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 146.618/s, 146.618/s/gpu LR: 0.000175 Logit Scale: 25.224 Contrastive_loss: 0.27426 (0.26762) Loss: 0.27426 (0.26762) 2025-03-19,05:11:26 | INFO | Train Epoch: 7 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 147.147/s, 147.147/s/gpu LR: 0.000174 Logit Scale: 25.221 Contrastive_loss: 0.16129 (0.26614) Loss: 0.16129 (0.26614) 2025-03-19,05:11:48 | INFO | Train Epoch: 7 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 151.308/s, 151.308/s/gpu LR: 0.000174 Logit Scale: 25.239 Contrastive_loss: 0.21736 (0.26548) Loss: 0.21736 (0.26548) 2025-03-19,05:12:09 | INFO | Train Epoch: 7 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 147.938/s, 147.938/s/gpu LR: 0.000174 Logit Scale: 25.238 Contrastive_loss: 0.42428 (0.26762) Loss: 0.42428 (0.26762) 2025-03-19,05:12:31 | INFO | Train Epoch: 7 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 146.796/s, 146.796/s/gpu LR: 0.000174 Logit Scale: 25.241 Contrastive_loss: 0.39047 (0.26926) Loss: 0.39047 (0.26926) 2025-03-19,05:12:52 | INFO | Train Epoch: 7 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 147.536/s, 147.536/s/gpu LR: 0.000174 Logit Scale: 25.227 Contrastive_loss: 0.099352 (0.26703) Loss: 0.099352 (0.26703) 2025-03-19,05:13:14 | INFO | Train Epoch: 7 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.218, 85.4998/s, 85.4998/s/gpu LR: 0.000174 Logit Scale: 25.205 Contrastive_loss: 0.11821 (0.26509) Loss: 0.11821 (0.26509) 2025-03-19,05:13:36 | INFO | Train Epoch: 7 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 149.380/s, 149.380/s/gpu LR: 0.000174 Logit Scale: 25.246 Contrastive_loss: 0.35039 (0.26619) Loss: 0.35039 (0.26619) 2025-03-19,05:13:57 | INFO | Train Epoch: 7 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.155/s, 149.155/s/gpu LR: 0.000174 Logit Scale: 25.271 Contrastive_loss: 0.19381 (0.26527) Loss: 0.19381 (0.26527) 2025-03-19,05:14:19 | INFO | Train Epoch: 7 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.329/s, 149.329/s/gpu LR: 0.000174 Logit Scale: 25.193 Contrastive_loss: 0.26087 (0.26521) Loss: 0.26087 (0.26521) 2025-03-19,05:14:40 | INFO | Train Epoch: 7 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 150.327/s, 150.327/s/gpu LR: 0.000174 Logit Scale: 25.230 Contrastive_loss: 0.39574 (0.26683) Loss: 0.39574 (0.26683) 2025-03-19,05:15:02 | INFO | Train Epoch: 7 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 148.997/s, 148.997/s/gpu LR: 0.000174 Logit Scale: 25.219 Contrastive_loss: 0.44073 (0.26895) Loss: 0.44073 (0.26895) 2025-03-19,05:15:24 | INFO | Train Epoch: 7 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.220, 145.834/s, 145.834/s/gpu LR: 0.000174 Logit Scale: 25.247 Contrastive_loss: 0.11271 (0.26706) Loss: 0.11271 (0.26706) 2025-03-19,05:15:46 | INFO | Train Epoch: 7 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.219, 148.388/s, 148.388/s/gpu LR: 0.000174 Logit Scale: 25.229 Contrastive_loss: 0.14112 (0.26557) Loss: 0.14112 (0.26557) 2025-03-19,05:16:08 | INFO | Train Epoch: 7 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.219, 148.858/s, 148.858/s/gpu LR: 0.000174 Logit Scale: 25.196 Contrastive_loss: 0.089147 (0.26349) Loss: 0.089147 (0.26349) 2025-03-19,05:16:29 | INFO | Train Epoch: 7 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.218, 148.506/s, 148.506/s/gpu LR: 0.000174 Logit Scale: 25.167 Contrastive_loss: 0.48691 (0.26609) Loss: 0.48691 (0.26609) 2025-03-19,05:16:51 | INFO | Train Epoch: 7 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 147.905/s, 147.905/s/gpu LR: 0.000174 Logit Scale: 25.165 Contrastive_loss: 0.48050 (0.26855) Loss: 0.48050 (0.26855) 2025-03-19,05:17:13 | INFO | Train Epoch: 7 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 146.374/s, 146.374/s/gpu LR: 0.000174 Logit Scale: 25.175 Contrastive_loss: 0.51897 (0.27140) Loss: 0.51897 (0.27140) 2025-03-19,05:17:34 | INFO | Train Epoch: 7 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 149.310/s, 149.310/s/gpu LR: 0.000174 Logit Scale: 25.217 Contrastive_loss: 0.12620 (0.26977) Loss: 0.12620 (0.26977) 2025-03-19,05:17:56 | INFO | Train Epoch: 7 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.218, 146.235/s, 146.235/s/gpu LR: 0.000174 Logit Scale: 25.175 Contrastive_loss: 0.11926 (0.26809) Loss: 0.11926 (0.26809) 2025-03-19,05:18:18 | INFO | Train Epoch: 7 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.219, 146.978/s, 146.978/s/gpu LR: 0.000174 Logit Scale: 25.189 Contrastive_loss: 0.18170 (0.26714) Loss: 0.18170 (0.26714) 2025-03-19,05:18:40 | INFO | Train Epoch: 7 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.222, 141.252/s, 141.252/s/gpu LR: 0.000174 Logit Scale: 25.203 Contrastive_loss: 0.17028 (0.26609) Loss: 0.17028 (0.26609) 2025-03-19,05:19:01 | INFO | Train Epoch: 7 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 151.358/s, 151.358/s/gpu LR: 0.000174 Logit Scale: 25.192 Contrastive_loss: 0.31857 (0.26666) Loss: 0.31857 (0.26666) 2025-03-19,05:19:23 | INFO | Train Epoch: 7 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 147.791/s, 147.791/s/gpu LR: 0.000174 Logit Scale: 25.210 Contrastive_loss: 0.21933 (0.26615) Loss: 0.21933 (0.26615) 2025-03-19,05:19:45 | INFO | Train Epoch: 7 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 151.713/s, 151.713/s/gpu LR: 0.000174 Logit Scale: 25.262 Contrastive_loss: 0.35249 (0.26706) Loss: 0.35249 (0.26706) 2025-03-19,05:20:06 | INFO | Train Epoch: 7 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.212, 150.232/s, 150.232/s/gpu LR: 0.000174 Logit Scale: 25.214 Contrastive_loss: 0.24678 (0.26685) Loss: 0.24678 (0.26685) 2025-03-19,05:20:27 | INFO | Train Epoch: 7 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 148.059/s, 148.059/s/gpu LR: 0.000174 Logit Scale: 25.248 Contrastive_loss: 0.083790 (0.26496) Loss: 0.083790 (0.26496) 2025-03-19,05:20:49 | INFO | Train Epoch: 7 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 148.288/s, 148.288/s/gpu LR: 0.000174 Logit Scale: 25.236 Contrastive_loss: 0.37815 (0.26612) Loss: 0.37815 (0.26612) 2025-03-19,05:21:11 | INFO | Train Epoch: 7 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 148.904/s, 148.904/s/gpu LR: 0.000174 Logit Scale: 25.220 Contrastive_loss: 0.14057 (0.26485) Loss: 0.14057 (0.26485) 2025-03-19,05:21:33 | INFO | Train Epoch: 7 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 149.397/s, 149.397/s/gpu LR: 0.000174 Logit Scale: 25.224 Contrastive_loss: 0.28122 (0.26501) Loss: 0.28122 (0.26501) 2025-03-19,05:21:54 | INFO | Train Epoch: 7 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 148.598/s, 148.598/s/gpu LR: 0.000174 Logit Scale: 25.179 Contrastive_loss: 0.33181 (0.26568) Loss: 0.33181 (0.26568) 2025-03-19,05:22:16 | INFO | Train Epoch: 7 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 146.644/s, 146.644/s/gpu LR: 0.000174 Logit Scale: 25.186 Contrastive_loss: 0.23905 (0.26541) Loss: 0.23905 (0.26541) 2025-03-19,05:22:38 | INFO | Train Epoch: 7 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 146.026/s, 146.026/s/gpu LR: 0.000174 Logit Scale: 25.234 Contrastive_loss: 0.24708 (0.26524) Loss: 0.24708 (0.26524) 2025-03-19,05:22:59 | INFO | Train Epoch: 7 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 148.647/s, 148.647/s/gpu LR: 0.000174 Logit Scale: 25.240 Contrastive_loss: 0.081747 (0.26347) Loss: 0.081747 (0.26347) 2025-03-19,05:23:21 | INFO | Train Epoch: 7 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.895/s, 149.895/s/gpu LR: 0.000173 Logit Scale: 25.218 Contrastive_loss: 0.14642 (0.26236) Loss: 0.14642 (0.26236) 2025-03-19,05:23:43 | INFO | Train Epoch: 7 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 147.808/s, 147.808/s/gpu LR: 0.000173 Logit Scale: 25.222 Contrastive_loss: 0.56916 (0.26525) Loss: 0.56916 (0.26525) 2025-03-19,05:24:04 | INFO | Train Epoch: 7 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.422/s, 148.422/s/gpu LR: 0.000173 Logit Scale: 25.252 Contrastive_loss: 0.37327 (0.26626) Loss: 0.37327 (0.26626) 2025-03-19,05:24:26 | INFO | Train Epoch: 7 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 147.896/s, 147.896/s/gpu LR: 0.000173 Logit Scale: 25.261 Contrastive_loss: 0.25526 (0.26616) Loss: 0.25526 (0.26616) 2025-03-19,05:24:47 | INFO | Train Epoch: 7 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 148.538/s, 148.538/s/gpu LR: 0.000173 Logit Scale: 25.221 Contrastive_loss: 0.27552 (0.26624) Loss: 0.27552 (0.26624) 2025-03-19,05:25:09 | INFO | Train Epoch: 7 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.144/s, 149.144/s/gpu LR: 0.000173 Logit Scale: 25.200 Contrastive_loss: 0.28363 (0.26640) Loss: 0.28363 (0.26640) 2025-03-19,05:25:30 | INFO | Train Epoch: 7 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.143/s, 149.143/s/gpu LR: 0.000173 Logit Scale: 25.251 Contrastive_loss: 0.42709 (0.26785) Loss: 0.42709 (0.26785) 2025-03-19,05:25:52 | INFO | Train Epoch: 7 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 148.843/s, 148.843/s/gpu LR: 0.000173 Logit Scale: 25.256 Contrastive_loss: 0.20228 (0.26726) Loss: 0.20228 (0.26726) 2025-03-19,05:26:13 | INFO | Train Epoch: 7 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 150.732/s, 150.732/s/gpu LR: 0.000173 Logit Scale: 25.208 Contrastive_loss: 0.20657 (0.26673) Loss: 0.20657 (0.26673) 2025-03-19,05:26:35 | INFO | Train Epoch: 7 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 145.998/s, 145.998/s/gpu LR: 0.000173 Logit Scale: 25.208 Contrastive_loss: 0.10945 (0.26535) Loss: 0.10945 (0.26535) 2025-03-19,05:26:57 | INFO | Train Epoch: 7 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 147.325/s, 147.325/s/gpu LR: 0.000173 Logit Scale: 25.243 Contrastive_loss: 0.35113 (0.26609) Loss: 0.35113 (0.26609) 2025-03-19,05:27:18 | INFO | Train Epoch: 7 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 146.324/s, 146.324/s/gpu LR: 0.000173 Logit Scale: 25.257 Contrastive_loss: 0.071998 (0.26442) Loss: 0.071998 (0.26442) 2025-03-19,05:27:40 | INFO | Train Epoch: 7 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 148.683/s, 148.683/s/gpu LR: 0.000173 Logit Scale: 25.246 Contrastive_loss: 0.18184 (0.26372) Loss: 0.18184 (0.26372) 2025-03-19,05:28:01 | INFO | Train Epoch: 7 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 150.177/s, 150.177/s/gpu LR: 0.000173 Logit Scale: 25.260 Contrastive_loss: 0.16211 (0.26285) Loss: 0.16211 (0.26285) 2025-03-19,05:28:23 | INFO | Train Epoch: 7 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.213, 150.268/s, 150.268/s/gpu LR: 0.000173 Logit Scale: 25.288 Contrastive_loss: 0.047109 (0.26104) Loss: 0.047109 (0.26104) 2025-03-19,05:28:45 | INFO | Train Epoch: 7 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.220, 144.673/s, 144.673/s/gpu LR: 0.000173 Logit Scale: 25.312 Contrastive_loss: 0.19541 (0.26049) Loss: 0.19541 (0.26049) 2025-03-19,05:29:07 | INFO | Train Epoch: 7 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.220, 146.118/s, 146.118/s/gpu LR: 0.000173 Logit Scale: 25.299 Contrastive_loss: 0.15079 (0.25959) Loss: 0.15079 (0.25959) 2025-03-19,05:29:29 | INFO | Train Epoch: 7 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.219, 145.513/s, 145.513/s/gpu LR: 0.000173 Logit Scale: 25.262 Contrastive_loss: 0.12467 (0.25848) Loss: 0.12467 (0.25848) 2025-03-19,05:29:51 | INFO | Train Epoch: 7 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.220, 146.350/s, 146.350/s/gpu LR: 0.000173 Logit Scale: 25.280 Contrastive_loss: 0.27115 (0.25858) Loss: 0.27115 (0.25858) 2025-03-19,05:30:13 | INFO | Train Epoch: 7 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.220, 145.577/s, 145.577/s/gpu LR: 0.000173 Logit Scale: 25.296 Contrastive_loss: 0.39079 (0.25965) Loss: 0.39079 (0.25965) 2025-03-19,05:30:35 | INFO | Train Epoch: 7 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.220, 145.626/s, 145.626/s/gpu LR: 0.000173 Logit Scale: 25.250 Contrastive_loss: 0.63730 (0.26267) Loss: 0.63730 (0.26267) 2025-03-19,05:30:57 | INFO | Train Epoch: 7 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.220, 145.014/s, 145.014/s/gpu LR: 0.000173 Logit Scale: 25.223 Contrastive_loss: 0.43629 (0.26405) Loss: 0.43629 (0.26405) 2025-03-19,05:31:19 | INFO | Train Epoch: 7 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.222, 142.780/s, 142.780/s/gpu LR: 0.000173 Logit Scale: 25.229 Contrastive_loss: 0.16964 (0.26331) Loss: 0.16964 (0.26331) 2025-03-19,05:31:41 | INFO | Train Epoch: 7 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 149.108/s, 149.108/s/gpu LR: 0.000173 Logit Scale: 25.269 Contrastive_loss: 0.24574 (0.26317) Loss: 0.24574 (0.26317) 2025-03-19,05:32:02 | INFO | Train Epoch: 7 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 149.123/s, 149.123/s/gpu LR: 0.000173 Logit Scale: 25.271 Contrastive_loss: 0.39729 (0.26421) Loss: 0.39729 (0.26421) 2025-03-19,05:32:24 | INFO | Train Epoch: 7 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 151.773/s, 151.773/s/gpu LR: 0.000173 Logit Scale: 25.249 Contrastive_loss: 0.17005 (0.26348) Loss: 0.17005 (0.26348) 2025-03-19,05:32:45 | INFO | Train Epoch: 7 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 148.562/s, 148.562/s/gpu LR: 0.000173 Logit Scale: 25.242 Contrastive_loss: 0.21961 (0.26315) Loss: 0.21961 (0.26315) 2025-03-19,05:33:07 | INFO | Train Epoch: 7 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 148.176/s, 148.176/s/gpu LR: 0.000173 Logit Scale: 25.243 Contrastive_loss: 0.35211 (0.26382) Loss: 0.35211 (0.26382) 2025-03-19,05:33:28 | INFO | Train Epoch: 7 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 148.101/s, 148.101/s/gpu LR: 0.000173 Logit Scale: 25.220 Contrastive_loss: 0.27971 (0.26394) Loss: 0.27971 (0.26394) 2025-03-19,05:33:50 | INFO | Train Epoch: 7 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 148.067/s, 148.067/s/gpu LR: 0.000173 Logit Scale: 25.254 Contrastive_loss: 0.68275 (0.26707) Loss: 0.68275 (0.26707) 2025-03-19,05:34:11 | INFO | Train Epoch: 7 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 149.586/s, 149.586/s/gpu LR: 0.000173 Logit Scale: 25.144 Contrastive_loss: 0.43478 (0.26831) Loss: 0.43478 (0.26831) 2025-03-19,05:34:33 | INFO | Train Epoch: 7 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 148.286/s, 148.286/s/gpu LR: 0.000173 Logit Scale: 25.188 Contrastive_loss: 0.50234 (0.27003) Loss: 0.50234 (0.27003) 2025-03-19,05:34:54 | INFO | Train Epoch: 7 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 152.104/s, 152.104/s/gpu LR: 0.000173 Logit Scale: 25.230 Contrastive_loss: 0.59367 (0.27239) Loss: 0.59367 (0.27239) 2025-03-19,05:35:15 | INFO | Train Epoch: 7 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.213, 181.008/s, 181.008/s/gpu LR: 0.000172 Logit Scale: 25.207 Contrastive_loss: 0.55663 (0.27445) Loss: 0.55663 (0.27445) 2025-03-19,05:35:37 | INFO | Train Epoch: 7 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.944/s, 147.944/s/gpu LR: 0.000172 Logit Scale: 25.240 Contrastive_loss: 0.33879 (0.27492) Loss: 0.33879 (0.27492) 2025-03-19,05:35:59 | INFO | Train Epoch: 7 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 147.523/s, 147.523/s/gpu LR: 0.000172 Logit Scale: 25.271 Contrastive_loss: 0.41284 (0.27590) Loss: 0.41284 (0.27590) 2025-03-19,05:36:20 | INFO | Train Epoch: 7 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 148.280/s, 148.280/s/gpu LR: 0.000172 Logit Scale: 25.293 Contrastive_loss: 0.21237 (0.27545) Loss: 0.21237 (0.27545) 2025-03-19,05:36:42 | INFO | Train Epoch: 7 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 149.825/s, 149.825/s/gpu LR: 0.000172 Logit Scale: 25.265 Contrastive_loss: 0.083613 (0.27410) Loss: 0.083613 (0.27410) 2025-03-19,05:37:03 | INFO | Train Epoch: 7 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.779/s, 149.779/s/gpu LR: 0.000172 Logit Scale: 25.265 Contrastive_loss: 0.065825 (0.27264) Loss: 0.065825 (0.27264) 2025-03-19,05:37:25 | INFO | Train Epoch: 7 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 149.130/s, 149.130/s/gpu LR: 0.000172 Logit Scale: 25.304 Contrastive_loss: 0.11451 (0.27155) Loss: 0.11451 (0.27155) 2025-03-19,05:37:46 | INFO | Train Epoch: 7 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 147.564/s, 147.564/s/gpu LR: 0.000172 Logit Scale: 25.325 Contrastive_loss: 0.12170 (0.27051) Loss: 0.12170 (0.27051) 2025-03-19,05:38:08 | INFO | Train Epoch: 7 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 149.856/s, 149.856/s/gpu LR: 0.000172 Logit Scale: 25.292 Contrastive_loss: 0.39793 (0.27138) Loss: 0.39793 (0.27138) 2025-03-19,05:38:29 | INFO | Train Epoch: 7 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 151.610/s, 151.610/s/gpu LR: 0.000172 Logit Scale: 25.303 Contrastive_loss: 0.19622 (0.27087) Loss: 0.19622 (0.27087) 2025-03-19,05:38:51 | INFO | Train Epoch: 7 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 147.176/s, 147.176/s/gpu LR: 0.000172 Logit Scale: 25.257 Contrastive_loss: 0.21657 (0.27051) Loss: 0.21657 (0.27051) 2025-03-19,05:39:13 | INFO | Train Epoch: 7 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.218, 144.929/s, 144.929/s/gpu LR: 0.000172 Logit Scale: 25.254 Contrastive_loss: 0.16629 (0.26981) Loss: 0.16629 (0.26981) 2025-03-19,05:39:35 | INFO | Train Epoch: 7 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.218, 147.051/s, 147.051/s/gpu LR: 0.000172 Logit Scale: 25.262 Contrastive_loss: 0.29363 (0.26997) Loss: 0.29363 (0.26997) 2025-03-19,05:39:56 | INFO | Train Epoch: 7 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.217, 149.015/s, 149.015/s/gpu LR: 0.000172 Logit Scale: 25.270 Contrastive_loss: 0.17996 (0.26937) Loss: 0.17996 (0.26937) 2025-03-19,05:40:18 | INFO | Train Epoch: 7 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.576/s, 148.576/s/gpu LR: 0.000172 Logit Scale: 25.243 Contrastive_loss: 0.60942 (0.27161) Loss: 0.60942 (0.27161) 2025-03-19,05:40:39 | INFO | Train Epoch: 7 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 148.771/s, 148.771/s/gpu LR: 0.000172 Logit Scale: 25.282 Contrastive_loss: 0.25824 (0.27152) Loss: 0.25824 (0.27152) 2025-03-19,05:41:01 | INFO | Train Epoch: 7 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 144.105/s, 144.105/s/gpu LR: 0.000172 Logit Scale: 25.287 Contrastive_loss: 0.28667 (0.27162) Loss: 0.28667 (0.27162) 2025-03-19,05:41:23 | INFO | Train Epoch: 7 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.219, 145.333/s, 145.333/s/gpu LR: 0.000172 Logit Scale: 25.295 Contrastive_loss: 0.20980 (0.27122) Loss: 0.20980 (0.27122) 2025-03-19,05:41:45 | INFO | Train Epoch: 7 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.220, 145.880/s, 145.880/s/gpu LR: 0.000172 Logit Scale: 25.350 Contrastive_loss: 0.15521 (0.27048) Loss: 0.15521 (0.27048) 2025-03-19,05:42:07 | INFO | Train Epoch: 7 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 149.242/s, 149.242/s/gpu LR: 0.000172 Logit Scale: 25.323 Contrastive_loss: 0.22252 (0.27017) Loss: 0.22252 (0.27017) 2025-03-19,05:42:28 | INFO | Train Epoch: 7 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 147.301/s, 147.301/s/gpu LR: 0.000172 Logit Scale: 25.337 Contrastive_loss: 0.16923 (0.26953) Loss: 0.16923 (0.26953) 2025-03-19,05:42:50 | INFO | Train Epoch: 7 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.021/s, 149.021/s/gpu LR: 0.000172 Logit Scale: 25.348 Contrastive_loss: 0.47568 (0.27083) Loss: 0.47568 (0.27083) 2025-03-19,05:43:11 | INFO | Train Epoch: 7 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 145.343/s, 145.343/s/gpu LR: 0.000172 Logit Scale: 25.319 Contrastive_loss: 0.48225 (0.27215) Loss: 0.48225 (0.27215) 2025-03-19,05:43:33 | INFO | Train Epoch: 7 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.220, 146.330/s, 146.330/s/gpu LR: 0.000172 Logit Scale: 25.358 Contrastive_loss: 0.30412 (0.27235) Loss: 0.30412 (0.27235) 2025-03-19,05:43:55 | INFO | Train Epoch: 7 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.219, 146.683/s, 146.683/s/gpu LR: 0.000172 Logit Scale: 25.298 Contrastive_loss: 0.65321 (0.27470) Loss: 0.65321 (0.27470) 2025-03-19,05:44:17 | INFO | Train Epoch: 7 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.219, 144.612/s, 144.612/s/gpu LR: 0.000172 Logit Scale: 25.256 Contrastive_loss: 0.57738 (0.27656) Loss: 0.57738 (0.27656) 2025-03-19,05:44:39 | INFO | Train Epoch: 7 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.219, 150.909/s, 150.909/s/gpu LR: 0.000172 Logit Scale: 25.309 Contrastive_loss: 0.13882 (0.27572) Loss: 0.13882 (0.27572) 2025-03-19,05:45:01 | INFO | Train Epoch: 7 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 145.101/s, 145.101/s/gpu LR: 0.000172 Logit Scale: 25.329 Contrastive_loss: 0.12176 (0.27478) Loss: 0.12176 (0.27478) 2025-03-19,05:45:22 | INFO | Train Epoch: 7 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 146.597/s, 146.597/s/gpu LR: 0.000172 Logit Scale: 25.317 Contrastive_loss: 0.058429 (0.27348) Loss: 0.058429 (0.27348) 2025-03-19,05:45:44 | INFO | Train Epoch: 7 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 146.326/s, 146.326/s/gpu LR: 0.000172 Logit Scale: 25.284 Contrastive_loss: 0.19427 (0.27301) Loss: 0.19427 (0.27301) 2025-03-19,05:46:06 | INFO | Train Epoch: 7 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 151.464/s, 151.464/s/gpu LR: 0.000172 Logit Scale: 25.316 Contrastive_loss: 0.32099 (0.27329) Loss: 0.32099 (0.27329) 2025-03-19,05:46:28 | INFO | Train Epoch: 7 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.220, 146.397/s, 146.397/s/gpu LR: 0.000172 Logit Scale: 25.275 Contrastive_loss: 0.33673 (0.27367) Loss: 0.33673 (0.27367) 2025-03-19,05:46:49 | INFO | Train Epoch: 7 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.213, 152.001/s, 152.001/s/gpu LR: 0.000172 Logit Scale: 25.264 Contrastive_loss: 0.31321 (0.27390) Loss: 0.31321 (0.27390) 2025-03-19,05:47:11 | INFO | Train Epoch: 7 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 146.151/s, 146.151/s/gpu LR: 0.000171 Logit Scale: 25.319 Contrastive_loss: 0.16509 (0.27326) Loss: 0.16509 (0.27326) 2025-03-19,05:47:33 | INFO | Train Epoch: 7 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 146.455/s, 146.455/s/gpu LR: 0.000171 Logit Scale: 25.296 Contrastive_loss: 0.18533 (0.27275) Loss: 0.18533 (0.27275) 2025-03-19,05:47:54 | INFO | Train Epoch: 7 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 145.857/s, 145.857/s/gpu LR: 0.000171 Logit Scale: 25.342 Contrastive_loss: 0.10421 (0.27178) Loss: 0.10421 (0.27178) 2025-03-19,05:48:16 | INFO | Train Epoch: 7 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 146.450/s, 146.450/s/gpu LR: 0.000171 Logit Scale: 25.313 Contrastive_loss: 0.30540 (0.27197) Loss: 0.30540 (0.27197) 2025-03-19,05:48:38 | INFO | Train Epoch: 7 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 145.130/s, 145.130/s/gpu LR: 0.000171 Logit Scale: 25.293 Contrastive_loss: 0.085202 (0.27090) Loss: 0.085202 (0.27090) 2025-03-19,05:49:00 | INFO | Train Epoch: 7 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.221, 143.583/s, 143.583/s/gpu LR: 0.000171 Logit Scale: 25.282 Contrastive_loss: 0.29704 (0.27105) Loss: 0.29704 (0.27105) 2025-03-19,05:49:22 | INFO | Train Epoch: 7 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.221, 146.505/s, 146.505/s/gpu LR: 0.000171 Logit Scale: 25.285 Contrastive_loss: 0.24832 (0.27092) Loss: 0.24832 (0.27092) 2025-03-19,05:49:44 | INFO | Train Epoch: 7 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 144.858/s, 144.858/s/gpu LR: 0.000171 Logit Scale: 25.241 Contrastive_loss: 0.60359 (0.27279) Loss: 0.60359 (0.27279) 2025-03-19,05:50:06 | INFO | Train Epoch: 7 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 145.892/s, 145.892/s/gpu LR: 0.000171 Logit Scale: 25.309 Contrastive_loss: 0.41279 (0.27357) Loss: 0.41279 (0.27357) 2025-03-19,05:50:28 | INFO | Train Epoch: 7 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.755/s, 149.755/s/gpu LR: 0.000171 Logit Scale: 25.243 Contrastive_loss: 0.17616 (0.27303) Loss: 0.17616 (0.27303) 2025-03-19,05:50:49 | INFO | Train Epoch: 7 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.731/s, 149.731/s/gpu LR: 0.000171 Logit Scale: 25.223 Contrastive_loss: 0.16822 (0.27245) Loss: 0.16822 (0.27245) 2025-03-19,05:51:11 | INFO | Train Epoch: 7 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.855/s, 149.855/s/gpu LR: 0.000171 Logit Scale: 25.239 Contrastive_loss: 0.10368 (0.27153) Loss: 0.10368 (0.27153) 2025-03-19,05:51:32 | INFO | Train Epoch: 7 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 150.639/s, 150.639/s/gpu LR: 0.000171 Logit Scale: 25.276 Contrastive_loss: 0.17061 (0.27097) Loss: 0.17061 (0.27097) 2025-03-19,05:51:54 | INFO | Train Epoch: 7 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 149.285/s, 149.285/s/gpu LR: 0.000171 Logit Scale: 25.220 Contrastive_loss: 0.14213 (0.27027) Loss: 0.14213 (0.27027) 2025-03-19,05:52:15 | INFO | Train Epoch: 7 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 147.584/s, 147.584/s/gpu LR: 0.000171 Logit Scale: 25.245 Contrastive_loss: 0.33248 (0.27061) Loss: 0.33248 (0.27061) 2025-03-19,05:52:37 | INFO | Train Epoch: 7 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 147.719/s, 147.719/s/gpu LR: 0.000171 Logit Scale: 25.262 Contrastive_loss: 0.46649 (0.27166) Loss: 0.46649 (0.27166) 2025-03-19,05:52:58 | INFO | Train Epoch: 7 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 147.746/s, 147.746/s/gpu LR: 0.000171 Logit Scale: 25.310 Contrastive_loss: 0.39061 (0.27230) Loss: 0.39061 (0.27230) 2025-03-19,05:53:20 | INFO | Train Epoch: 7 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 146.661/s, 146.661/s/gpu LR: 0.000171 Logit Scale: 25.318 Contrastive_loss: 0.48711 (0.27344) Loss: 0.48711 (0.27344) 2025-03-19,05:53:42 | INFO | Train Epoch: 7 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 144.229/s, 144.229/s/gpu LR: 0.000171 Logit Scale: 25.333 Contrastive_loss: 0.60625 (0.27520) Loss: 0.60625 (0.27520) 2025-03-19,05:54:03 | INFO | Train Epoch: 7 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 146.525/s, 146.525/s/gpu LR: 0.000171 Logit Scale: 25.353 Contrastive_loss: 0.28485 (0.27525) Loss: 0.28485 (0.27525) 2025-03-19,05:54:25 | INFO | Train Epoch: 7 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.219, 147.289/s, 147.289/s/gpu LR: 0.000171 Logit Scale: 25.311 Contrastive_loss: 0.29687 (0.27537) Loss: 0.29687 (0.27537) 2025-03-19,05:54:47 | INFO | Train Epoch: 7 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 151.206/s, 151.206/s/gpu LR: 0.000171 Logit Scale: 25.257 Contrastive_loss: 0.18611 (0.27490) Loss: 0.18611 (0.27490) 2025-03-19,05:55:08 | INFO | Train Epoch: 7 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 148.407/s, 148.407/s/gpu LR: 0.000171 Logit Scale: 25.284 Contrastive_loss: 0.51458 (0.27614) Loss: 0.51458 (0.27614) 2025-03-19,05:55:30 | INFO | Train Epoch: 7 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 147.578/s, 147.578/s/gpu LR: 0.000171 Logit Scale: 25.300 Contrastive_loss: 0.35211 (0.27654) Loss: 0.35211 (0.27654) 2025-03-19,05:55:51 | INFO | Train Epoch: 7 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 148.768/s, 148.768/s/gpu LR: 0.000171 Logit Scale: 25.294 Contrastive_loss: 0.087889 (0.27557) Loss: 0.087889 (0.27557) 2025-03-19,05:56:13 | INFO | Train Epoch: 7 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.896/s, 148.896/s/gpu LR: 0.000171 Logit Scale: 25.278 Contrastive_loss: 0.29432 (0.27566) Loss: 0.29432 (0.27566) 2025-03-19,05:56:34 | INFO | Train Epoch: 7 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 148.857/s, 148.857/s/gpu LR: 0.000171 Logit Scale: 25.247 Contrastive_loss: 0.25131 (0.27554) Loss: 0.25131 (0.27554) 2025-03-19,05:56:56 | INFO | Train Epoch: 7 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 148.723/s, 148.723/s/gpu LR: 0.000171 Logit Scale: 25.250 Contrastive_loss: 0.15316 (0.27492) Loss: 0.15316 (0.27492) 2025-03-19,05:57:17 | INFO | Train Epoch: 7 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 149.399/s, 149.399/s/gpu LR: 0.000171 Logit Scale: 25.271 Contrastive_loss: 0.45642 (0.27583) Loss: 0.45642 (0.27583) 2025-03-19,05:57:39 | INFO | Train Epoch: 7 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 147.747/s, 147.747/s/gpu LR: 0.000171 Logit Scale: 25.256 Contrastive_loss: 0.15674 (0.27524) Loss: 0.15674 (0.27524) 2025-03-19,05:58:01 | INFO | Train Epoch: 7 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 146.798/s, 146.798/s/gpu LR: 0.000171 Logit Scale: 25.279 Contrastive_loss: 0.49309 (0.27632) Loss: 0.49309 (0.27632) 2025-03-19,05:58:22 | INFO | Train Epoch: 7 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 147.554/s, 147.554/s/gpu LR: 0.000171 Logit Scale: 25.273 Contrastive_loss: 0.24330 (0.27616) Loss: 0.24330 (0.27616) 2025-03-19,05:58:44 | INFO | Train Epoch: 7 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.698/s, 148.698/s/gpu LR: 0.000170 Logit Scale: 25.298 Contrastive_loss: 0.33158 (0.27643) Loss: 0.33158 (0.27643) 2025-03-19,05:59:06 | INFO | Train Epoch: 7 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 147.783/s, 147.783/s/gpu LR: 0.000170 Logit Scale: 25.303 Contrastive_loss: 0.12622 (0.27570) Loss: 0.12622 (0.27570) 2025-03-19,05:59:27 | INFO | Train Epoch: 7 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 142.264/s, 142.264/s/gpu LR: 0.000170 Logit Scale: 25.301 Contrastive_loss: 0.11403 (0.27491) Loss: 0.11403 (0.27491) 2025-03-19,05:59:49 | INFO | Train Epoch: 7 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.757/s, 149.757/s/gpu LR: 0.000170 Logit Scale: 25.299 Contrastive_loss: 0.34169 (0.27523) Loss: 0.34169 (0.27523) 2025-03-19,06:00:10 | INFO | Train Epoch: 7 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 149.305/s, 149.305/s/gpu LR: 0.000170 Logit Scale: 25.291 Contrastive_loss: 0.39071 (0.27579) Loss: 0.39071 (0.27579) 2025-03-19,06:00:32 | INFO | Train Epoch: 7 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 150.356/s, 150.356/s/gpu LR: 0.000170 Logit Scale: 25.312 Contrastive_loss: 0.18007 (0.27533) Loss: 0.18007 (0.27533) 2025-03-19,06:00:53 | INFO | Train Epoch: 7 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 152.288/s, 152.288/s/gpu LR: 0.000170 Logit Scale: 25.301 Contrastive_loss: 0.10060 (0.27449) Loss: 0.10060 (0.27449) 2025-03-19,06:01:15 | INFO | Train Epoch: 7 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 147.973/s, 147.973/s/gpu LR: 0.000170 Logit Scale: 25.304 Contrastive_loss: 0.21113 (0.27419) Loss: 0.21113 (0.27419) 2025-03-19,06:01:36 | INFO | Train Epoch: 7 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 148.092/s, 148.092/s/gpu LR: 0.000170 Logit Scale: 25.330 Contrastive_loss: 0.072810 (0.27324) Loss: 0.072810 (0.27324) 2025-03-19,06:01:58 | INFO | Train Epoch: 7 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 148.586/s, 148.586/s/gpu LR: 0.000170 Logit Scale: 25.283 Contrastive_loss: 0.10111 (0.27243) Loss: 0.10111 (0.27243) 2025-03-19,06:02:19 | INFO | Train Epoch: 7 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.961/s, 148.961/s/gpu LR: 0.000170 Logit Scale: 25.301 Contrastive_loss: 0.098916 (0.27161) Loss: 0.098916 (0.27161) 2025-03-19,06:02:41 | INFO | Train Epoch: 7 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 151.752/s, 151.752/s/gpu LR: 0.000170 Logit Scale: 25.302 Contrastive_loss: 0.11261 (0.27087) Loss: 0.11261 (0.27087) 2025-03-19,06:03:02 | INFO | Train Epoch: 7 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 148.839/s, 148.839/s/gpu LR: 0.000170 Logit Scale: 25.322 Contrastive_loss: 0.24258 (0.27074) Loss: 0.24258 (0.27074) 2025-03-19,06:03:24 | INFO | Train Epoch: 7 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 151.352/s, 151.352/s/gpu LR: 0.000170 Logit Scale: 25.329 Contrastive_loss: 0.094015 (0.26992) Loss: 0.094015 (0.26992) 2025-03-19,06:03:45 | INFO | Train Epoch: 7 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 151.557/s, 151.557/s/gpu LR: 0.000170 Logit Scale: 25.344 Contrastive_loss: 0.75295 (0.27214) Loss: 0.75295 (0.27214) 2025-03-19,06:04:06 | INFO | Train Epoch: 7 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 150.115/s, 150.115/s/gpu LR: 0.000170 Logit Scale: 25.307 Contrastive_loss: 0.15557 (0.27161) Loss: 0.15557 (0.27161) 2025-03-19,06:04:28 | INFO | Train Epoch: 7 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 147.351/s, 147.351/s/gpu LR: 0.000170 Logit Scale: 25.326 Contrastive_loss: 0.067203 (0.27068) Loss: 0.067203 (0.27068) 2025-03-19,06:04:49 | INFO | Train Epoch: 7 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 147.756/s, 147.756/s/gpu LR: 0.000170 Logit Scale: 25.308 Contrastive_loss: 0.30981 (0.27085) Loss: 0.30981 (0.27085) 2025-03-19,06:05:11 | INFO | Train Epoch: 7 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 148.054/s, 148.054/s/gpu LR: 0.000170 Logit Scale: 25.301 Contrastive_loss: 0.33876 (0.27116) Loss: 0.33876 (0.27116) 2025-03-19,06:05:33 | INFO | Train Epoch: 7 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 147.834/s, 147.834/s/gpu LR: 0.000170 Logit Scale: 25.272 Contrastive_loss: 0.30224 (0.27130) Loss: 0.30224 (0.27130) 2025-03-19,06:05:54 | INFO | Train Epoch: 7 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.807/s, 149.807/s/gpu LR: 0.000170 Logit Scale: 25.295 Contrastive_loss: 0.23579 (0.27114) Loss: 0.23579 (0.27114) 2025-03-19,06:06:16 | INFO | Train Epoch: 7 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.676/s, 149.676/s/gpu LR: 0.000170 Logit Scale: 25.232 Contrastive_loss: 0.25218 (0.27106) Loss: 0.25218 (0.27106) 2025-03-19,06:06:37 | INFO | Train Epoch: 7 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.760/s, 148.760/s/gpu LR: 0.000170 Logit Scale: 25.308 Contrastive_loss: 0.30553 (0.27121) Loss: 0.30553 (0.27121) 2025-03-19,06:06:59 | INFO | Train Epoch: 7 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 148.106/s, 148.106/s/gpu LR: 0.000170 Logit Scale: 25.342 Contrastive_loss: 0.34932 (0.27156) Loss: 0.34932 (0.27156) 2025-03-19,06:07:20 | INFO | Train Epoch: 7 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 147.458/s, 147.458/s/gpu LR: 0.000170 Logit Scale: 25.378 Contrastive_loss: 0.18083 (0.27116) Loss: 0.18083 (0.27116) 2025-03-19,06:07:42 | INFO | Train Epoch: 7 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 144.644/s, 144.644/s/gpu LR: 0.000170 Logit Scale: 25.380 Contrastive_loss: 0.38612 (0.27166) Loss: 0.38612 (0.27166) 2025-03-19,06:08:04 | INFO | Train Epoch: 7 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.218, 146.074/s, 146.074/s/gpu LR: 0.000170 Logit Scale: 25.358 Contrastive_loss: 0.071070 (0.27078) Loss: 0.071070 (0.27078) 2025-03-19,06:08:26 | INFO | Train Epoch: 7 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.219, 145.457/s, 145.457/s/gpu LR: 0.000170 Logit Scale: 25.372 Contrastive_loss: 0.24104 (0.27066) Loss: 0.24104 (0.27066) 2025-03-19,06:08:48 | INFO | Train Epoch: 7 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.219, 146.552/s, 146.552/s/gpu LR: 0.000170 Logit Scale: 25.308 Contrastive_loss: 0.19609 (0.27033) Loss: 0.19609 (0.27033) 2025-03-19,06:09:09 | INFO | Train Epoch: 7 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.218, 145.852/s, 145.852/s/gpu LR: 0.000170 Logit Scale: 25.328 Contrastive_loss: 0.19947 (0.27003) Loss: 0.19947 (0.27003) 2025-03-19,06:09:31 | INFO | Train Epoch: 7 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.032/s, 149.032/s/gpu LR: 0.000170 Logit Scale: 25.328 Contrastive_loss: 0.013347 (0.26893) Loss: 0.013347 (0.26893) 2025-03-19,06:09:52 | INFO | Train Epoch: 7 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 151.357/s, 151.357/s/gpu LR: 0.000169 Logit Scale: 25.326 Contrastive_loss: 0.44959 (0.26970) Loss: 0.44959 (0.26970) 2025-03-19,06:10:14 | INFO | Train Epoch: 7 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 149.461/s, 149.461/s/gpu LR: 0.000169 Logit Scale: 25.367 Contrastive_loss: 0.12096 (0.26906) Loss: 0.12096 (0.26906) 2025-03-19,06:10:36 | INFO | Train Epoch: 7 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 149.017/s, 149.017/s/gpu LR: 0.000169 Logit Scale: 25.348 Contrastive_loss: 0.33616 (0.26935) Loss: 0.33616 (0.26935) 2025-03-19,06:10:57 | INFO | Train Epoch: 7 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.751/s, 149.751/s/gpu LR: 0.000169 Logit Scale: 25.370 Contrastive_loss: 0.45907 (0.27015) Loss: 0.45907 (0.27015) 2025-03-19,06:11:18 | INFO | Train Epoch: 7 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 148.974/s, 148.974/s/gpu LR: 0.000169 Logit Scale: 25.325 Contrastive_loss: 0.58588 (0.27148) Loss: 0.58588 (0.27148) 2025-03-19,06:11:40 | INFO | Train Epoch: 7 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 151.582/s, 151.582/s/gpu LR: 0.000169 Logit Scale: 25.387 Contrastive_loss: 0.36201 (0.27185) Loss: 0.36201 (0.27185) 2025-03-19,06:12:01 | INFO | Train Epoch: 7 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.213, 147.114/s, 147.114/s/gpu LR: 0.000169 Logit Scale: 25.375 Contrastive_loss: 0.29643 (0.27196) Loss: 0.29643 (0.27196) 2025-03-19,06:12:09 | INFO | Train Epoch: 7 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.214, 152.136/s, 152.136/s/gpu LR: 0.000169 Logit Scale: 25.357 Contrastive_loss: 0.090492 (0.27120) Loss: 0.090492 (0.27120) 2025-03-19,06:12:09 | INFO | Eval Epoch: 8 [32 / 7443] Clip Loss: 3.153469 2025-03-19,06:12:15 | INFO | Eval Epoch: 8 [3232 / 7443] Clip Loss: 0.888817 2025-03-19,06:12:21 | INFO | Eval Epoch: 8 [6432 / 7443] Clip Loss: 0.694900 2025-03-19,06:12:23 | INFO | Eval Epoch: 8 image_to_text_mean_rank: 95.7304 image_to_text_median_rank: 8.0000 image_to_text_R@1: 0.1185 image_to_text_R@5: 0.3985 image_to_text_R@10: 0.5717 text_to_image_mean_rank: 66.8243 text_to_image_median_rank: 8.0000 text_to_image_R@1: 0.1212 text_to_image_R@5: 0.4025 text_to_image_R@10: 0.5780 clip_val_loss: 0.6553 epoch: 8.0000 num_samples: 7443.0000 2025-03-19,06:12:55 | INFO | Start epoch 8 2025-03-19,06:12:56 | INFO | Train Epoch: 8 [ 32/766009 (0%)] Data (t): 0.168 Batch (t): 0.367, 87.2924/s, 87.2924/s/gpu LR: 0.000169 Logit Scale: 25.357 Contrastive_loss: 0.29312 (0.29312) Loss: 0.29312 (0.29312) 2025-03-19,06:13:18 | INFO | Train Epoch: 8 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.218, 149.441/s, 149.441/s/gpu LR: 0.000169 Logit Scale: 25.454 Contrastive_loss: 0.43469 (0.36390) Loss: 0.43469 (0.36390) 2025-03-19,06:13:39 | INFO | Train Epoch: 8 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 150.952/s, 150.952/s/gpu LR: 0.000169 Logit Scale: 25.480 Contrastive_loss: 0.23944 (0.32242) Loss: 0.23944 (0.32242) 2025-03-19,06:14:01 | INFO | Train Epoch: 8 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.061/s, 149.061/s/gpu LR: 0.000169 Logit Scale: 25.457 Contrastive_loss: 0.14598 (0.27831) Loss: 0.14598 (0.27831) 2025-03-19,06:14:22 | INFO | Train Epoch: 8 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 148.666/s, 148.666/s/gpu LR: 0.000169 Logit Scale: 25.462 Contrastive_loss: 0.073443 (0.23734) Loss: 0.073443 (0.23734) 2025-03-19,06:14:43 | INFO | Train Epoch: 8 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.754/s, 149.754/s/gpu LR: 0.000169 Logit Scale: 25.510 Contrastive_loss: 0.30655 (0.24887) Loss: 0.30655 (0.24887) 2025-03-19,06:15:05 | INFO | Train Epoch: 8 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 149.267/s, 149.267/s/gpu LR: 0.000169 Logit Scale: 25.459 Contrastive_loss: 0.35552 (0.26411) Loss: 0.35552 (0.26411) 2025-03-19,06:15:26 | INFO | Train Epoch: 8 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.882/s, 149.882/s/gpu LR: 0.000169 Logit Scale: 25.450 Contrastive_loss: 0.39087 (0.27995) Loss: 0.39087 (0.27995) 2025-03-19,06:15:48 | INFO | Train Epoch: 8 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 148.084/s, 148.084/s/gpu LR: 0.000169 Logit Scale: 25.441 Contrastive_loss: 0.24519 (0.27609) Loss: 0.24519 (0.27609) 2025-03-19,06:16:10 | INFO | Train Epoch: 8 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.218, 146.562/s, 146.562/s/gpu LR: 0.000169 Logit Scale: 25.496 Contrastive_loss: 0.27524 (0.27600) Loss: 0.27524 (0.27600) 2025-03-19,06:16:31 | INFO | Train Epoch: 8 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.219, 147.844/s, 147.844/s/gpu LR: 0.000169 Logit Scale: 25.496 Contrastive_loss: 0.099189 (0.25993) Loss: 0.099189 (0.25993) 2025-03-19,06:16:53 | INFO | Train Epoch: 8 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.839/s, 148.839/s/gpu LR: 0.000169 Logit Scale: 25.482 Contrastive_loss: 0.18690 (0.25384) Loss: 0.18690 (0.25384) 2025-03-19,06:17:15 | INFO | Train Epoch: 8 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.488/s, 148.488/s/gpu LR: 0.000169 Logit Scale: 25.488 Contrastive_loss: 0.14709 (0.24563) Loss: 0.14709 (0.24563) 2025-03-19,06:17:37 | INFO | Train Epoch: 8 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 146.713/s, 146.713/s/gpu LR: 0.000169 Logit Scale: 25.501 Contrastive_loss: 0.11527 (0.23632) Loss: 0.11527 (0.23632) 2025-03-19,06:17:58 | INFO | Train Epoch: 8 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.218, 147.636/s, 147.636/s/gpu LR: 0.000169 Logit Scale: 25.533 Contrastive_loss: 0.091563 (0.22667) Loss: 0.091563 (0.22667) 2025-03-19,06:18:20 | INFO | Train Epoch: 8 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.218, 146.510/s, 146.510/s/gpu LR: 0.000169 Logit Scale: 25.495 Contrastive_loss: 0.30431 (0.23152) Loss: 0.30431 (0.23152) 2025-03-19,06:18:42 | INFO | Train Epoch: 8 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.220, 144.587/s, 144.587/s/gpu LR: 0.000169 Logit Scale: 25.506 Contrastive_loss: 0.042401 (0.22040) Loss: 0.042401 (0.22040) 2025-03-19,06:19:04 | INFO | Train Epoch: 8 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.222, 145.932/s, 145.932/s/gpu LR: 0.000169 Logit Scale: 25.529 Contrastive_loss: 0.10377 (0.21392) Loss: 0.10377 (0.21392) 2025-03-19,06:19:26 | INFO | Train Epoch: 8 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.220, 145.853/s, 145.853/s/gpu LR: 0.000169 Logit Scale: 25.535 Contrastive_loss: 0.18953 (0.21264) Loss: 0.18953 (0.21264) 2025-03-19,06:19:49 | INFO | Train Epoch: 8 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.221, 146.921/s, 146.921/s/gpu LR: 0.000169 Logit Scale: 25.542 Contrastive_loss: 0.36154 (0.22008) Loss: 0.36154 (0.22008) 2025-03-19,06:20:11 | INFO | Train Epoch: 8 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.221, 147.518/s, 147.518/s/gpu LR: 0.000169 Logit Scale: 25.571 Contrastive_loss: 0.16979 (0.21769) Loss: 0.16979 (0.21769) 2025-03-19,06:20:32 | INFO | Train Epoch: 8 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 148.220/s, 148.220/s/gpu LR: 0.000169 Logit Scale: 25.536 Contrastive_loss: 0.41975 (0.22687) Loss: 0.41975 (0.22687) 2025-03-19,06:20:54 | INFO | Train Epoch: 8 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 149.045/s, 149.045/s/gpu LR: 0.000169 Logit Scale: 25.551 Contrastive_loss: 0.27417 (0.22893) Loss: 0.27417 (0.22893) 2025-03-19,06:21:15 | INFO | Train Epoch: 8 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 148.128/s, 148.128/s/gpu LR: 0.000169 Logit Scale: 25.508 Contrastive_loss: 0.20139 (0.22778) Loss: 0.20139 (0.22778) 2025-03-19,06:21:37 | INFO | Train Epoch: 8 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.213, 151.529/s, 151.529/s/gpu LR: 0.000169 Logit Scale: 25.585 Contrastive_loss: 0.17476 (0.22566) Loss: 0.17476 (0.22566) 2025-03-19,06:21:58 | INFO | Train Epoch: 8 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.213, 151.677/s, 151.677/s/gpu LR: 0.000168 Logit Scale: 25.581 Contrastive_loss: 0.23232 (0.22592) Loss: 0.23232 (0.22592) 2025-03-19,06:22:19 | INFO | Train Epoch: 8 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.213, 150.361/s, 150.361/s/gpu LR: 0.000168 Logit Scale: 25.555 Contrastive_loss: 0.084874 (0.22069) Loss: 0.084874 (0.22069) 2025-03-19,06:22:41 | INFO | Train Epoch: 8 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.215, 148.527/s, 148.527/s/gpu LR: 0.000168 Logit Scale: 25.582 Contrastive_loss: 0.20902 (0.22027) Loss: 0.20902 (0.22027) 2025-03-19,06:23:02 | INFO | Train Epoch: 8 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 148.285/s, 148.285/s/gpu LR: 0.000168 Logit Scale: 25.532 Contrastive_loss: 0.34230 (0.22448) Loss: 0.34230 (0.22448) 2025-03-19,06:23:24 | INFO | Train Epoch: 8 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.213, 148.214/s, 148.214/s/gpu LR: 0.000168 Logit Scale: 25.540 Contrastive_loss: 0.19072 (0.22336) Loss: 0.19072 (0.22336) 2025-03-19,06:23:45 | INFO | Train Epoch: 8 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 151.464/s, 151.464/s/gpu LR: 0.000168 Logit Scale: 25.525 Contrastive_loss: 0.39630 (0.22894) Loss: 0.39630 (0.22894) 2025-03-19,06:24:07 | INFO | Train Epoch: 8 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 148.782/s, 148.782/s/gpu LR: 0.000168 Logit Scale: 25.542 Contrastive_loss: 0.22932 (0.22895) Loss: 0.22932 (0.22895) 2025-03-19,06:24:28 | INFO | Train Epoch: 8 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 152.157/s, 152.157/s/gpu LR: 0.000168 Logit Scale: 25.507 Contrastive_loss: 0.15712 (0.22677) Loss: 0.15712 (0.22677) 2025-03-19,06:24:49 | INFO | Train Epoch: 8 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.213, 151.862/s, 151.862/s/gpu LR: 0.000168 Logit Scale: 25.530 Contrastive_loss: 0.25907 (0.22772) Loss: 0.25907 (0.22772) 2025-03-19,06:25:11 | INFO | Train Epoch: 8 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 148.423/s, 148.423/s/gpu LR: 0.000168 Logit Scale: 25.500 Contrastive_loss: 0.16308 (0.22587) Loss: 0.16308 (0.22587) 2025-03-19,06:25:32 | INFO | Train Epoch: 8 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 150.782/s, 150.782/s/gpu LR: 0.000168 Logit Scale: 25.521 Contrastive_loss: 0.23763 (0.22620) Loss: 0.23763 (0.22620) 2025-03-19,06:25:54 | INFO | Train Epoch: 8 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.984/s, 148.984/s/gpu LR: 0.000168 Logit Scale: 25.462 Contrastive_loss: 0.13138 (0.22364) Loss: 0.13138 (0.22364) 2025-03-19,06:26:15 | INFO | Train Epoch: 8 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.278/s, 148.278/s/gpu LR: 0.000168 Logit Scale: 25.497 Contrastive_loss: 0.18044 (0.22250) Loss: 0.18044 (0.22250) 2025-03-19,06:26:37 | INFO | Train Epoch: 8 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 149.426/s, 149.426/s/gpu LR: 0.000168 Logit Scale: 25.475 Contrastive_loss: 0.16093 (0.22092) Loss: 0.16093 (0.22092) 2025-03-19,06:26:59 | INFO | Train Epoch: 8 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.217, 148.240/s, 148.240/s/gpu LR: 0.000168 Logit Scale: 25.477 Contrastive_loss: 0.10137 (0.21793) Loss: 0.10137 (0.21793) 2025-03-19,06:27:20 | INFO | Train Epoch: 8 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.212, 151.702/s, 151.702/s/gpu LR: 0.000168 Logit Scale: 25.476 Contrastive_loss: 0.49779 (0.22476) Loss: 0.49779 (0.22476) 2025-03-19,06:27:41 | INFO | Train Epoch: 8 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 148.114/s, 148.114/s/gpu LR: 0.000168 Logit Scale: 25.502 Contrastive_loss: 0.12970 (0.22250) Loss: 0.12970 (0.22250) 2025-03-19,06:28:03 | INFO | Train Epoch: 8 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 148.262/s, 148.262/s/gpu LR: 0.000168 Logit Scale: 25.500 Contrastive_loss: 0.14171 (0.22062) Loss: 0.14171 (0.22062) 2025-03-19,06:28:25 | INFO | Train Epoch: 8 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 147.211/s, 147.211/s/gpu LR: 0.000168 Logit Scale: 25.498 Contrastive_loss: 0.18799 (0.21988) Loss: 0.18799 (0.21988) 2025-03-19,06:28:47 | INFO | Train Epoch: 8 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 147.106/s, 147.106/s/gpu LR: 0.000168 Logit Scale: 25.499 Contrastive_loss: 0.15790 (0.21850) Loss: 0.15790 (0.21850) 2025-03-19,06:29:09 | INFO | Train Epoch: 8 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.220, 147.366/s, 147.366/s/gpu LR: 0.000168 Logit Scale: 25.466 Contrastive_loss: 0.10897 (0.21612) Loss: 0.10897 (0.21612) 2025-03-19,06:29:31 | INFO | Train Epoch: 8 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.219, 146.867/s, 146.867/s/gpu LR: 0.000168 Logit Scale: 25.476 Contrastive_loss: 0.16032 (0.21493) Loss: 0.16032 (0.21493) 2025-03-19,06:29:52 | INFO | Train Epoch: 8 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 150.124/s, 150.124/s/gpu LR: 0.000168 Logit Scale: 25.507 Contrastive_loss: 0.039655 (0.21128) Loss: 0.039655 (0.21128) 2025-03-19,06:30:13 | INFO | Train Epoch: 8 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.212, 149.676/s, 149.676/s/gpu LR: 0.000168 Logit Scale: 25.536 Contrastive_loss: 0.22563 (0.21157) Loss: 0.22563 (0.21157) 2025-03-19,06:30:35 | INFO | Train Epoch: 8 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.127/s, 149.127/s/gpu LR: 0.000168 Logit Scale: 25.542 Contrastive_loss: 0.18005 (0.21094) Loss: 0.18005 (0.21094) 2025-03-19,06:30:56 | INFO | Train Epoch: 8 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.482/s, 149.482/s/gpu LR: 0.000168 Logit Scale: 25.521 Contrastive_loss: 0.38057 (0.21427) Loss: 0.38057 (0.21427) 2025-03-19,06:31:18 | INFO | Train Epoch: 8 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.894/s, 149.894/s/gpu LR: 0.000168 Logit Scale: 25.571 Contrastive_loss: 0.21926 (0.21436) Loss: 0.21926 (0.21436) 2025-03-19,06:31:39 | INFO | Train Epoch: 8 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.959/s, 149.959/s/gpu LR: 0.000168 Logit Scale: 25.548 Contrastive_loss: 0.22708 (0.21460) Loss: 0.22708 (0.21460) 2025-03-19,06:32:00 | INFO | Train Epoch: 8 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.192/s, 149.192/s/gpu LR: 0.000168 Logit Scale: 25.542 Contrastive_loss: 0.17592 (0.21389) Loss: 0.17592 (0.21389) 2025-03-19,06:32:22 | INFO | Train Epoch: 8 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 149.057/s, 149.057/s/gpu LR: 0.000168 Logit Scale: 25.549 Contrastive_loss: 0.15778 (0.21287) Loss: 0.15778 (0.21287) 2025-03-19,06:32:44 | INFO | Train Epoch: 8 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 148.155/s, 148.155/s/gpu LR: 0.000168 Logit Scale: 25.536 Contrastive_loss: 0.053932 (0.21003) Loss: 0.053932 (0.21003) 2025-03-19,06:33:05 | INFO | Train Epoch: 8 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 148.896/s, 148.896/s/gpu LR: 0.000167 Logit Scale: 25.492 Contrastive_loss: 0.089281 (0.20791) Loss: 0.089281 (0.20791) 2025-03-19,06:33:27 | INFO | Train Epoch: 8 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 148.818/s, 148.818/s/gpu LR: 0.000167 Logit Scale: 25.498 Contrastive_loss: 0.14587 (0.20684) Loss: 0.14587 (0.20684) 2025-03-19,06:33:48 | INFO | Train Epoch: 8 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 150.604/s, 150.604/s/gpu LR: 0.000167 Logit Scale: 25.494 Contrastive_loss: 0.12598 (0.20547) Loss: 0.12598 (0.20547) 2025-03-19,06:34:09 | INFO | Train Epoch: 8 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.213, 150.043/s, 150.043/s/gpu LR: 0.000167 Logit Scale: 25.517 Contrastive_loss: 0.58585 (0.21181) Loss: 0.58585 (0.21181) 2025-03-19,06:34:31 | INFO | Train Epoch: 8 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 151.966/s, 151.966/s/gpu LR: 0.000167 Logit Scale: 25.480 Contrastive_loss: 0.27879 (0.21291) Loss: 0.27879 (0.21291) 2025-03-19,06:34:52 | INFO | Train Epoch: 8 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.213, 150.738/s, 150.738/s/gpu LR: 0.000167 Logit Scale: 25.507 Contrastive_loss: 0.52103 (0.21788) Loss: 0.52103 (0.21788) 2025-03-19,06:35:13 | INFO | Train Epoch: 8 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 148.866/s, 148.866/s/gpu LR: 0.000167 Logit Scale: 25.521 Contrastive_loss: 0.21062 (0.21776) Loss: 0.21062 (0.21776) 2025-03-19,06:35:35 | INFO | Train Epoch: 8 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.218, 149.314/s, 149.314/s/gpu LR: 0.000167 Logit Scale: 25.550 Contrastive_loss: 0.11283 (0.21612) Loss: 0.11283 (0.21612) 2025-03-19,06:35:57 | INFO | Train Epoch: 8 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.320/s, 149.320/s/gpu LR: 0.000167 Logit Scale: 25.518 Contrastive_loss: 0.23116 (0.21635) Loss: 0.23116 (0.21635) 2025-03-19,06:36:18 | INFO | Train Epoch: 8 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.217, 146.573/s, 146.573/s/gpu LR: 0.000167 Logit Scale: 25.543 Contrastive_loss: 0.30657 (0.21772) Loss: 0.30657 (0.21772) 2025-03-19,06:36:40 | INFO | Train Epoch: 8 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 148.790/s, 148.790/s/gpu LR: 0.000167 Logit Scale: 25.538 Contrastive_loss: 0.14172 (0.21659) Loss: 0.14172 (0.21659) 2025-03-19,06:37:02 | INFO | Train Epoch: 8 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.594/s, 148.594/s/gpu LR: 0.000167 Logit Scale: 25.546 Contrastive_loss: 0.52747 (0.22116) Loss: 0.52747 (0.22116) 2025-03-19,06:37:23 | INFO | Train Epoch: 8 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.303/s, 149.303/s/gpu LR: 0.000167 Logit Scale: 25.540 Contrastive_loss: 0.25441 (0.22164) Loss: 0.25441 (0.22164) 2025-03-19,06:37:45 | INFO | Train Epoch: 8 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 149.118/s, 149.118/s/gpu LR: 0.000167 Logit Scale: 25.515 Contrastive_loss: 0.40211 (0.22422) Loss: 0.40211 (0.22422) 2025-03-19,06:38:06 | INFO | Train Epoch: 8 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 149.597/s, 149.597/s/gpu LR: 0.000167 Logit Scale: 25.490 Contrastive_loss: 0.27259 (0.22490) Loss: 0.27259 (0.22490) 2025-03-19,06:38:28 | INFO | Train Epoch: 8 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 150.704/s, 150.704/s/gpu LR: 0.000167 Logit Scale: 25.447 Contrastive_loss: 0.28017 (0.22567) Loss: 0.28017 (0.22567) 2025-03-19,06:38:49 | INFO | Train Epoch: 8 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 150.525/s, 150.525/s/gpu LR: 0.000167 Logit Scale: 25.428 Contrastive_loss: 0.11266 (0.22412) Loss: 0.11266 (0.22412) 2025-03-19,06:39:11 | INFO | Train Epoch: 8 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.988/s, 148.988/s/gpu LR: 0.000167 Logit Scale: 25.422 Contrastive_loss: 0.11165 (0.22260) Loss: 0.11165 (0.22260) 2025-03-19,06:39:32 | INFO | Train Epoch: 8 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.053/s, 149.053/s/gpu LR: 0.000167 Logit Scale: 25.473 Contrastive_loss: 0.48703 (0.22613) Loss: 0.48703 (0.22613) 2025-03-19,06:39:54 | INFO | Train Epoch: 8 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.411/s, 149.411/s/gpu LR: 0.000167 Logit Scale: 25.484 Contrastive_loss: 0.20650 (0.22587) Loss: 0.20650 (0.22587) 2025-03-19,06:40:16 | INFO | Train Epoch: 8 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.217, 148.342/s, 148.342/s/gpu LR: 0.000167 Logit Scale: 25.525 Contrastive_loss: 0.0080951 (0.22304) Loss: 0.0080951 (0.22304) 2025-03-19,06:40:37 | INFO | Train Epoch: 8 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 147.687/s, 147.687/s/gpu LR: 0.000167 Logit Scale: 25.507 Contrastive_loss: 0.057229 (0.22091) Loss: 0.057229 (0.22091) 2025-03-19,06:40:59 | INFO | Train Epoch: 8 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.218, 149.479/s, 149.479/s/gpu LR: 0.000167 Logit Scale: 25.507 Contrastive_loss: 0.22293 (0.22094) Loss: 0.22293 (0.22094) 2025-03-19,06:41:20 | INFO | Train Epoch: 8 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 148.003/s, 148.003/s/gpu LR: 0.000167 Logit Scale: 25.512 Contrastive_loss: 0.065220 (0.21899) Loss: 0.065220 (0.21899) 2025-03-19,06:41:42 | INFO | Train Epoch: 8 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.218, 149.785/s, 149.785/s/gpu LR: 0.000167 Logit Scale: 25.508 Contrastive_loss: 0.10771 (0.21762) Loss: 0.10771 (0.21762) 2025-03-19,06:42:04 | INFO | Train Epoch: 8 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 149.102/s, 149.102/s/gpu LR: 0.000167 Logit Scale: 25.528 Contrastive_loss: 0.73051 (0.22387) Loss: 0.73051 (0.22387) 2025-03-19,06:42:25 | INFO | Train Epoch: 8 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 146.229/s, 146.229/s/gpu LR: 0.000167 Logit Scale: 25.515 Contrastive_loss: 0.11686 (0.22258) Loss: 0.11686 (0.22258) 2025-03-19,06:42:47 | INFO | Train Epoch: 8 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.217, 145.841/s, 145.841/s/gpu LR: 0.000167 Logit Scale: 25.486 Contrastive_loss: 0.29365 (0.22343) Loss: 0.29365 (0.22343) 2025-03-19,06:43:09 | INFO | Train Epoch: 8 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.217, 148.238/s, 148.238/s/gpu LR: 0.000167 Logit Scale: 25.517 Contrastive_loss: 0.070457 (0.22163) Loss: 0.070457 (0.22163) 2025-03-19,06:43:30 | INFO | Train Epoch: 8 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 147.652/s, 147.652/s/gpu LR: 0.000167 Logit Scale: 25.472 Contrastive_loss: 0.43932 (0.22416) Loss: 0.43932 (0.22416) 2025-03-19,06:43:52 | INFO | Train Epoch: 8 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 147.542/s, 147.542/s/gpu LR: 0.000166 Logit Scale: 25.462 Contrastive_loss: 0.20487 (0.22394) Loss: 0.20487 (0.22394) 2025-03-19,06:44:14 | INFO | Train Epoch: 8 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 146.067/s, 146.067/s/gpu LR: 0.000166 Logit Scale: 25.457 Contrastive_loss: 0.13122 (0.22289) Loss: 0.13122 (0.22289) 2025-03-19,06:44:35 | INFO | Train Epoch: 8 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 148.252/s, 148.252/s/gpu LR: 0.000166 Logit Scale: 25.479 Contrastive_loss: 0.049068 (0.22093) Loss: 0.049068 (0.22093) 2025-03-19,06:44:57 | INFO | Train Epoch: 8 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.217, 149.267/s, 149.267/s/gpu LR: 0.000166 Logit Scale: 25.504 Contrastive_loss: 0.12994 (0.21992) Loss: 0.12994 (0.21992) 2025-03-19,06:45:19 | INFO | Train Epoch: 8 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 149.673/s, 149.673/s/gpu LR: 0.000166 Logit Scale: 25.472 Contrastive_loss: 0.42314 (0.22216) Loss: 0.42314 (0.22216) 2025-03-19,06:45:40 | INFO | Train Epoch: 8 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.096/s, 149.096/s/gpu LR: 0.000166 Logit Scale: 25.526 Contrastive_loss: 0.095319 (0.22078) Loss: 0.095319 (0.22078) 2025-03-19,06:46:02 | INFO | Train Epoch: 8 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.213, 149.824/s, 149.824/s/gpu LR: 0.000166 Logit Scale: 25.484 Contrastive_loss: 0.21997 (0.22077) Loss: 0.21997 (0.22077) 2025-03-19,06:46:23 | INFO | Train Epoch: 8 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.946/s, 148.946/s/gpu LR: 0.000166 Logit Scale: 25.511 Contrastive_loss: 0.48503 (0.22358) Loss: 0.48503 (0.22358) 2025-03-19,06:46:45 | INFO | Train Epoch: 8 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 152.311/s, 152.311/s/gpu LR: 0.000166 Logit Scale: 25.496 Contrastive_loss: 0.17049 (0.22302) Loss: 0.17049 (0.22302) 2025-03-19,06:47:06 | INFO | Train Epoch: 8 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 148.479/s, 148.479/s/gpu LR: 0.000166 Logit Scale: 25.516 Contrastive_loss: 0.23354 (0.22313) Loss: 0.23354 (0.22313) 2025-03-19,06:47:28 | INFO | Train Epoch: 8 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 150.038/s, 150.038/s/gpu LR: 0.000166 Logit Scale: 25.508 Contrastive_loss: 0.11443 (0.22201) Loss: 0.11443 (0.22201) 2025-03-19,06:47:49 | INFO | Train Epoch: 8 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 150.279/s, 150.279/s/gpu LR: 0.000166 Logit Scale: 25.530 Contrastive_loss: 0.16541 (0.22143) Loss: 0.16541 (0.22143) 2025-03-19,06:48:11 | INFO | Train Epoch: 8 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 147.306/s, 147.306/s/gpu LR: 0.000166 Logit Scale: 25.557 Contrastive_loss: 0.33092 (0.22254) Loss: 0.33092 (0.22254) 2025-03-19,06:48:32 | INFO | Train Epoch: 8 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.707/s, 148.707/s/gpu LR: 0.000166 Logit Scale: 25.544 Contrastive_loss: 0.21875 (0.22250) Loss: 0.21875 (0.22250) 2025-03-19,06:48:54 | INFO | Train Epoch: 8 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.215, 149.949/s, 149.949/s/gpu LR: 0.000166 Logit Scale: 25.534 Contrastive_loss: 0.36597 (0.22392) Loss: 0.36597 (0.22392) 2025-03-19,06:49:15 | INFO | Train Epoch: 8 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.726/s, 149.726/s/gpu LR: 0.000166 Logit Scale: 25.534 Contrastive_loss: 0.19281 (0.22362) Loss: 0.19281 (0.22362) 2025-03-19,06:49:36 | INFO | Train Epoch: 8 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.497/s, 149.497/s/gpu LR: 0.000166 Logit Scale: 25.548 Contrastive_loss: 0.40414 (0.22537) Loss: 0.40414 (0.22537) 2025-03-19,06:49:58 | INFO | Train Epoch: 8 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.213, 149.086/s, 149.086/s/gpu LR: 0.000166 Logit Scale: 25.534 Contrastive_loss: 0.30138 (0.22610) Loss: 0.30138 (0.22610) 2025-03-19,06:50:19 | INFO | Train Epoch: 8 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 147.510/s, 147.510/s/gpu LR: 0.000166 Logit Scale: 25.578 Contrastive_loss: 0.12181 (0.22511) Loss: 0.12181 (0.22511) 2025-03-19,06:50:41 | INFO | Train Epoch: 8 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 151.695/s, 151.695/s/gpu LR: 0.000166 Logit Scale: 25.564 Contrastive_loss: 0.28871 (0.22571) Loss: 0.28871 (0.22571) 2025-03-19,06:51:02 | INFO | Train Epoch: 8 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 148.235/s, 148.235/s/gpu LR: 0.000166 Logit Scale: 25.579 Contrastive_loss: 0.086605 (0.22441) Loss: 0.086605 (0.22441) 2025-03-19,06:51:24 | INFO | Train Epoch: 8 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.232/s, 149.232/s/gpu LR: 0.000166 Logit Scale: 25.500 Contrastive_loss: 0.15713 (0.22378) Loss: 0.15713 (0.22378) 2025-03-19,06:51:45 | INFO | Train Epoch: 8 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 147.154/s, 147.154/s/gpu LR: 0.000166 Logit Scale: 25.476 Contrastive_loss: 0.39998 (0.22540) Loss: 0.39998 (0.22540) 2025-03-19,06:52:07 | INFO | Train Epoch: 8 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 142.944/s, 142.944/s/gpu LR: 0.000166 Logit Scale: 25.491 Contrastive_loss: 0.32380 (0.22629) Loss: 0.32380 (0.22629) 2025-03-19,06:52:29 | INFO | Train Epoch: 8 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 149.258/s, 149.258/s/gpu LR: 0.000166 Logit Scale: 25.491 Contrastive_loss: 0.48515 (0.22863) Loss: 0.48515 (0.22863) 2025-03-19,06:52:50 | INFO | Train Epoch: 8 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.397/s, 149.397/s/gpu LR: 0.000166 Logit Scale: 25.524 Contrastive_loss: 0.26735 (0.22897) Loss: 0.26735 (0.22897) 2025-03-19,06:53:12 | INFO | Train Epoch: 8 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 149.486/s, 149.486/s/gpu LR: 0.000166 Logit Scale: 25.513 Contrastive_loss: 0.36178 (0.23015) Loss: 0.36178 (0.23015) 2025-03-19,06:53:33 | INFO | Train Epoch: 8 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 147.866/s, 147.866/s/gpu LR: 0.000166 Logit Scale: 25.531 Contrastive_loss: 0.28337 (0.23061) Loss: 0.28337 (0.23061) 2025-03-19,06:53:55 | INFO | Train Epoch: 8 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 149.105/s, 149.105/s/gpu LR: 0.000166 Logit Scale: 25.515 Contrastive_loss: 0.13742 (0.22980) Loss: 0.13742 (0.22980) 2025-03-19,06:54:17 | INFO | Train Epoch: 8 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.219, 147.925/s, 147.925/s/gpu LR: 0.000166 Logit Scale: 25.542 Contrastive_loss: 0.22804 (0.22979) Loss: 0.22804 (0.22979) 2025-03-19,06:54:38 | INFO | Train Epoch: 8 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 146.440/s, 146.440/s/gpu LR: 0.000165 Logit Scale: 25.563 Contrastive_loss: 0.10076 (0.22869) Loss: 0.10076 (0.22869) 2025-03-19,06:55:00 | INFO | Train Epoch: 8 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 148.190/s, 148.190/s/gpu LR: 0.000165 Logit Scale: 25.571 Contrastive_loss: 0.18544 (0.22832) Loss: 0.18544 (0.22832) 2025-03-19,06:55:22 | INFO | Train Epoch: 8 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 147.797/s, 147.797/s/gpu LR: 0.000165 Logit Scale: 25.552 Contrastive_loss: 0.066196 (0.22696) Loss: 0.066196 (0.22696) 2025-03-19,06:55:43 | INFO | Train Epoch: 8 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.217, 148.577/s, 148.577/s/gpu LR: 0.000165 Logit Scale: 25.534 Contrastive_loss: 0.15926 (0.22639) Loss: 0.15926 (0.22639) 2025-03-19,06:56:05 | INFO | Train Epoch: 8 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 149.754/s, 149.754/s/gpu LR: 0.000165 Logit Scale: 25.587 Contrastive_loss: 0.31422 (0.22712) Loss: 0.31422 (0.22712) 2025-03-19,06:56:26 | INFO | Train Epoch: 8 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.213, 148.293/s, 148.293/s/gpu LR: 0.000165 Logit Scale: 25.658 Contrastive_loss: 0.47484 (0.22915) Loss: 0.47484 (0.22915) 2025-03-19,06:56:47 | INFO | Train Epoch: 8 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 148.919/s, 148.919/s/gpu LR: 0.000165 Logit Scale: 25.583 Contrastive_loss: 0.24405 (0.22927) Loss: 0.24405 (0.22927) 2025-03-19,06:57:09 | INFO | Train Epoch: 8 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.670/s, 149.670/s/gpu LR: 0.000165 Logit Scale: 25.623 Contrastive_loss: 0.22088 (0.22920) Loss: 0.22088 (0.22920) 2025-03-19,06:57:30 | INFO | Train Epoch: 8 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 148.916/s, 148.916/s/gpu LR: 0.000165 Logit Scale: 25.559 Contrastive_loss: 0.14073 (0.22849) Loss: 0.14073 (0.22849) 2025-03-19,06:57:52 | INFO | Train Epoch: 8 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 145.687/s, 145.687/s/gpu LR: 0.000165 Logit Scale: 25.522 Contrastive_loss: 0.25733 (0.22872) Loss: 0.25733 (0.22872) 2025-03-19,06:58:14 | INFO | Train Epoch: 8 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 145.910/s, 145.910/s/gpu LR: 0.000165 Logit Scale: 25.535 Contrastive_loss: 0.48027 (0.23070) Loss: 0.48027 (0.23070) 2025-03-19,06:58:36 | INFO | Train Epoch: 8 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 148.253/s, 148.253/s/gpu LR: 0.000165 Logit Scale: 25.558 Contrastive_loss: 0.20284 (0.23049) Loss: 0.20284 (0.23049) 2025-03-19,06:58:57 | INFO | Train Epoch: 8 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 145.973/s, 145.973/s/gpu LR: 0.000165 Logit Scale: 25.541 Contrastive_loss: 0.28385 (0.23090) Loss: 0.28385 (0.23090) 2025-03-19,06:59:19 | INFO | Train Epoch: 8 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.218, 145.073/s, 145.073/s/gpu LR: 0.000165 Logit Scale: 25.522 Contrastive_loss: 0.20964 (0.23074) Loss: 0.20964 (0.23074) 2025-03-19,06:59:41 | INFO | Train Epoch: 8 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.220, 145.758/s, 145.758/s/gpu LR: 0.000165 Logit Scale: 25.523 Contrastive_loss: 0.26723 (0.23101) Loss: 0.26723 (0.23101) 2025-03-19,07:00:03 | INFO | Train Epoch: 8 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 151.313/s, 151.313/s/gpu LR: 0.000165 Logit Scale: 25.537 Contrastive_loss: 0.32072 (0.23169) Loss: 0.32072 (0.23169) 2025-03-19,07:00:25 | INFO | Train Epoch: 8 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 146.997/s, 146.997/s/gpu LR: 0.000165 Logit Scale: 25.570 Contrastive_loss: 0.61676 (0.23459) Loss: 0.61676 (0.23459) 2025-03-19,07:00:46 | INFO | Train Epoch: 8 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 145.809/s, 145.809/s/gpu LR: 0.000165 Logit Scale: 25.561 Contrastive_loss: 0.13692 (0.23386) Loss: 0.13692 (0.23386) 2025-03-19,07:01:08 | INFO | Train Epoch: 8 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 144.113/s, 144.113/s/gpu LR: 0.000165 Logit Scale: 25.571 Contrastive_loss: 0.17215 (0.23340) Loss: 0.17215 (0.23340) 2025-03-19,07:01:30 | INFO | Train Epoch: 8 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 148.069/s, 148.069/s/gpu LR: 0.000165 Logit Scale: 25.597 Contrastive_loss: 0.32267 (0.23406) Loss: 0.32267 (0.23406) 2025-03-19,07:01:51 | INFO | Train Epoch: 8 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 148.681/s, 148.681/s/gpu LR: 0.000165 Logit Scale: 25.587 Contrastive_loss: 0.26630 (0.23430) Loss: 0.26630 (0.23430) 2025-03-19,07:02:13 | INFO | Train Epoch: 8 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 148.641/s, 148.641/s/gpu LR: 0.000165 Logit Scale: 25.556 Contrastive_loss: 0.17700 (0.23388) Loss: 0.17700 (0.23388) 2025-03-19,07:02:34 | INFO | Train Epoch: 8 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 143.519/s, 143.519/s/gpu LR: 0.000165 Logit Scale: 25.550 Contrastive_loss: 0.13843 (0.23319) Loss: 0.13843 (0.23319) 2025-03-19,07:02:56 | INFO | Train Epoch: 8 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.940/s, 148.940/s/gpu LR: 0.000165 Logit Scale: 25.562 Contrastive_loss: 0.12746 (0.23244) Loss: 0.12746 (0.23244) 2025-03-19,07:03:17 | INFO | Train Epoch: 8 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.100/s, 149.100/s/gpu LR: 0.000165 Logit Scale: 25.583 Contrastive_loss: 0.096173 (0.23147) Loss: 0.096173 (0.23147) 2025-03-19,07:03:39 | INFO | Train Epoch: 8 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 149.025/s, 149.025/s/gpu LR: 0.000165 Logit Scale: 25.567 Contrastive_loss: 0.21440 (0.23135) Loss: 0.21440 (0.23135) 2025-03-19,07:04:01 | INFO | Train Epoch: 8 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 148.134/s, 148.134/s/gpu LR: 0.000165 Logit Scale: 25.550 Contrastive_loss: 0.17114 (0.23093) Loss: 0.17114 (0.23093) 2025-03-19,07:04:22 | INFO | Train Epoch: 8 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 145.583/s, 145.583/s/gpu LR: 0.000165 Logit Scale: 25.560 Contrastive_loss: 0.36899 (0.23189) Loss: 0.36899 (0.23189) 2025-03-19,07:04:44 | INFO | Train Epoch: 8 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 147.316/s, 147.316/s/gpu LR: 0.000165 Logit Scale: 25.602 Contrastive_loss: 0.18632 (0.23157) Loss: 0.18632 (0.23157) 2025-03-19,07:05:05 | INFO | Train Epoch: 8 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.072/s, 148.072/s/gpu LR: 0.000165 Logit Scale: 25.602 Contrastive_loss: 0.61270 (0.23419) Loss: 0.61270 (0.23419) 2025-03-19,07:05:27 | INFO | Train Epoch: 8 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.217, 146.005/s, 146.005/s/gpu LR: 0.000164 Logit Scale: 25.602 Contrastive_loss: 0.18916 (0.23388) Loss: 0.18916 (0.23388) 2025-03-19,07:05:49 | INFO | Train Epoch: 8 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.725/s, 148.725/s/gpu LR: 0.000164 Logit Scale: 25.593 Contrastive_loss: 0.20153 (0.23366) Loss: 0.20153 (0.23366) 2025-03-19,07:06:10 | INFO | Train Epoch: 8 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.107/s, 149.107/s/gpu LR: 0.000164 Logit Scale: 25.591 Contrastive_loss: 0.63563 (0.23636) Loss: 0.63563 (0.23636) 2025-03-19,07:06:32 | INFO | Train Epoch: 8 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 148.657/s, 148.657/s/gpu LR: 0.000164 Logit Scale: 25.590 Contrastive_loss: 0.10115 (0.23546) Loss: 0.10115 (0.23546) 2025-03-19,07:06:53 | INFO | Train Epoch: 8 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 151.473/s, 151.473/s/gpu LR: 0.000164 Logit Scale: 25.584 Contrastive_loss: 0.098402 (0.23455) Loss: 0.098402 (0.23455) 2025-03-19,07:07:14 | INFO | Train Epoch: 8 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 149.566/s, 149.566/s/gpu LR: 0.000164 Logit Scale: 25.533 Contrastive_loss: 0.42812 (0.23582) Loss: 0.42812 (0.23582) 2025-03-19,07:07:36 | INFO | Train Epoch: 8 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 146.250/s, 146.250/s/gpu LR: 0.000164 Logit Scale: 25.524 Contrastive_loss: 0.38607 (0.23680) Loss: 0.38607 (0.23680) 2025-03-19,07:07:57 | INFO | Train Epoch: 8 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 152.256/s, 152.256/s/gpu LR: 0.000164 Logit Scale: 25.523 Contrastive_loss: 0.066143 (0.23570) Loss: 0.066143 (0.23570) 2025-03-19,07:08:19 | INFO | Train Epoch: 8 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 151.489/s, 151.489/s/gpu LR: 0.000164 Logit Scale: 25.545 Contrastive_loss: 0.26783 (0.23590) Loss: 0.26783 (0.23590) 2025-03-19,07:08:40 | INFO | Train Epoch: 8 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.192/s, 149.192/s/gpu LR: 0.000164 Logit Scale: 25.560 Contrastive_loss: 0.14802 (0.23534) Loss: 0.14802 (0.23534) 2025-03-19,07:09:02 | INFO | Train Epoch: 8 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 147.591/s, 147.591/s/gpu LR: 0.000164 Logit Scale: 25.566 Contrastive_loss: 0.18764 (0.23504) Loss: 0.18764 (0.23504) 2025-03-19,07:09:24 | INFO | Train Epoch: 8 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 148.110/s, 148.110/s/gpu LR: 0.000164 Logit Scale: 25.565 Contrastive_loss: 0.44434 (0.23636) Loss: 0.44434 (0.23636) 2025-03-19,07:09:46 | INFO | Train Epoch: 8 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 147.096/s, 147.096/s/gpu LR: 0.000164 Logit Scale: 25.584 Contrastive_loss: 0.19581 (0.23611) Loss: 0.19581 (0.23611) 2025-03-19,07:10:07 | INFO | Train Epoch: 8 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 147.494/s, 147.494/s/gpu LR: 0.000164 Logit Scale: 25.580 Contrastive_loss: 0.26118 (0.23626) Loss: 0.26118 (0.23626) 2025-03-19,07:10:29 | INFO | Train Epoch: 8 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 149.737/s, 149.737/s/gpu LR: 0.000164 Logit Scale: 25.579 Contrastive_loss: 0.30632 (0.23670) Loss: 0.30632 (0.23670) 2025-03-19,07:10:50 | INFO | Train Epoch: 8 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 148.364/s, 148.364/s/gpu LR: 0.000164 Logit Scale: 25.530 Contrastive_loss: 0.23546 (0.23669) Loss: 0.23546 (0.23669) 2025-03-19,07:11:12 | INFO | Train Epoch: 8 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 150.163/s, 150.163/s/gpu LR: 0.000164 Logit Scale: 25.555 Contrastive_loss: 0.15628 (0.23620) Loss: 0.15628 (0.23620) 2025-03-19,07:11:33 | INFO | Train Epoch: 8 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 148.056/s, 148.056/s/gpu LR: 0.000164 Logit Scale: 25.531 Contrastive_loss: 0.13129 (0.23556) Loss: 0.13129 (0.23556) 2025-03-19,07:11:55 | INFO | Train Epoch: 8 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 147.471/s, 147.471/s/gpu LR: 0.000164 Logit Scale: 25.552 Contrastive_loss: 0.20151 (0.23535) Loss: 0.20151 (0.23535) 2025-03-19,07:12:17 | INFO | Train Epoch: 8 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 147.469/s, 147.469/s/gpu LR: 0.000164 Logit Scale: 25.529 Contrastive_loss: 0.32064 (0.23586) Loss: 0.32064 (0.23586) 2025-03-19,07:12:38 | INFO | Train Epoch: 8 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 148.981/s, 148.981/s/gpu LR: 0.000164 Logit Scale: 25.529 Contrastive_loss: 0.41097 (0.23691) Loss: 0.41097 (0.23691) 2025-03-19,07:13:00 | INFO | Train Epoch: 8 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.219, 147.488/s, 147.488/s/gpu LR: 0.000164 Logit Scale: 25.492 Contrastive_loss: 0.096867 (0.23608) Loss: 0.096867 (0.23608) 2025-03-19,07:13:22 | INFO | Train Epoch: 8 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 147.780/s, 147.780/s/gpu LR: 0.000164 Logit Scale: 25.509 Contrastive_loss: 0.34072 (0.23670) Loss: 0.34072 (0.23670) 2025-03-19,07:13:44 | INFO | Train Epoch: 8 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 147.337/s, 147.337/s/gpu LR: 0.000164 Logit Scale: 25.479 Contrastive_loss: 0.36150 (0.23743) Loss: 0.36150 (0.23743) 2025-03-19,07:14:05 | INFO | Train Epoch: 8 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 148.696/s, 148.696/s/gpu LR: 0.000164 Logit Scale: 25.518 Contrastive_loss: 0.12076 (0.23675) Loss: 0.12076 (0.23675) 2025-03-19,07:14:27 | INFO | Train Epoch: 8 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 147.537/s, 147.537/s/gpu LR: 0.000164 Logit Scale: 25.549 Contrastive_loss: 0.075184 (0.23581) Loss: 0.075184 (0.23581) 2025-03-19,07:14:49 | INFO | Train Epoch: 8 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 147.089/s, 147.089/s/gpu LR: 0.000164 Logit Scale: 25.525 Contrastive_loss: 0.17328 (0.23545) Loss: 0.17328 (0.23545) 2025-03-19,07:15:10 | INFO | Train Epoch: 8 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 147.646/s, 147.646/s/gpu LR: 0.000164 Logit Scale: 25.517 Contrastive_loss: 0.29093 (0.23577) Loss: 0.29093 (0.23577) 2025-03-19,07:15:32 | INFO | Train Epoch: 8 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 148.823/s, 148.823/s/gpu LR: 0.000164 Logit Scale: 25.505 Contrastive_loss: 0.30976 (0.23619) Loss: 0.30976 (0.23619) 2025-03-19,07:15:54 | INFO | Train Epoch: 8 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.220, 144.785/s, 144.785/s/gpu LR: 0.000163 Logit Scale: 25.471 Contrastive_loss: 0.19480 (0.23596) Loss: 0.19480 (0.23596) 2025-03-19,07:16:16 | INFO | Train Epoch: 8 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.223, 143.489/s, 143.489/s/gpu LR: 0.000163 Logit Scale: 25.476 Contrastive_loss: 0.18037 (0.23564) Loss: 0.18037 (0.23564) 2025-03-19,07:16:39 | INFO | Train Epoch: 8 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.222, 144.553/s, 144.553/s/gpu LR: 0.000163 Logit Scale: 25.510 Contrastive_loss: 0.37314 (0.23642) Loss: 0.37314 (0.23642) 2025-03-19,07:17:00 | INFO | Train Epoch: 8 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 150.282/s, 150.282/s/gpu LR: 0.000163 Logit Scale: 25.521 Contrastive_loss: 0.40222 (0.23734) Loss: 0.40222 (0.23734) 2025-03-19,07:17:21 | INFO | Train Epoch: 8 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 147.063/s, 147.063/s/gpu LR: 0.000163 Logit Scale: 25.545 Contrastive_loss: 0.25884 (0.23746) Loss: 0.25884 (0.23746) 2025-03-19,07:17:43 | INFO | Train Epoch: 8 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 145.166/s, 145.166/s/gpu LR: 0.000163 Logit Scale: 25.568 Contrastive_loss: 0.14819 (0.23697) Loss: 0.14819 (0.23697) 2025-03-19,07:18:05 | INFO | Train Epoch: 8 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.220, 146.520/s, 146.520/s/gpu LR: 0.000163 Logit Scale: 25.549 Contrastive_loss: 0.16138 (0.23655) Loss: 0.16138 (0.23655) 2025-03-19,07:18:26 | INFO | Train Epoch: 8 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 151.270/s, 151.270/s/gpu LR: 0.000163 Logit Scale: 25.548 Contrastive_loss: 0.14745 (0.23607) Loss: 0.14745 (0.23607) 2025-03-19,07:18:48 | INFO | Train Epoch: 8 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 147.128/s, 147.128/s/gpu LR: 0.000163 Logit Scale: 25.581 Contrastive_loss: 0.19370 (0.23584) Loss: 0.19370 (0.23584) 2025-03-19,07:19:10 | INFO | Train Epoch: 8 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 147.569/s, 147.569/s/gpu LR: 0.000163 Logit Scale: 25.604 Contrastive_loss: 0.33648 (0.23638) Loss: 0.33648 (0.23638) 2025-03-19,07:19:31 | INFO | Train Epoch: 8 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.522/s, 149.522/s/gpu LR: 0.000163 Logit Scale: 25.583 Contrastive_loss: 0.069977 (0.23548) Loss: 0.069977 (0.23548) 2025-03-19,07:19:53 | INFO | Train Epoch: 8 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 149.754/s, 149.754/s/gpu LR: 0.000163 Logit Scale: 25.601 Contrastive_loss: 0.35450 (0.23612) Loss: 0.35450 (0.23612) 2025-03-19,07:20:14 | INFO | Train Epoch: 8 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.527/s, 149.527/s/gpu LR: 0.000163 Logit Scale: 25.589 Contrastive_loss: 0.064029 (0.23521) Loss: 0.064029 (0.23521) 2025-03-19,07:20:36 | INFO | Train Epoch: 8 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.233/s, 149.233/s/gpu LR: 0.000163 Logit Scale: 25.561 Contrastive_loss: 0.34765 (0.23580) Loss: 0.34765 (0.23580) 2025-03-19,07:20:57 | INFO | Train Epoch: 8 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.084/s, 148.084/s/gpu LR: 0.000163 Logit Scale: 25.501 Contrastive_loss: 0.32742 (0.23628) Loss: 0.32742 (0.23628) 2025-03-19,07:21:19 | INFO | Train Epoch: 8 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 147.523/s, 147.523/s/gpu LR: 0.000163 Logit Scale: 25.476 Contrastive_loss: 0.17837 (0.23598) Loss: 0.17837 (0.23598) 2025-03-19,07:21:40 | INFO | Train Epoch: 8 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 149.537/s, 149.537/s/gpu LR: 0.000163 Logit Scale: 25.500 Contrastive_loss: 0.20961 (0.23584) Loss: 0.20961 (0.23584) 2025-03-19,07:22:02 | INFO | Train Epoch: 8 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 150.079/s, 150.079/s/gpu LR: 0.000163 Logit Scale: 25.505 Contrastive_loss: 0.45815 (0.23699) Loss: 0.45815 (0.23699) 2025-03-19,07:22:24 | INFO | Train Epoch: 8 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.217, 146.920/s, 146.920/s/gpu LR: 0.000163 Logit Scale: 25.498 Contrastive_loss: 0.21930 (0.23690) Loss: 0.21930 (0.23690) 2025-03-19,07:22:45 | INFO | Train Epoch: 8 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 150.983/s, 150.983/s/gpu LR: 0.000163 Logit Scale: 25.529 Contrastive_loss: 0.26548 (0.23705) Loss: 0.26548 (0.23705) 2025-03-19,07:23:07 | INFO | Train Epoch: 8 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 150.932/s, 150.932/s/gpu LR: 0.000163 Logit Scale: 25.524 Contrastive_loss: 0.23821 (0.23706) Loss: 0.23821 (0.23706) 2025-03-19,07:23:28 | INFO | Train Epoch: 8 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 145.309/s, 145.309/s/gpu LR: 0.000163 Logit Scale: 25.495 Contrastive_loss: 0.27824 (0.23726) Loss: 0.27824 (0.23726) 2025-03-19,07:23:50 | INFO | Train Epoch: 8 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.219, 146.130/s, 146.130/s/gpu LR: 0.000163 Logit Scale: 25.499 Contrastive_loss: 0.38020 (0.23799) Loss: 0.38020 (0.23799) 2025-03-19,07:24:12 | INFO | Train Epoch: 8 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.217, 147.368/s, 147.368/s/gpu LR: 0.000163 Logit Scale: 25.479 Contrastive_loss: 0.62423 (0.23993) Loss: 0.62423 (0.23993) 2025-03-19,07:24:34 | INFO | Train Epoch: 8 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 147.023/s, 147.023/s/gpu LR: 0.000163 Logit Scale: 25.510 Contrastive_loss: 0.28724 (0.24016) Loss: 0.28724 (0.24016) 2025-03-19,07:24:55 | INFO | Train Epoch: 8 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 151.592/s, 151.592/s/gpu LR: 0.000163 Logit Scale: 25.477 Contrastive_loss: 0.26143 (0.24027) Loss: 0.26143 (0.24027) 2025-03-19,07:25:17 | INFO | Train Epoch: 8 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 147.726/s, 147.726/s/gpu LR: 0.000163 Logit Scale: 25.487 Contrastive_loss: 0.26353 (0.24038) Loss: 0.26353 (0.24038) 2025-03-19,07:25:38 | INFO | Train Epoch: 8 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 151.180/s, 151.180/s/gpu LR: 0.000163 Logit Scale: 25.504 Contrastive_loss: 0.35256 (0.24094) Loss: 0.35256 (0.24094) 2025-03-19,07:26:00 | INFO | Train Epoch: 8 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.212, 146.980/s, 146.980/s/gpu LR: 0.000163 Logit Scale: 25.506 Contrastive_loss: 0.36101 (0.24153) Loss: 0.36101 (0.24153) 2025-03-19,07:26:21 | INFO | Train Epoch: 8 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 147.663/s, 147.663/s/gpu LR: 0.000162 Logit Scale: 25.558 Contrastive_loss: 0.23725 (0.24151) Loss: 0.23725 (0.24151) 2025-03-19,07:26:42 | INFO | Train Epoch: 8 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.212, 151.609/s, 151.609/s/gpu LR: 0.000162 Logit Scale: 25.565 Contrastive_loss: 0.090363 (0.24077) Loss: 0.090363 (0.24077) 2025-03-19,07:27:04 | INFO | Train Epoch: 8 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.211, 150.465/s, 150.465/s/gpu LR: 0.000162 Logit Scale: 25.598 Contrastive_loss: 0.17601 (0.24046) Loss: 0.17601 (0.24046) 2025-03-19,07:27:25 | INFO | Train Epoch: 8 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.213, 148.870/s, 148.870/s/gpu LR: 0.000162 Logit Scale: 25.618 Contrastive_loss: 0.32552 (0.24087) Loss: 0.32552 (0.24087) 2025-03-19,07:27:46 | INFO | Train Epoch: 8 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 149.092/s, 149.092/s/gpu LR: 0.000162 Logit Scale: 25.586 Contrastive_loss: 0.31749 (0.24123) Loss: 0.31749 (0.24123) 2025-03-19,07:28:08 | INFO | Train Epoch: 8 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 148.191/s, 148.191/s/gpu LR: 0.000162 Logit Scale: 25.611 Contrastive_loss: 0.32971 (0.24166) Loss: 0.32971 (0.24166) 2025-03-19,07:28:29 | INFO | Train Epoch: 8 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.800/s, 149.800/s/gpu LR: 0.000162 Logit Scale: 25.595 Contrastive_loss: 0.15605 (0.24125) Loss: 0.15605 (0.24125) 2025-03-19,07:28:51 | INFO | Train Epoch: 8 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 147.203/s, 147.203/s/gpu LR: 0.000162 Logit Scale: 25.590 Contrastive_loss: 0.13600 (0.24075) Loss: 0.13600 (0.24075) 2025-03-19,07:29:12 | INFO | Train Epoch: 8 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 151.348/s, 151.348/s/gpu LR: 0.000162 Logit Scale: 25.546 Contrastive_loss: 0.28135 (0.24094) Loss: 0.28135 (0.24094) 2025-03-19,07:29:34 | INFO | Train Epoch: 8 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 142.511/s, 142.511/s/gpu LR: 0.000162 Logit Scale: 25.585 Contrastive_loss: 0.29845 (0.24121) Loss: 0.29845 (0.24121) 2025-03-19,07:29:56 | INFO | Train Epoch: 8 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.221, 146.103/s, 146.103/s/gpu LR: 0.000162 Logit Scale: 25.563 Contrastive_loss: 0.12945 (0.24069) Loss: 0.12945 (0.24069) 2025-03-19,07:30:18 | INFO | Train Epoch: 8 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.219, 146.829/s, 146.829/s/gpu LR: 0.000162 Logit Scale: 25.593 Contrastive_loss: 0.21284 (0.24056) Loss: 0.21284 (0.24056) 2025-03-19,07:30:40 | INFO | Train Epoch: 8 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.217, 151.096/s, 151.096/s/gpu LR: 0.000162 Logit Scale: 25.603 Contrastive_loss: 0.41877 (0.24139) Loss: 0.41877 (0.24139) 2025-03-19,07:31:01 | INFO | Train Epoch: 8 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 151.291/s, 151.291/s/gpu LR: 0.000162 Logit Scale: 25.605 Contrastive_loss: 0.26748 (0.24150) Loss: 0.26748 (0.24150) 2025-03-19,07:31:23 | INFO | Train Epoch: 8 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.766/s, 148.766/s/gpu LR: 0.000162 Logit Scale: 25.543 Contrastive_loss: 0.15230 (0.24110) Loss: 0.15230 (0.24110) 2025-03-19,07:31:45 | INFO | Train Epoch: 8 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.220, 146.235/s, 146.235/s/gpu LR: 0.000162 Logit Scale: 25.578 Contrastive_loss: 0.36644 (0.24167) Loss: 0.36644 (0.24167) 2025-03-19,07:32:06 | INFO | Train Epoch: 8 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 146.994/s, 146.994/s/gpu LR: 0.000162 Logit Scale: 25.532 Contrastive_loss: 0.29766 (0.24192) Loss: 0.29766 (0.24192) 2025-03-19,07:32:28 | INFO | Train Epoch: 8 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 148.770/s, 148.770/s/gpu LR: 0.000162 Logit Scale: 25.565 Contrastive_loss: 0.37761 (0.24253) Loss: 0.37761 (0.24253) 2025-03-19,07:32:50 | INFO | Train Epoch: 8 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 150.288/s, 150.288/s/gpu LR: 0.000162 Logit Scale: 25.519 Contrastive_loss: 0.21312 (0.24240) Loss: 0.21312 (0.24240) 2025-03-19,07:33:11 | INFO | Train Epoch: 8 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 145.135/s, 145.135/s/gpu LR: 0.000162 Logit Scale: 25.553 Contrastive_loss: 0.35211 (0.24289) Loss: 0.35211 (0.24289) 2025-03-19,07:33:33 | INFO | Train Epoch: 8 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.221, 148.790/s, 148.790/s/gpu LR: 0.000162 Logit Scale: 25.541 Contrastive_loss: 0.14843 (0.24247) Loss: 0.14843 (0.24247) 2025-03-19,07:33:55 | INFO | Train Epoch: 8 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.699/s, 148.699/s/gpu LR: 0.000162 Logit Scale: 25.533 Contrastive_loss: 0.22970 (0.24241) Loss: 0.22970 (0.24241) 2025-03-19,07:34:16 | INFO | Train Epoch: 8 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.523/s, 148.523/s/gpu LR: 0.000162 Logit Scale: 25.563 Contrastive_loss: 0.11364 (0.24185) Loss: 0.11364 (0.24185) 2025-03-19,07:34:38 | INFO | Train Epoch: 8 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 148.799/s, 148.799/s/gpu LR: 0.000162 Logit Scale: 25.633 Contrastive_loss: 0.15167 (0.24145) Loss: 0.15167 (0.24145) 2025-03-19,07:35:00 | INFO | Train Epoch: 8 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.218, 148.664/s, 148.664/s/gpu LR: 0.000162 Logit Scale: 25.598 Contrastive_loss: 0.31695 (0.24178) Loss: 0.31695 (0.24178) 2025-03-19,07:35:21 | INFO | Train Epoch: 8 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 147.671/s, 147.671/s/gpu LR: 0.000162 Logit Scale: 25.572 Contrastive_loss: 0.15724 (0.24141) Loss: 0.15724 (0.24141) 2025-03-19,07:35:43 | INFO | Train Epoch: 8 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 146.976/s, 146.976/s/gpu LR: 0.000162 Logit Scale: 25.605 Contrastive_loss: 0.21854 (0.24131) Loss: 0.21854 (0.24131) 2025-03-19,07:36:04 | INFO | Train Epoch: 8 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.217, 144.987/s, 144.987/s/gpu LR: 0.000162 Logit Scale: 25.598 Contrastive_loss: 0.18088 (0.24105) Loss: 0.18088 (0.24105) 2025-03-19,07:36:26 | INFO | Train Epoch: 8 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.220, 147.633/s, 147.633/s/gpu LR: 0.000162 Logit Scale: 25.596 Contrastive_loss: 0.28974 (0.24126) Loss: 0.28974 (0.24126) 2025-03-19,07:36:48 | INFO | Train Epoch: 8 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 147.081/s, 147.081/s/gpu LR: 0.000161 Logit Scale: 25.619 Contrastive_loss: 0.077986 (0.24056) Loss: 0.077986 (0.24056) 2025-03-19,07:37:10 | INFO | Train Epoch: 8 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 146.484/s, 146.484/s/gpu LR: 0.000161 Logit Scale: 25.563 Contrastive_loss: 0.35500 (0.24105) Loss: 0.35500 (0.24105) 2025-03-19,07:37:31 | INFO | Train Epoch: 8 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 143.192/s, 143.192/s/gpu LR: 0.000161 Logit Scale: 25.575 Contrastive_loss: 0.27754 (0.24121) Loss: 0.27754 (0.24121) 2025-03-19,07:37:53 | INFO | Train Epoch: 8 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.219, 152.024/s, 152.024/s/gpu LR: 0.000161 Logit Scale: 25.569 Contrastive_loss: 0.064439 (0.24046) Loss: 0.064439 (0.24046) 2025-03-19,07:38:15 | INFO | Train Epoch: 8 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 151.180/s, 151.180/s/gpu LR: 0.000161 Logit Scale: 25.574 Contrastive_loss: 0.22669 (0.24040) Loss: 0.22669 (0.24040) 2025-03-19,07:38:36 | INFO | Train Epoch: 8 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.632/s, 149.632/s/gpu LR: 0.000161 Logit Scale: 25.574 Contrastive_loss: 0.26496 (0.24050) Loss: 0.26496 (0.24050) 2025-03-19,07:38:58 | INFO | Train Epoch: 8 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.215, 148.368/s, 148.368/s/gpu LR: 0.000161 Logit Scale: 25.572 Contrastive_loss: 0.071865 (0.23980) Loss: 0.071865 (0.23980) 2025-03-19,07:39:05 | INFO | Train Epoch: 8 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.217, 147.689/s, 147.689/s/gpu LR: 0.000161 Logit Scale: 25.601 Contrastive_loss: 0.41478 (0.24053) Loss: 0.41478 (0.24053) 2025-03-19,07:39:06 | INFO | Eval Epoch: 9 [32 / 7443] Clip Loss: 3.651047 2025-03-19,07:39:11 | INFO | Eval Epoch: 9 [3232 / 7443] Clip Loss: 0.926250 2025-03-19,07:39:17 | INFO | Eval Epoch: 9 [6432 / 7443] Clip Loss: 0.716814 2025-03-19,07:39:20 | INFO | Eval Epoch: 9 image_to_text_mean_rank: 107.5384 image_to_text_median_rank: 8.0000 image_to_text_R@1: 0.1151 image_to_text_R@5: 0.4087 image_to_text_R@10: 0.5858 text_to_image_mean_rank: 68.3478 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1258 text_to_image_R@5: 0.4169 text_to_image_R@10: 0.5906 clip_val_loss: 0.6697 epoch: 9.0000 num_samples: 7443.0000 2025-03-19,07:39:52 | INFO | Start epoch 9 2025-03-19,07:39:53 | INFO | Train Epoch: 9 [ 32/766009 (0%)] Data (t): 0.162 Batch (t): 0.364, 87.8548/s, 87.8548/s/gpu LR: 0.000161 Logit Scale: 25.602 Contrastive_loss: 0.088381 (0.088381) Loss: 0.088381 (0.088381) 2025-03-19,07:40:14 | INFO | Train Epoch: 9 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 152.077/s, 152.077/s/gpu LR: 0.000161 Logit Scale: 25.657 Contrastive_loss: 0.22899 (0.15868) Loss: 0.22899 (0.15868) 2025-03-19,07:40:35 | INFO | Train Epoch: 9 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 146.631/s, 146.631/s/gpu LR: 0.000161 Logit Scale: 25.713 Contrastive_loss: 0.13671 (0.15136) Loss: 0.13671 (0.15136) 2025-03-19,07:40:57 | INFO | Train Epoch: 9 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 147.058/s, 147.058/s/gpu LR: 0.000161 Logit Scale: 25.722 Contrastive_loss: 0.20088 (0.16374) Loss: 0.20088 (0.16374) 2025-03-19,07:41:19 | INFO | Train Epoch: 9 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.220, 147.257/s, 147.257/s/gpu LR: 0.000161 Logit Scale: 25.701 Contrastive_loss: 0.086358 (0.14826) Loss: 0.086358 (0.14826) 2025-03-19,07:41:41 | INFO | Train Epoch: 9 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.218, 147.990/s, 147.990/s/gpu LR: 0.000161 Logit Scale: 25.746 Contrastive_loss: 0.12216 (0.14391) Loss: 0.12216 (0.14391) 2025-03-19,07:42:03 | INFO | Train Epoch: 9 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 146.894/s, 146.894/s/gpu LR: 0.000161 Logit Scale: 25.755 Contrastive_loss: 0.10692 (0.13863) Loss: 0.10692 (0.13863) 2025-03-19,07:42:24 | INFO | Train Epoch: 9 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 148.743/s, 148.743/s/gpu LR: 0.000161 Logit Scale: 25.796 Contrastive_loss: 0.028615 (0.12488) Loss: 0.028615 (0.12488) 2025-03-19,07:42:46 | INFO | Train Epoch: 9 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 148.096/s, 148.096/s/gpu LR: 0.000161 Logit Scale: 25.789 Contrastive_loss: 0.10986 (0.12321) Loss: 0.10986 (0.12321) 2025-03-19,07:43:08 | INFO | Train Epoch: 9 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 147.594/s, 147.594/s/gpu LR: 0.000161 Logit Scale: 25.808 Contrastive_loss: 0.26291 (0.13718) Loss: 0.26291 (0.13718) 2025-03-19,07:43:29 | INFO | Train Epoch: 9 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.216, 148.636/s, 148.636/s/gpu LR: 0.000161 Logit Scale: 25.774 Contrastive_loss: 0.074154 (0.13145) Loss: 0.074154 (0.13145) 2025-03-19,07:43:51 | INFO | Train Epoch: 9 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 147.496/s, 147.496/s/gpu LR: 0.000161 Logit Scale: 25.745 Contrastive_loss: 0.077219 (0.12693) Loss: 0.077219 (0.12693) 2025-03-19,07:44:12 | INFO | Train Epoch: 9 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 148.657/s, 148.657/s/gpu LR: 0.000161 Logit Scale: 25.735 Contrastive_loss: 0.47898 (0.15401) Loss: 0.47898 (0.15401) 2025-03-19,07:44:34 | INFO | Train Epoch: 9 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.857/s, 148.857/s/gpu LR: 0.000161 Logit Scale: 25.744 Contrastive_loss: 0.24503 (0.16051) Loss: 0.24503 (0.16051) 2025-03-19,07:44:56 | INFO | Train Epoch: 9 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 147.988/s, 147.988/s/gpu LR: 0.000161 Logit Scale: 25.733 Contrastive_loss: 0.16550 (0.16084) Loss: 0.16550 (0.16084) 2025-03-19,07:45:18 | INFO | Train Epoch: 9 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.220, 146.923/s, 146.923/s/gpu LR: 0.000161 Logit Scale: 25.728 Contrastive_loss: 0.14719 (0.15999) Loss: 0.14719 (0.15999) 2025-03-19,07:45:39 | INFO | Train Epoch: 9 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 148.726/s, 148.726/s/gpu LR: 0.000161 Logit Scale: 25.717 Contrastive_loss: 0.084627 (0.15556) Loss: 0.084627 (0.15556) 2025-03-19,07:46:01 | INFO | Train Epoch: 9 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 150.196/s, 150.196/s/gpu LR: 0.000161 Logit Scale: 25.679 Contrastive_loss: 0.12728 (0.15399) Loss: 0.12728 (0.15399) 2025-03-19,07:46:22 | INFO | Train Epoch: 9 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.056/s, 149.056/s/gpu LR: 0.000161 Logit Scale: 25.656 Contrastive_loss: 0.33113 (0.16331) Loss: 0.33113 (0.16331) 2025-03-19,07:46:44 | INFO | Train Epoch: 9 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 150.164/s, 150.164/s/gpu LR: 0.000161 Logit Scale: 25.648 Contrastive_loss: 0.076818 (0.15899) Loss: 0.076818 (0.15899) 2025-03-19,07:47:05 | INFO | Train Epoch: 9 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.242/s, 149.242/s/gpu LR: 0.000161 Logit Scale: 25.667 Contrastive_loss: 0.049630 (0.15378) Loss: 0.049630 (0.15378) 2025-03-19,07:47:27 | INFO | Train Epoch: 9 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 150.124/s, 150.124/s/gpu LR: 0.000161 Logit Scale: 25.745 Contrastive_loss: 0.20688 (0.15619) Loss: 0.20688 (0.15619) 2025-03-19,07:47:48 | INFO | Train Epoch: 9 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.213, 151.312/s, 151.312/s/gpu LR: 0.000160 Logit Scale: 25.710 Contrastive_loss: 0.087027 (0.15319) Loss: 0.087027 (0.15319) 2025-03-19,07:48:09 | INFO | Train Epoch: 9 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.211, 151.988/s, 151.988/s/gpu LR: 0.000160 Logit Scale: 25.703 Contrastive_loss: 0.33963 (0.16095) Loss: 0.33963 (0.16095) 2025-03-19,07:48:31 | INFO | Train Epoch: 9 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.212, 149.019/s, 149.019/s/gpu LR: 0.000160 Logit Scale: 25.693 Contrastive_loss: 0.19079 (0.16215) Loss: 0.19079 (0.16215) 2025-03-19,07:48:52 | INFO | Train Epoch: 9 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.461/s, 149.461/s/gpu LR: 0.000160 Logit Scale: 25.734 Contrastive_loss: 0.33113 (0.16865) Loss: 0.33113 (0.16865) 2025-03-19,07:49:14 | INFO | Train Epoch: 9 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 149.794/s, 149.794/s/gpu LR: 0.000160 Logit Scale: 25.716 Contrastive_loss: 0.28311 (0.17289) Loss: 0.28311 (0.17289) 2025-03-19,07:49:35 | INFO | Train Epoch: 9 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.215, 149.248/s, 149.248/s/gpu LR: 0.000160 Logit Scale: 25.744 Contrastive_loss: 0.21389 (0.17435) Loss: 0.21389 (0.17435) 2025-03-19,07:49:57 | INFO | Train Epoch: 9 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.311/s, 148.311/s/gpu LR: 0.000160 Logit Scale: 25.752 Contrastive_loss: 0.29939 (0.17866) Loss: 0.29939 (0.17866) 2025-03-19,07:50:18 | INFO | Train Epoch: 9 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.498/s, 148.498/s/gpu LR: 0.000160 Logit Scale: 25.742 Contrastive_loss: 0.33418 (0.18385) Loss: 0.33418 (0.18385) 2025-03-19,07:50:40 | INFO | Train Epoch: 9 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.707/s, 149.707/s/gpu LR: 0.000160 Logit Scale: 25.754 Contrastive_loss: 0.18854 (0.18400) Loss: 0.18854 (0.18400) 2025-03-19,07:51:01 | INFO | Train Epoch: 9 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 151.239/s, 151.239/s/gpu LR: 0.000160 Logit Scale: 25.730 Contrastive_loss: 0.28114 (0.18703) Loss: 0.28114 (0.18703) 2025-03-19,07:51:23 | INFO | Train Epoch: 9 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.212, 150.444/s, 150.444/s/gpu LR: 0.000160 Logit Scale: 25.734 Contrastive_loss: 0.14870 (0.18587) Loss: 0.14870 (0.18587) 2025-03-19,07:51:44 | INFO | Train Epoch: 9 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 150.286/s, 150.286/s/gpu LR: 0.000160 Logit Scale: 25.745 Contrastive_loss: 0.16021 (0.18512) Loss: 0.16021 (0.18512) 2025-03-19,07:52:05 | INFO | Train Epoch: 9 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 149.898/s, 149.898/s/gpu LR: 0.000160 Logit Scale: 25.720 Contrastive_loss: 0.24032 (0.18669) Loss: 0.24032 (0.18669) 2025-03-19,07:52:27 | INFO | Train Epoch: 9 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.238/s, 149.238/s/gpu LR: 0.000160 Logit Scale: 25.721 Contrastive_loss: 0.20572 (0.18722) Loss: 0.20572 (0.18722) 2025-03-19,07:52:48 | INFO | Train Epoch: 9 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.172/s, 149.172/s/gpu LR: 0.000160 Logit Scale: 25.731 Contrastive_loss: 0.096498 (0.18477) Loss: 0.096498 (0.18477) 2025-03-19,07:53:10 | INFO | Train Epoch: 9 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 149.040/s, 149.040/s/gpu LR: 0.000160 Logit Scale: 25.721 Contrastive_loss: 0.52202 (0.19365) Loss: 0.52202 (0.19365) 2025-03-19,07:53:31 | INFO | Train Epoch: 9 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 148.902/s, 148.902/s/gpu LR: 0.000160 Logit Scale: 25.720 Contrastive_loss: 0.12261 (0.19182) Loss: 0.12261 (0.19182) 2025-03-19,07:53:53 | INFO | Train Epoch: 9 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 149.071/s, 149.071/s/gpu LR: 0.000160 Logit Scale: 25.701 Contrastive_loss: 0.29006 (0.19428) Loss: 0.29006 (0.19428) 2025-03-19,07:54:14 | INFO | Train Epoch: 9 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 150.029/s, 150.029/s/gpu LR: 0.000160 Logit Scale: 25.721 Contrastive_loss: 0.21369 (0.19475) Loss: 0.21369 (0.19475) 2025-03-19,07:54:36 | INFO | Train Epoch: 9 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 149.041/s, 149.041/s/gpu LR: 0.000160 Logit Scale: 25.742 Contrastive_loss: 0.10733 (0.19267) Loss: 0.10733 (0.19267) 2025-03-19,07:54:57 | INFO | Train Epoch: 9 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 148.673/s, 148.673/s/gpu LR: 0.000160 Logit Scale: 25.727 Contrastive_loss: 0.21350 (0.19316) Loss: 0.21350 (0.19316) 2025-03-19,07:55:19 | INFO | Train Epoch: 9 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.139/s, 149.139/s/gpu LR: 0.000160 Logit Scale: 25.717 Contrastive_loss: 0.37815 (0.19736) Loss: 0.37815 (0.19736) 2025-03-19,07:55:40 | INFO | Train Epoch: 9 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 151.984/s, 151.984/s/gpu LR: 0.000160 Logit Scale: 25.725 Contrastive_loss: 0.31532 (0.19998) Loss: 0.31532 (0.19998) 2025-03-19,07:56:01 | INFO | Train Epoch: 9 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.213, 147.906/s, 147.906/s/gpu LR: 0.000160 Logit Scale: 25.685 Contrastive_loss: 0.21315 (0.20027) Loss: 0.21315 (0.20027) 2025-03-19,07:56:23 | INFO | Train Epoch: 9 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 150.314/s, 150.314/s/gpu LR: 0.000160 Logit Scale: 25.687 Contrastive_loss: 0.19894 (0.20024) Loss: 0.19894 (0.20024) 2025-03-19,07:56:44 | INFO | Train Epoch: 9 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 147.340/s, 147.340/s/gpu LR: 0.000160 Logit Scale: 25.722 Contrastive_loss: 0.20186 (0.20027) Loss: 0.20186 (0.20027) 2025-03-19,07:57:06 | INFO | Train Epoch: 9 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 150.117/s, 150.117/s/gpu LR: 0.000160 Logit Scale: 25.723 Contrastive_loss: 0.25363 (0.20136) Loss: 0.25363 (0.20136) 2025-03-19,07:57:27 | INFO | Train Epoch: 9 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 148.153/s, 148.153/s/gpu LR: 0.000160 Logit Scale: 25.704 Contrastive_loss: 0.28002 (0.20294) Loss: 0.28002 (0.20294) 2025-03-19,07:57:49 | INFO | Train Epoch: 9 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 148.861/s, 148.861/s/gpu LR: 0.000159 Logit Scale: 25.759 Contrastive_loss: 0.053138 (0.20000) Loss: 0.053138 (0.20000) 2025-03-19,07:58:10 | INFO | Train Epoch: 9 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 148.093/s, 148.093/s/gpu LR: 0.000159 Logit Scale: 25.723 Contrastive_loss: 0.18227 (0.19966) Loss: 0.18227 (0.19966) 2025-03-19,07:58:32 | INFO | Train Epoch: 9 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 149.416/s, 149.416/s/gpu LR: 0.000159 Logit Scale: 25.776 Contrastive_loss: 0.53335 (0.20595) Loss: 0.53335 (0.20595) 2025-03-19,07:58:53 | INFO | Train Epoch: 9 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.095/s, 149.095/s/gpu LR: 0.000159 Logit Scale: 25.760 Contrastive_loss: 0.12730 (0.20450) Loss: 0.12730 (0.20450) 2025-03-19,07:59:15 | INFO | Train Epoch: 9 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.361/s, 149.361/s/gpu LR: 0.000159 Logit Scale: 25.690 Contrastive_loss: 0.32089 (0.20661) Loss: 0.32089 (0.20661) 2025-03-19,07:59:36 | INFO | Train Epoch: 9 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 148.587/s, 148.587/s/gpu LR: 0.000159 Logit Scale: 25.672 Contrastive_loss: 0.37454 (0.20961) Loss: 0.37454 (0.20961) 2025-03-19,07:59:58 | INFO | Train Epoch: 9 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 147.518/s, 147.518/s/gpu LR: 0.000159 Logit Scale: 25.712 Contrastive_loss: 0.40128 (0.21297) Loss: 0.40128 (0.21297) 2025-03-19,08:00:20 | INFO | Train Epoch: 9 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.219, 148.198/s, 148.198/s/gpu LR: 0.000159 Logit Scale: 25.644 Contrastive_loss: 0.35141 (0.21536) Loss: 0.35141 (0.21536) 2025-03-19,08:00:41 | INFO | Train Epoch: 9 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 146.206/s, 146.206/s/gpu LR: 0.000159 Logit Scale: 25.693 Contrastive_loss: 0.27033 (0.21629) Loss: 0.27033 (0.21629) 2025-03-19,08:01:03 | INFO | Train Epoch: 9 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 146.851/s, 146.851/s/gpu LR: 0.000159 Logit Scale: 25.717 Contrastive_loss: 0.18886 (0.21584) Loss: 0.18886 (0.21584) 2025-03-19,08:01:25 | INFO | Train Epoch: 9 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 146.755/s, 146.755/s/gpu LR: 0.000159 Logit Scale: 25.696 Contrastive_loss: 0.39006 (0.21869) Loss: 0.39006 (0.21869) 2025-03-19,08:01:46 | INFO | Train Epoch: 9 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 148.040/s, 148.040/s/gpu LR: 0.000159 Logit Scale: 25.682 Contrastive_loss: 0.24949 (0.21919) Loss: 0.24949 (0.21919) 2025-03-19,08:02:08 | INFO | Train Epoch: 9 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.213, 149.961/s, 149.961/s/gpu LR: 0.000159 Logit Scale: 25.745 Contrastive_loss: 0.19900 (0.21887) Loss: 0.19900 (0.21887) 2025-03-19,08:02:30 | INFO | Train Epoch: 9 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.218, 147.604/s, 147.604/s/gpu LR: 0.000159 Logit Scale: 25.776 Contrastive_loss: 0.026821 (0.21587) Loss: 0.026821 (0.21587) 2025-03-19,08:02:51 | INFO | Train Epoch: 9 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.218, 145.208/s, 145.208/s/gpu LR: 0.000159 Logit Scale: 25.749 Contrastive_loss: 0.11992 (0.21439) Loss: 0.11992 (0.21439) 2025-03-19,08:03:13 | INFO | Train Epoch: 9 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.220, 148.254/s, 148.254/s/gpu LR: 0.000159 Logit Scale: 25.756 Contrastive_loss: 0.36071 (0.21661) Loss: 0.36071 (0.21661) 2025-03-19,08:03:35 | INFO | Train Epoch: 9 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.219, 144.911/s, 144.911/s/gpu LR: 0.000159 Logit Scale: 25.733 Contrastive_loss: 0.15018 (0.21562) Loss: 0.15018 (0.21562) 2025-03-19,08:03:57 | INFO | Train Epoch: 9 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.219, 148.555/s, 148.555/s/gpu LR: 0.000159 Logit Scale: 25.748 Contrastive_loss: 0.036612 (0.21298) Loss: 0.036612 (0.21298) 2025-03-19,08:04:19 | INFO | Train Epoch: 9 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 149.139/s, 149.139/s/gpu LR: 0.000159 Logit Scale: 25.703 Contrastive_loss: 0.24372 (0.21343) Loss: 0.24372 (0.21343) 2025-03-19,08:04:41 | INFO | Train Epoch: 9 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.218, 147.484/s, 147.484/s/gpu LR: 0.000159 Logit Scale: 25.769 Contrastive_loss: 0.21290 (0.21342) Loss: 0.21290 (0.21342) 2025-03-19,08:05:02 | INFO | Train Epoch: 9 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 146.565/s, 146.565/s/gpu LR: 0.000159 Logit Scale: 25.717 Contrastive_loss: 0.17260 (0.21285) Loss: 0.17260 (0.21285) 2025-03-19,08:05:24 | INFO | Train Epoch: 9 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 149.638/s, 149.638/s/gpu LR: 0.000159 Logit Scale: 25.757 Contrastive_loss: 0.25689 (0.21346) Loss: 0.25689 (0.21346) 2025-03-19,08:05:45 | INFO | Train Epoch: 9 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 146.607/s, 146.607/s/gpu LR: 0.000159 Logit Scale: 25.694 Contrastive_loss: 0.54488 (0.21800) Loss: 0.54488 (0.21800) 2025-03-19,08:06:07 | INFO | Train Epoch: 9 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.212, 150.147/s, 150.147/s/gpu LR: 0.000159 Logit Scale: 25.662 Contrastive_loss: 0.33774 (0.21962) Loss: 0.33774 (0.21962) 2025-03-19,08:06:28 | INFO | Train Epoch: 9 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.220, 147.969/s, 147.969/s/gpu LR: 0.000159 Logit Scale: 25.697 Contrastive_loss: 0.42342 (0.22233) Loss: 0.42342 (0.22233) 2025-03-19,08:06:50 | INFO | Train Epoch: 9 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 148.003/s, 148.003/s/gpu LR: 0.000159 Logit Scale: 25.711 Contrastive_loss: 0.079766 (0.22046) Loss: 0.079766 (0.22046) 2025-03-19,08:07:12 | INFO | Train Epoch: 9 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.217, 147.274/s, 147.274/s/gpu LR: 0.000159 Logit Scale: 25.728 Contrastive_loss: 0.12811 (0.21926) Loss: 0.12811 (0.21926) 2025-03-19,08:07:33 | INFO | Train Epoch: 9 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 150.146/s, 150.146/s/gpu LR: 0.000159 Logit Scale: 25.712 Contrastive_loss: 0.17506 (0.21869) Loss: 0.17506 (0.21869) 2025-03-19,08:07:55 | INFO | Train Epoch: 9 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 149.261/s, 149.261/s/gpu LR: 0.000158 Logit Scale: 25.752 Contrastive_loss: 0.26368 (0.21926) Loss: 0.26368 (0.21926) 2025-03-19,08:08:16 | INFO | Train Epoch: 9 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.090/s, 150.090/s/gpu LR: 0.000158 Logit Scale: 25.742 Contrastive_loss: 0.27193 (0.21992) Loss: 0.27193 (0.21992) 2025-03-19,08:08:38 | INFO | Train Epoch: 9 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.547/s, 147.547/s/gpu LR: 0.000158 Logit Scale: 25.740 Contrastive_loss: 0.23126 (0.22006) Loss: 0.23126 (0.22006) 2025-03-19,08:09:00 | INFO | Train Epoch: 9 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.218, 148.041/s, 148.041/s/gpu LR: 0.000158 Logit Scale: 25.699 Contrastive_loss: 0.40340 (0.22230) Loss: 0.40340 (0.22230) 2025-03-19,08:09:21 | INFO | Train Epoch: 9 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.217, 136.043/s, 136.043/s/gpu LR: 0.000158 Logit Scale: 25.716 Contrastive_loss: 0.14713 (0.22139) Loss: 0.14713 (0.22139) 2025-03-19,08:09:43 | INFO | Train Epoch: 9 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.217, 145.757/s, 145.757/s/gpu LR: 0.000158 Logit Scale: 25.737 Contrastive_loss: 0.39204 (0.22342) Loss: 0.39204 (0.22342) 2025-03-19,08:10:05 | INFO | Train Epoch: 9 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 148.735/s, 148.735/s/gpu LR: 0.000158 Logit Scale: 25.727 Contrastive_loss: 0.10638 (0.22205) Loss: 0.10638 (0.22205) 2025-03-19,08:10:26 | INFO | Train Epoch: 9 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.911/s, 148.911/s/gpu LR: 0.000158 Logit Scale: 25.761 Contrastive_loss: 0.19788 (0.22176) Loss: 0.19788 (0.22176) 2025-03-19,08:10:48 | INFO | Train Epoch: 9 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 150.017/s, 150.017/s/gpu LR: 0.000158 Logit Scale: 25.745 Contrastive_loss: 0.32574 (0.22296) Loss: 0.32574 (0.22296) 2025-03-19,08:11:09 | INFO | Train Epoch: 9 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 150.090/s, 150.090/s/gpu LR: 0.000158 Logit Scale: 25.749 Contrastive_loss: 0.51242 (0.22625) Loss: 0.51242 (0.22625) 2025-03-19,08:11:30 | INFO | Train Epoch: 9 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 150.024/s, 150.024/s/gpu LR: 0.000158 Logit Scale: 25.710 Contrastive_loss: 0.13197 (0.22519) Loss: 0.13197 (0.22519) 2025-03-19,08:11:52 | INFO | Train Epoch: 9 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 149.657/s, 149.657/s/gpu LR: 0.000158 Logit Scale: 25.671 Contrastive_loss: 0.25424 (0.22551) Loss: 0.25424 (0.22551) 2025-03-19,08:12:13 | INFO | Train Epoch: 9 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 150.393/s, 150.393/s/gpu LR: 0.000158 Logit Scale: 25.688 Contrastive_loss: 0.20263 (0.22526) Loss: 0.20263 (0.22526) 2025-03-19,08:12:35 | INFO | Train Epoch: 9 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.545/s, 149.545/s/gpu LR: 0.000158 Logit Scale: 25.751 Contrastive_loss: 0.061902 (0.22349) Loss: 0.061902 (0.22349) 2025-03-19,08:12:56 | INFO | Train Epoch: 9 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 150.212/s, 150.212/s/gpu LR: 0.000158 Logit Scale: 25.777 Contrastive_loss: 0.10441 (0.22220) Loss: 0.10441 (0.22220) 2025-03-19,08:13:18 | INFO | Train Epoch: 9 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 147.708/s, 147.708/s/gpu LR: 0.000158 Logit Scale: 25.774 Contrastive_loss: 0.28875 (0.22291) Loss: 0.28875 (0.22291) 2025-03-19,08:13:39 | INFO | Train Epoch: 9 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.582/s, 149.582/s/gpu LR: 0.000158 Logit Scale: 25.790 Contrastive_loss: 0.072411 (0.22133) Loss: 0.072411 (0.22133) 2025-03-19,08:14:01 | INFO | Train Epoch: 9 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 149.582/s, 149.582/s/gpu LR: 0.000158 Logit Scale: 25.826 Contrastive_loss: 0.10313 (0.22010) Loss: 0.10313 (0.22010) 2025-03-19,08:14:22 | INFO | Train Epoch: 9 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 149.615/s, 149.615/s/gpu LR: 0.000158 Logit Scale: 25.821 Contrastive_loss: 0.26212 (0.22053) Loss: 0.26212 (0.22053) 2025-03-19,08:14:44 | INFO | Train Epoch: 9 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 152.279/s, 152.279/s/gpu LR: 0.000158 Logit Scale: 25.834 Contrastive_loss: 0.076673 (0.21906) Loss: 0.076673 (0.21906) 2025-03-19,08:15:05 | INFO | Train Epoch: 9 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 147.452/s, 147.452/s/gpu LR: 0.000158 Logit Scale: 25.863 Contrastive_loss: 0.25210 (0.21940) Loss: 0.25210 (0.21940) 2025-03-19,08:15:27 | INFO | Train Epoch: 9 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.714/s, 148.714/s/gpu LR: 0.000158 Logit Scale: 25.850 Contrastive_loss: 0.25448 (0.21975) Loss: 0.25448 (0.21975) 2025-03-19,08:15:48 | INFO | Train Epoch: 9 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 153.393/s, 153.393/s/gpu LR: 0.000158 Logit Scale: 25.808 Contrastive_loss: 0.086077 (0.21842) Loss: 0.086077 (0.21842) 2025-03-19,08:16:09 | INFO | Train Epoch: 9 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 150.361/s, 150.361/s/gpu LR: 0.000158 Logit Scale: 25.867 Contrastive_loss: 0.11300 (0.21739) Loss: 0.11300 (0.21739) 2025-03-19,08:16:31 | INFO | Train Epoch: 9 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.543/s, 149.543/s/gpu LR: 0.000158 Logit Scale: 25.868 Contrastive_loss: 0.32061 (0.21839) Loss: 0.32061 (0.21839) 2025-03-19,08:16:52 | INFO | Train Epoch: 9 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.837/s, 149.837/s/gpu LR: 0.000158 Logit Scale: 25.840 Contrastive_loss: 0.25694 (0.21876) Loss: 0.25694 (0.21876) 2025-03-19,08:17:14 | INFO | Train Epoch: 9 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.614/s, 149.614/s/gpu LR: 0.000158 Logit Scale: 25.808 Contrastive_loss: 0.23258 (0.21889) Loss: 0.23258 (0.21889) 2025-03-19,08:17:35 | INFO | Train Epoch: 9 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 149.104/s, 149.104/s/gpu LR: 0.000158 Logit Scale: 25.770 Contrastive_loss: 0.17602 (0.21849) Loss: 0.17602 (0.21849) 2025-03-19,08:17:57 | INFO | Train Epoch: 9 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.858/s, 148.858/s/gpu LR: 0.000157 Logit Scale: 25.759 Contrastive_loss: 0.32556 (0.21949) Loss: 0.32556 (0.21949) 2025-03-19,08:18:18 | INFO | Train Epoch: 9 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 151.684/s, 151.684/s/gpu LR: 0.000157 Logit Scale: 25.793 Contrastive_loss: 0.24449 (0.21972) Loss: 0.24449 (0.21972) 2025-03-19,08:18:39 | INFO | Train Epoch: 9 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.213, 148.608/s, 148.608/s/gpu LR: 0.000157 Logit Scale: 25.778 Contrastive_loss: 0.64650 (0.22364) Loss: 0.64650 (0.22364) 2025-03-19,08:19:01 | INFO | Train Epoch: 9 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.577/s, 149.577/s/gpu LR: 0.000157 Logit Scale: 25.714 Contrastive_loss: 0.44665 (0.22566) Loss: 0.44665 (0.22566) 2025-03-19,08:19:22 | INFO | Train Epoch: 9 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.756/s, 149.756/s/gpu LR: 0.000157 Logit Scale: 25.716 Contrastive_loss: 0.068707 (0.22425) Loss: 0.068707 (0.22425) 2025-03-19,08:19:44 | INFO | Train Epoch: 9 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 148.711/s, 148.711/s/gpu LR: 0.000157 Logit Scale: 25.748 Contrastive_loss: 0.36280 (0.22549) Loss: 0.36280 (0.22549) 2025-03-19,08:20:05 | INFO | Train Epoch: 9 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 147.736/s, 147.736/s/gpu LR: 0.000157 Logit Scale: 25.748 Contrastive_loss: 0.19517 (0.22522) Loss: 0.19517 (0.22522) 2025-03-19,08:20:27 | INFO | Train Epoch: 9 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.213, 150.573/s, 150.573/s/gpu LR: 0.000157 Logit Scale: 25.767 Contrastive_loss: 0.094084 (0.22407) Loss: 0.094084 (0.22407) 2025-03-19,08:20:48 | INFO | Train Epoch: 9 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 149.344/s, 149.344/s/gpu LR: 0.000157 Logit Scale: 25.739 Contrastive_loss: 0.42103 (0.22578) Loss: 0.42103 (0.22578) 2025-03-19,08:21:10 | INFO | Train Epoch: 9 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.488/s, 148.488/s/gpu LR: 0.000157 Logit Scale: 25.766 Contrastive_loss: 0.25453 (0.22603) Loss: 0.25453 (0.22603) 2025-03-19,08:21:31 | INFO | Train Epoch: 9 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.766/s, 148.766/s/gpu LR: 0.000157 Logit Scale: 25.795 Contrastive_loss: 0.072586 (0.22472) Loss: 0.072586 (0.22472) 2025-03-19,08:21:53 | INFO | Train Epoch: 9 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 147.090/s, 147.090/s/gpu LR: 0.000157 Logit Scale: 25.776 Contrastive_loss: 0.12381 (0.22386) Loss: 0.12381 (0.22386) 2025-03-19,08:22:15 | INFO | Train Epoch: 9 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 141.382/s, 141.382/s/gpu LR: 0.000157 Logit Scale: 25.775 Contrastive_loss: 0.15726 (0.22330) Loss: 0.15726 (0.22330) 2025-03-19,08:22:36 | INFO | Train Epoch: 9 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 149.916/s, 149.916/s/gpu LR: 0.000157 Logit Scale: 25.779 Contrastive_loss: 0.24848 (0.22351) Loss: 0.24848 (0.22351) 2025-03-19,08:22:58 | INFO | Train Epoch: 9 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 149.463/s, 149.463/s/gpu LR: 0.000157 Logit Scale: 25.779 Contrastive_loss: 0.15866 (0.22298) Loss: 0.15866 (0.22298) 2025-03-19,08:23:19 | INFO | Train Epoch: 9 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 147.987/s, 147.987/s/gpu LR: 0.000157 Logit Scale: 25.764 Contrastive_loss: 0.14529 (0.22234) Loss: 0.14529 (0.22234) 2025-03-19,08:23:41 | INFO | Train Epoch: 9 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 148.302/s, 148.302/s/gpu LR: 0.000157 Logit Scale: 25.743 Contrastive_loss: 0.22564 (0.22237) Loss: 0.22564 (0.22237) 2025-03-19,08:24:02 | INFO | Train Epoch: 9 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 147.781/s, 147.781/s/gpu LR: 0.000157 Logit Scale: 25.753 Contrastive_loss: 0.099369 (0.22138) Loss: 0.099369 (0.22138) 2025-03-19,08:24:24 | INFO | Train Epoch: 9 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 149.144/s, 149.144/s/gpu LR: 0.000157 Logit Scale: 25.739 Contrastive_loss: 0.42912 (0.22304) Loss: 0.42912 (0.22304) 2025-03-19,08:24:46 | INFO | Train Epoch: 9 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.107/s, 148.107/s/gpu LR: 0.000157 Logit Scale: 25.759 Contrastive_loss: 0.28651 (0.22354) Loss: 0.28651 (0.22354) 2025-03-19,08:25:08 | INFO | Train Epoch: 9 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.221, 142.141/s, 142.141/s/gpu LR: 0.000157 Logit Scale: 25.720 Contrastive_loss: 0.15371 (0.22299) Loss: 0.15371 (0.22299) 2025-03-19,08:25:30 | INFO | Train Epoch: 9 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 145.865/s, 145.865/s/gpu LR: 0.000157 Logit Scale: 25.740 Contrastive_loss: 0.045337 (0.22160) Loss: 0.045337 (0.22160) 2025-03-19,08:25:51 | INFO | Train Epoch: 9 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 146.135/s, 146.135/s/gpu LR: 0.000157 Logit Scale: 25.749 Contrastive_loss: 0.029281 (0.22011) Loss: 0.029281 (0.22011) 2025-03-19,08:26:13 | INFO | Train Epoch: 9 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.218, 143.297/s, 143.297/s/gpu LR: 0.000157 Logit Scale: 25.721 Contrastive_loss: 0.16805 (0.21971) Loss: 0.16805 (0.21971) 2025-03-19,08:26:35 | INFO | Train Epoch: 9 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.220, 147.274/s, 147.274/s/gpu LR: 0.000157 Logit Scale: 25.763 Contrastive_loss: 0.23150 (0.21980) Loss: 0.23150 (0.21980) 2025-03-19,08:26:57 | INFO | Train Epoch: 9 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.220, 146.962/s, 146.962/s/gpu LR: 0.000157 Logit Scale: 25.764 Contrastive_loss: 0.18020 (0.21950) Loss: 0.18020 (0.21950) 2025-03-19,08:27:19 | INFO | Train Epoch: 9 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.219, 147.750/s, 147.750/s/gpu LR: 0.000157 Logit Scale: 25.748 Contrastive_loss: 0.089113 (0.21852) Loss: 0.089113 (0.21852) 2025-03-19,08:27:41 | INFO | Train Epoch: 9 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 143.198/s, 143.198/s/gpu LR: 0.000156 Logit Scale: 25.764 Contrastive_loss: 0.27594 (0.21895) Loss: 0.27594 (0.21895) 2025-03-19,08:28:03 | INFO | Train Epoch: 9 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.221, 146.764/s, 146.764/s/gpu LR: 0.000156 Logit Scale: 25.752 Contrastive_loss: 0.12437 (0.21825) Loss: 0.12437 (0.21825) 2025-03-19,08:28:25 | INFO | Train Epoch: 9 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.220, 145.444/s, 145.444/s/gpu LR: 0.000156 Logit Scale: 25.738 Contrastive_loss: 0.15875 (0.21781) Loss: 0.15875 (0.21781) 2025-03-19,08:28:47 | INFO | Train Epoch: 9 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.219, 147.219/s, 147.219/s/gpu LR: 0.000156 Logit Scale: 25.717 Contrastive_loss: 0.20754 (0.21774) Loss: 0.20754 (0.21774) 2025-03-19,08:29:09 | INFO | Train Epoch: 9 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.219, 147.698/s, 147.698/s/gpu LR: 0.000156 Logit Scale: 25.731 Contrastive_loss: 0.11427 (0.21699) Loss: 0.11427 (0.21699) 2025-03-19,08:29:30 | INFO | Train Epoch: 9 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 151.202/s, 151.202/s/gpu LR: 0.000156 Logit Scale: 25.741 Contrastive_loss: 0.13718 (0.21641) Loss: 0.13718 (0.21641) 2025-03-19,08:29:52 | INFO | Train Epoch: 9 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 144.217/s, 144.217/s/gpu LR: 0.000156 Logit Scale: 25.728 Contrastive_loss: 0.077651 (0.21542) Loss: 0.077651 (0.21542) 2025-03-19,08:30:13 | INFO | Train Epoch: 9 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 148.728/s, 148.728/s/gpu LR: 0.000156 Logit Scale: 25.730 Contrastive_loss: 0.12976 (0.21481) Loss: 0.12976 (0.21481) 2025-03-19,08:30:34 | INFO | Train Epoch: 9 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 148.980/s, 148.980/s/gpu LR: 0.000156 Logit Scale: 25.745 Contrastive_loss: 0.18779 (0.21462) Loss: 0.18779 (0.21462) 2025-03-19,08:30:56 | INFO | Train Epoch: 9 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 148.027/s, 148.027/s/gpu LR: 0.000156 Logit Scale: 25.740 Contrastive_loss: 0.22483 (0.21470) Loss: 0.22483 (0.21470) 2025-03-19,08:31:18 | INFO | Train Epoch: 9 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 146.695/s, 146.695/s/gpu LR: 0.000156 Logit Scale: 25.738 Contrastive_loss: 0.21961 (0.21473) Loss: 0.21961 (0.21473) 2025-03-19,08:31:39 | INFO | Train Epoch: 9 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 150.163/s, 150.163/s/gpu LR: 0.000156 Logit Scale: 25.747 Contrastive_loss: 0.26297 (0.21506) Loss: 0.26297 (0.21506) 2025-03-19,08:32:01 | INFO | Train Epoch: 9 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 150.460/s, 150.460/s/gpu LR: 0.000156 Logit Scale: 25.722 Contrastive_loss: 0.10135 (0.21428) Loss: 0.10135 (0.21428) 2025-03-19,08:32:22 | INFO | Train Epoch: 9 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.218, 146.593/s, 146.593/s/gpu LR: 0.000156 Logit Scale: 25.720 Contrastive_loss: 0.063497 (0.21326) Loss: 0.063497 (0.21326) 2025-03-19,08:32:44 | INFO | Train Epoch: 9 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 149.546/s, 149.546/s/gpu LR: 0.000156 Logit Scale: 25.742 Contrastive_loss: 0.32523 (0.21401) Loss: 0.32523 (0.21401) 2025-03-19,08:33:06 | INFO | Train Epoch: 9 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 149.849/s, 149.849/s/gpu LR: 0.000156 Logit Scale: 25.799 Contrastive_loss: 0.10641 (0.21329) Loss: 0.10641 (0.21329) 2025-03-19,08:33:27 | INFO | Train Epoch: 9 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.212, 151.455/s, 151.455/s/gpu LR: 0.000156 Logit Scale: 25.755 Contrastive_loss: 0.018414 (0.21199) Loss: 0.018414 (0.21199) 2025-03-19,08:33:48 | INFO | Train Epoch: 9 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.212, 152.662/s, 152.662/s/gpu LR: 0.000156 Logit Scale: 25.795 Contrastive_loss: 0.13990 (0.21152) Loss: 0.13990 (0.21152) 2025-03-19,08:34:09 | INFO | Train Epoch: 9 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.098/s, 149.098/s/gpu LR: 0.000156 Logit Scale: 25.770 Contrastive_loss: 0.40464 (0.21279) Loss: 0.40464 (0.21279) 2025-03-19,08:34:31 | INFO | Train Epoch: 9 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.218, 148.272/s, 148.272/s/gpu LR: 0.000156 Logit Scale: 25.748 Contrastive_loss: 0.16499 (0.21247) Loss: 0.16499 (0.21247) 2025-03-19,08:34:53 | INFO | Train Epoch: 9 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.220, 146.905/s, 146.905/s/gpu LR: 0.000156 Logit Scale: 25.783 Contrastive_loss: 0.26990 (0.21285) Loss: 0.26990 (0.21285) 2025-03-19,08:35:15 | INFO | Train Epoch: 9 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.218, 147.778/s, 147.778/s/gpu LR: 0.000156 Logit Scale: 25.794 Contrastive_loss: 0.14538 (0.21241) Loss: 0.14538 (0.21241) 2025-03-19,08:35:36 | INFO | Train Epoch: 9 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.020/s, 149.020/s/gpu LR: 0.000156 Logit Scale: 25.785 Contrastive_loss: 0.19202 (0.21228) Loss: 0.19202 (0.21228) 2025-03-19,08:35:58 | INFO | Train Epoch: 9 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.213, 150.385/s, 150.385/s/gpu LR: 0.000156 Logit Scale: 25.782 Contrastive_loss: 0.16935 (0.21201) Loss: 0.16935 (0.21201) 2025-03-19,08:36:19 | INFO | Train Epoch: 9 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.212, 151.230/s, 151.230/s/gpu LR: 0.000156 Logit Scale: 25.811 Contrastive_loss: 0.16089 (0.21168) Loss: 0.16089 (0.21168) 2025-03-19,08:36:41 | INFO | Train Epoch: 9 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 147.419/s, 147.419/s/gpu LR: 0.000156 Logit Scale: 25.799 Contrastive_loss: 0.28426 (0.21214) Loss: 0.28426 (0.21214) 2025-03-19,08:37:03 | INFO | Train Epoch: 9 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 146.536/s, 146.536/s/gpu LR: 0.000156 Logit Scale: 25.825 Contrastive_loss: 0.17415 (0.21190) Loss: 0.17415 (0.21190) 2025-03-19,08:37:25 | INFO | Train Epoch: 9 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.221, 143.714/s, 143.714/s/gpu LR: 0.000156 Logit Scale: 25.858 Contrastive_loss: 0.43758 (0.21330) Loss: 0.43758 (0.21330) 2025-03-19,08:37:47 | INFO | Train Epoch: 9 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.221, 146.555/s, 146.555/s/gpu LR: 0.000155 Logit Scale: 25.818 Contrastive_loss: 0.25064 (0.21353) Loss: 0.25064 (0.21353) 2025-03-19,08:38:09 | INFO | Train Epoch: 9 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 146.570/s, 146.570/s/gpu LR: 0.000155 Logit Scale: 25.824 Contrastive_loss: 0.56594 (0.21570) Loss: 0.56594 (0.21570) 2025-03-19,08:38:30 | INFO | Train Epoch: 9 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 144.136/s, 144.136/s/gpu LR: 0.000155 Logit Scale: 25.830 Contrastive_loss: 0.12690 (0.21516) Loss: 0.12690 (0.21516) 2025-03-19,08:38:52 | INFO | Train Epoch: 9 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 145.953/s, 145.953/s/gpu LR: 0.000155 Logit Scale: 25.815 Contrastive_loss: 0.10962 (0.21452) Loss: 0.10962 (0.21452) 2025-03-19,08:39:14 | INFO | Train Epoch: 9 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 145.643/s, 145.643/s/gpu LR: 0.000155 Logit Scale: 25.798 Contrastive_loss: 0.13232 (0.21402) Loss: 0.13232 (0.21402) 2025-03-19,08:39:36 | INFO | Train Epoch: 9 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 147.620/s, 147.620/s/gpu LR: 0.000155 Logit Scale: 25.796 Contrastive_loss: 0.077846 (0.21321) Loss: 0.077846 (0.21321) 2025-03-19,08:39:58 | INFO | Train Epoch: 9 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 144.776/s, 144.776/s/gpu LR: 0.000155 Logit Scale: 25.815 Contrastive_loss: 0.27513 (0.21357) Loss: 0.27513 (0.21357) 2025-03-19,08:40:20 | INFO | Train Epoch: 9 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 147.383/s, 147.383/s/gpu LR: 0.000155 Logit Scale: 25.802 Contrastive_loss: 0.083433 (0.21280) Loss: 0.083433 (0.21280) 2025-03-19,08:40:42 | INFO | Train Epoch: 9 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 144.833/s, 144.833/s/gpu LR: 0.000155 Logit Scale: 25.794 Contrastive_loss: 0.42960 (0.21408) Loss: 0.42960 (0.21408) 2025-03-19,08:41:03 | INFO | Train Epoch: 9 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 146.541/s, 146.541/s/gpu LR: 0.000155 Logit Scale: 25.816 Contrastive_loss: 0.24410 (0.21425) Loss: 0.24410 (0.21425) 2025-03-19,08:41:25 | INFO | Train Epoch: 9 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.218, 147.925/s, 147.925/s/gpu LR: 0.000155 Logit Scale: 25.843 Contrastive_loss: 0.19607 (0.21415) Loss: 0.19607 (0.21415) 2025-03-19,08:41:47 | INFO | Train Epoch: 9 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 147.949/s, 147.949/s/gpu LR: 0.000155 Logit Scale: 25.760 Contrastive_loss: 0.19390 (0.21403) Loss: 0.19390 (0.21403) 2025-03-19,08:42:09 | INFO | Train Epoch: 9 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 147.045/s, 147.045/s/gpu LR: 0.000155 Logit Scale: 25.779 Contrastive_loss: 0.11787 (0.21348) Loss: 0.11787 (0.21348) 2025-03-19,08:42:31 | INFO | Train Epoch: 9 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.218, 147.369/s, 147.369/s/gpu LR: 0.000155 Logit Scale: 25.832 Contrastive_loss: 0.37727 (0.21442) Loss: 0.37727 (0.21442) 2025-03-19,08:42:52 | INFO | Train Epoch: 9 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 148.601/s, 148.601/s/gpu LR: 0.000155 Logit Scale: 25.797 Contrastive_loss: 0.21300 (0.21441) Loss: 0.21300 (0.21441) 2025-03-19,08:43:14 | INFO | Train Epoch: 9 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.217, 148.267/s, 148.267/s/gpu LR: 0.000155 Logit Scale: 25.752 Contrastive_loss: 0.24303 (0.21457) Loss: 0.24303 (0.21457) 2025-03-19,08:43:36 | INFO | Train Epoch: 9 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.217, 147.436/s, 147.436/s/gpu LR: 0.000155 Logit Scale: 25.775 Contrastive_loss: 0.070424 (0.21376) Loss: 0.070424 (0.21376) 2025-03-19,08:43:57 | INFO | Train Epoch: 9 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 148.192/s, 148.192/s/gpu LR: 0.000155 Logit Scale: 25.796 Contrastive_loss: 0.099111 (0.21312) Loss: 0.099111 (0.21312) 2025-03-19,08:44:19 | INFO | Train Epoch: 9 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.217, 149.767/s, 149.767/s/gpu LR: 0.000155 Logit Scale: 25.761 Contrastive_loss: 0.15391 (0.21279) Loss: 0.15391 (0.21279) 2025-03-19,08:44:40 | INFO | Train Epoch: 9 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.620/s, 149.620/s/gpu LR: 0.000155 Logit Scale: 25.742 Contrastive_loss: 0.21624 (0.21281) Loss: 0.21624 (0.21281) 2025-03-19,08:45:02 | INFO | Train Epoch: 9 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 149.433/s, 149.433/s/gpu LR: 0.000155 Logit Scale: 25.738 Contrastive_loss: 0.24265 (0.21297) Loss: 0.24265 (0.21297) 2025-03-19,08:45:23 | INFO | Train Epoch: 9 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.624/s, 148.624/s/gpu LR: 0.000155 Logit Scale: 25.733 Contrastive_loss: 0.073023 (0.21221) Loss: 0.073023 (0.21221) 2025-03-19,08:45:45 | INFO | Train Epoch: 9 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 145.586/s, 145.586/s/gpu LR: 0.000155 Logit Scale: 25.762 Contrastive_loss: 0.18904 (0.21208) Loss: 0.18904 (0.21208) 2025-03-19,08:46:06 | INFO | Train Epoch: 9 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.020/s, 149.020/s/gpu LR: 0.000155 Logit Scale: 25.803 Contrastive_loss: 0.14176 (0.21170) Loss: 0.14176 (0.21170) 2025-03-19,08:46:28 | INFO | Train Epoch: 9 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.270/s, 149.270/s/gpu LR: 0.000155 Logit Scale: 25.804 Contrastive_loss: 0.35573 (0.21248) Loss: 0.35573 (0.21248) 2025-03-19,08:46:49 | INFO | Train Epoch: 9 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.130/s, 149.130/s/gpu LR: 0.000155 Logit Scale: 25.757 Contrastive_loss: 0.16422 (0.21222) Loss: 0.16422 (0.21222) 2025-03-19,08:47:11 | INFO | Train Epoch: 9 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 146.470/s, 146.470/s/gpu LR: 0.000155 Logit Scale: 25.763 Contrastive_loss: 0.24548 (0.21239) Loss: 0.24548 (0.21239) 2025-03-19,08:47:32 | INFO | Train Epoch: 9 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.214/s, 149.214/s/gpu LR: 0.000154 Logit Scale: 25.789 Contrastive_loss: 0.38687 (0.21332) Loss: 0.38687 (0.21332) 2025-03-19,08:47:54 | INFO | Train Epoch: 9 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 152.389/s, 152.389/s/gpu LR: 0.000154 Logit Scale: 25.765 Contrastive_loss: 0.31371 (0.21385) Loss: 0.31371 (0.21385) 2025-03-19,08:48:16 | INFO | Train Epoch: 9 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 150.374/s, 150.374/s/gpu LR: 0.000154 Logit Scale: 25.808 Contrastive_loss: 0.17826 (0.21366) Loss: 0.17826 (0.21366) 2025-03-19,08:48:37 | INFO | Train Epoch: 9 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 149.981/s, 149.981/s/gpu LR: 0.000154 Logit Scale: 25.816 Contrastive_loss: 0.23257 (0.21376) Loss: 0.23257 (0.21376) 2025-03-19,08:48:59 | INFO | Train Epoch: 9 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 146.299/s, 146.299/s/gpu LR: 0.000154 Logit Scale: 25.803 Contrastive_loss: 0.25708 (0.21398) Loss: 0.25708 (0.21398) 2025-03-19,08:49:20 | INFO | Train Epoch: 9 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.217, 148.627/s, 148.627/s/gpu LR: 0.000154 Logit Scale: 25.806 Contrastive_loss: 0.34861 (0.21468) Loss: 0.34861 (0.21468) 2025-03-19,08:49:42 | INFO | Train Epoch: 9 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.217, 147.286/s, 147.286/s/gpu LR: 0.000154 Logit Scale: 25.793 Contrastive_loss: 0.15980 (0.21440) Loss: 0.15980 (0.21440) 2025-03-19,08:50:04 | INFO | Train Epoch: 9 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.217, 149.142/s, 149.142/s/gpu LR: 0.000154 Logit Scale: 25.827 Contrastive_loss: 0.041472 (0.21351) Loss: 0.041472 (0.21351) 2025-03-19,08:50:26 | INFO | Train Epoch: 9 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.219, 149.652/s, 149.652/s/gpu LR: 0.000154 Logit Scale: 25.831 Contrastive_loss: 0.24359 (0.21367) Loss: 0.24359 (0.21367) 2025-03-19,08:50:47 | INFO | Train Epoch: 9 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 147.779/s, 147.779/s/gpu LR: 0.000154 Logit Scale: 25.816 Contrastive_loss: 0.21175 (0.21366) Loss: 0.21175 (0.21366) 2025-03-19,08:51:09 | INFO | Train Epoch: 9 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 153.970/s, 153.970/s/gpu LR: 0.000154 Logit Scale: 25.796 Contrastive_loss: 0.25836 (0.21388) Loss: 0.25836 (0.21388) 2025-03-19,08:51:30 | INFO | Train Epoch: 9 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 148.345/s, 148.345/s/gpu LR: 0.000154 Logit Scale: 25.812 Contrastive_loss: 0.22529 (0.21394) Loss: 0.22529 (0.21394) 2025-03-19,08:51:52 | INFO | Train Epoch: 9 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 148.858/s, 148.858/s/gpu LR: 0.000154 Logit Scale: 25.822 Contrastive_loss: 0.22715 (0.21400) Loss: 0.22715 (0.21400) 2025-03-19,08:52:14 | INFO | Train Epoch: 9 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 144.133/s, 144.133/s/gpu LR: 0.000154 Logit Scale: 25.821 Contrastive_loss: 0.19116 (0.21389) Loss: 0.19116 (0.21389) 2025-03-19,08:52:35 | INFO | Train Epoch: 9 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 147.937/s, 147.937/s/gpu LR: 0.000154 Logit Scale: 25.774 Contrastive_loss: 0.13319 (0.21349) Loss: 0.13319 (0.21349) 2025-03-19,08:52:57 | INFO | Train Epoch: 9 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 142.880/s, 142.880/s/gpu LR: 0.000154 Logit Scale: 25.777 Contrastive_loss: 0.15398 (0.21320) Loss: 0.15398 (0.21320) 2025-03-19,08:53:19 | INFO | Train Epoch: 9 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 151.053/s, 151.053/s/gpu LR: 0.000154 Logit Scale: 25.774 Contrastive_loss: 0.40924 (0.21416) Loss: 0.40924 (0.21416) 2025-03-19,08:53:40 | INFO | Train Epoch: 9 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.889/s, 148.889/s/gpu LR: 0.000154 Logit Scale: 25.781 Contrastive_loss: 0.20364 (0.21411) Loss: 0.20364 (0.21411) 2025-03-19,08:54:02 | INFO | Train Epoch: 9 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.706/s, 149.706/s/gpu LR: 0.000154 Logit Scale: 25.792 Contrastive_loss: 0.59143 (0.21593) Loss: 0.59143 (0.21593) 2025-03-19,08:54:23 | INFO | Train Epoch: 9 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.213, 149.501/s, 149.501/s/gpu LR: 0.000154 Logit Scale: 25.801 Contrastive_loss: 0.41877 (0.21690) Loss: 0.41877 (0.21690) 2025-03-19,08:54:45 | INFO | Train Epoch: 9 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 149.791/s, 149.791/s/gpu LR: 0.000154 Logit Scale: 25.787 Contrastive_loss: 0.33381 (0.21746) Loss: 0.33381 (0.21746) 2025-03-19,08:55:06 | INFO | Train Epoch: 9 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 152.211/s, 152.211/s/gpu LR: 0.000154 Logit Scale: 25.834 Contrastive_loss: 0.49671 (0.21879) Loss: 0.49671 (0.21879) 2025-03-19,08:55:28 | INFO | Train Epoch: 9 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 149.095/s, 149.095/s/gpu LR: 0.000154 Logit Scale: 25.827 Contrastive_loss: 0.31217 (0.21924) Loss: 0.31217 (0.21924) 2025-03-19,08:55:49 | INFO | Train Epoch: 9 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 148.071/s, 148.071/s/gpu LR: 0.000154 Logit Scale: 25.823 Contrastive_loss: 0.12505 (0.21879) Loss: 0.12505 (0.21879) 2025-03-19,08:56:11 | INFO | Train Epoch: 9 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.560/s, 149.560/s/gpu LR: 0.000154 Logit Scale: 25.797 Contrastive_loss: 0.18826 (0.21865) Loss: 0.18826 (0.21865) 2025-03-19,08:56:32 | INFO | Train Epoch: 9 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.460/s, 149.460/s/gpu LR: 0.000154 Logit Scale: 25.792 Contrastive_loss: 0.18911 (0.21851) Loss: 0.18911 (0.21851) 2025-03-19,08:56:53 | INFO | Train Epoch: 9 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.213, 151.358/s, 151.358/s/gpu LR: 0.000153 Logit Scale: 25.804 Contrastive_loss: 0.13456 (0.21812) Loss: 0.13456 (0.21812) 2025-03-19,08:57:15 | INFO | Train Epoch: 9 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 148.470/s, 148.470/s/gpu LR: 0.000153 Logit Scale: 25.790 Contrastive_loss: 0.34967 (0.21873) Loss: 0.34967 (0.21873) 2025-03-19,08:57:37 | INFO | Train Epoch: 9 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 149.408/s, 149.408/s/gpu LR: 0.000153 Logit Scale: 25.780 Contrastive_loss: 0.15757 (0.21845) Loss: 0.15757 (0.21845) 2025-03-19,08:57:58 | INFO | Train Epoch: 9 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 146.727/s, 146.727/s/gpu LR: 0.000153 Logit Scale: 25.793 Contrastive_loss: 0.22990 (0.21850) Loss: 0.22990 (0.21850) 2025-03-19,08:58:20 | INFO | Train Epoch: 9 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.218, 144.566/s, 144.566/s/gpu LR: 0.000153 Logit Scale: 25.804 Contrastive_loss: 0.20707 (0.21845) Loss: 0.20707 (0.21845) 2025-03-19,08:58:42 | INFO | Train Epoch: 9 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.219, 143.140/s, 143.140/s/gpu LR: 0.000153 Logit Scale: 25.755 Contrastive_loss: 0.067094 (0.21776) Loss: 0.067094 (0.21776) 2025-03-19,08:59:04 | INFO | Train Epoch: 9 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.220, 147.260/s, 147.260/s/gpu LR: 0.000153 Logit Scale: 25.801 Contrastive_loss: 0.24923 (0.21790) Loss: 0.24923 (0.21790) 2025-03-19,08:59:26 | INFO | Train Epoch: 9 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 148.305/s, 148.305/s/gpu LR: 0.000153 Logit Scale: 25.819 Contrastive_loss: 0.33096 (0.21841) Loss: 0.33096 (0.21841) 2025-03-19,08:59:47 | INFO | Train Epoch: 9 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.218, 148.182/s, 148.182/s/gpu LR: 0.000153 Logit Scale: 25.823 Contrastive_loss: 0.11727 (0.21796) Loss: 0.11727 (0.21796) 2025-03-19,09:00:09 | INFO | Train Epoch: 9 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.217, 149.903/s, 149.903/s/gpu LR: 0.000153 Logit Scale: 25.810 Contrastive_loss: 0.43141 (0.21891) Loss: 0.43141 (0.21891) 2025-03-19,09:00:31 | INFO | Train Epoch: 9 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.380/s, 148.380/s/gpu LR: 0.000153 Logit Scale: 25.842 Contrastive_loss: 0.14915 (0.21860) Loss: 0.14915 (0.21860) 2025-03-19,09:00:52 | INFO | Train Epoch: 9 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.019/s, 149.019/s/gpu LR: 0.000153 Logit Scale: 25.842 Contrastive_loss: 0.36529 (0.21925) Loss: 0.36529 (0.21925) 2025-03-19,09:01:14 | INFO | Train Epoch: 9 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 147.552/s, 147.552/s/gpu LR: 0.000153 Logit Scale: 25.845 Contrastive_loss: 0.12066 (0.21882) Loss: 0.12066 (0.21882) 2025-03-19,09:01:35 | INFO | Train Epoch: 9 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.217, 146.342/s, 146.342/s/gpu LR: 0.000153 Logit Scale: 25.835 Contrastive_loss: 0.23763 (0.21890) Loss: 0.23763 (0.21890) 2025-03-19,09:01:57 | INFO | Train Epoch: 9 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.217, 147.288/s, 147.288/s/gpu LR: 0.000153 Logit Scale: 25.787 Contrastive_loss: 0.32002 (0.21934) Loss: 0.32002 (0.21934) 2025-03-19,09:02:19 | INFO | Train Epoch: 9 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.217, 149.135/s, 149.135/s/gpu LR: 0.000153 Logit Scale: 25.775 Contrastive_loss: 0.27519 (0.21958) Loss: 0.27519 (0.21958) 2025-03-19,09:02:40 | INFO | Train Epoch: 9 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 147.005/s, 147.005/s/gpu LR: 0.000153 Logit Scale: 25.806 Contrastive_loss: 0.13667 (0.21922) Loss: 0.13667 (0.21922) 2025-03-19,09:03:02 | INFO | Train Epoch: 9 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.217, 148.930/s, 148.930/s/gpu LR: 0.000153 Logit Scale: 25.806 Contrastive_loss: 0.15590 (0.21895) Loss: 0.15590 (0.21895) 2025-03-19,09:03:24 | INFO | Train Epoch: 9 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 145.090/s, 145.090/s/gpu LR: 0.000153 Logit Scale: 25.813 Contrastive_loss: 0.42152 (0.21982) Loss: 0.42152 (0.21982) 2025-03-19,09:03:46 | INFO | Train Epoch: 9 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.219, 148.526/s, 148.526/s/gpu LR: 0.000153 Logit Scale: 25.820 Contrastive_loss: 0.10646 (0.21934) Loss: 0.10646 (0.21934) 2025-03-19,09:04:07 | INFO | Train Epoch: 9 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.219, 145.064/s, 145.064/s/gpu LR: 0.000153 Logit Scale: 25.789 Contrastive_loss: 0.38474 (0.22004) Loss: 0.38474 (0.22004) 2025-03-19,09:04:29 | INFO | Train Epoch: 9 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 148.781/s, 148.781/s/gpu LR: 0.000153 Logit Scale: 25.766 Contrastive_loss: 0.16809 (0.21982) Loss: 0.16809 (0.21982) 2025-03-19,09:04:51 | INFO | Train Epoch: 9 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 147.418/s, 147.418/s/gpu LR: 0.000153 Logit Scale: 25.781 Contrastive_loss: 0.46702 (0.22086) Loss: 0.46702 (0.22086) 2025-03-19,09:05:12 | INFO | Train Epoch: 9 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.972/s, 149.972/s/gpu LR: 0.000153 Logit Scale: 25.831 Contrastive_loss: 0.19227 (0.22074) Loss: 0.19227 (0.22074) 2025-03-19,09:05:33 | INFO | Train Epoch: 9 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.212, 151.871/s, 151.871/s/gpu LR: 0.000153 Logit Scale: 25.863 Contrastive_loss: 0.19875 (0.22065) Loss: 0.19875 (0.22065) 2025-03-19,09:05:55 | INFO | Train Epoch: 9 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.215, 148.637/s, 148.637/s/gpu LR: 0.000153 Logit Scale: 25.828 Contrastive_loss: 0.61227 (0.22228) Loss: 0.61227 (0.22228) 2025-03-19,09:06:03 | INFO | Train Epoch: 9 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.221, 149.659/s, 149.659/s/gpu LR: 0.000153 Logit Scale: 25.824 Contrastive_loss: 0.26325 (0.22245) Loss: 0.26325 (0.22245) 2025-03-19,09:06:03 | INFO | Eval Epoch: 10 [32 / 7443] Clip Loss: 3.036880 2025-03-19,09:06:09 | INFO | Eval Epoch: 10 [3232 / 7443] Clip Loss: 0.891526 2025-03-19,09:06:15 | INFO | Eval Epoch: 10 [6432 / 7443] Clip Loss: 0.678851 2025-03-19,09:06:17 | INFO | Eval Epoch: 10 image_to_text_mean_rank: 92.2495 image_to_text_median_rank: 7.0000 image_to_text_R@1: 0.1275 image_to_text_R@5: 0.4195 image_to_text_R@10: 0.5956 text_to_image_mean_rank: 62.0415 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1256 text_to_image_R@5: 0.4266 text_to_image_R@10: 0.5949 clip_val_loss: 0.6403 epoch: 10.0000 num_samples: 7443.0000 2025-03-19,09:06:50 | INFO | Start epoch 10 2025-03-19,09:06:51 | INFO | Train Epoch: 10 [ 32/766009 (0%)] Data (t): 0.165 Batch (t): 0.371, 86.2221/s, 86.2221/s/gpu LR: 0.000153 Logit Scale: 25.824 Contrastive_loss: 0.32701 (0.32701) Loss: 0.32701 (0.32701) 2025-03-19,09:07:12 | INFO | Train Epoch: 10 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.215, 148.792/s, 148.792/s/gpu LR: 0.000153 Logit Scale: 25.884 Contrastive_loss: 0.11249 (0.21975) Loss: 0.11249 (0.21975) 2025-03-19,09:07:34 | INFO | Train Epoch: 10 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 149.710/s, 149.710/s/gpu LR: 0.000152 Logit Scale: 25.915 Contrastive_loss: 0.28850 (0.24267) Loss: 0.28850 (0.24267) 2025-03-19,09:07:55 | INFO | Train Epoch: 10 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.120/s, 149.120/s/gpu LR: 0.000152 Logit Scale: 25.907 Contrastive_loss: 0.15923 (0.22181) Loss: 0.15923 (0.22181) 2025-03-19,09:08:16 | INFO | Train Epoch: 10 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 150.608/s, 150.608/s/gpu LR: 0.000152 Logit Scale: 25.924 Contrastive_loss: 0.50771 (0.27899) Loss: 0.50771 (0.27899) 2025-03-19,09:08:38 | INFO | Train Epoch: 10 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 146.877/s, 146.877/s/gpu LR: 0.000152 Logit Scale: 25.933 Contrastive_loss: 0.17318 (0.26136) Loss: 0.17318 (0.26136) 2025-03-19,09:08:59 | INFO | Train Epoch: 10 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 148.850/s, 148.850/s/gpu LR: 0.000152 Logit Scale: 25.949 Contrastive_loss: 0.14318 (0.24447) Loss: 0.14318 (0.24447) 2025-03-19,09:09:21 | INFO | Train Epoch: 10 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.218, 147.448/s, 147.448/s/gpu LR: 0.000152 Logit Scale: 25.977 Contrastive_loss: 0.26460 (0.24699) Loss: 0.26460 (0.24699) 2025-03-19,09:09:43 | INFO | Train Epoch: 10 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 147.885/s, 147.885/s/gpu LR: 0.000152 Logit Scale: 26.004 Contrastive_loss: 0.069607 (0.22728) Loss: 0.069607 (0.22728) 2025-03-19,09:10:04 | INFO | Train Epoch: 10 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.363/s, 149.363/s/gpu LR: 0.000152 Logit Scale: 25.974 Contrastive_loss: 0.11552 (0.21610) Loss: 0.11552 (0.21610) 2025-03-19,09:10:26 | INFO | Train Epoch: 10 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 151.899/s, 151.899/s/gpu LR: 0.000152 Logit Scale: 26.000 Contrastive_loss: 0.094306 (0.20503) Loss: 0.094306 (0.20503) 2025-03-19,09:10:47 | INFO | Train Epoch: 10 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 150.003/s, 150.003/s/gpu LR: 0.000152 Logit Scale: 26.024 Contrastive_loss: 0.27598 (0.21094) Loss: 0.27598 (0.21094) 2025-03-19,09:11:09 | INFO | Train Epoch: 10 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.980/s, 147.980/s/gpu LR: 0.000152 Logit Scale: 26.076 Contrastive_loss: 0.081783 (0.20101) Loss: 0.081783 (0.20101) 2025-03-19,09:11:30 | INFO | Train Epoch: 10 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.875/s, 147.875/s/gpu LR: 0.000152 Logit Scale: 26.040 Contrastive_loss: 0.32933 (0.21017) Loss: 0.32933 (0.21017) 2025-03-19,09:11:52 | INFO | Train Epoch: 10 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.217, 147.707/s, 147.707/s/gpu LR: 0.000152 Logit Scale: 26.016 Contrastive_loss: 0.16486 (0.20715) Loss: 0.16486 (0.20715) 2025-03-19,09:12:14 | INFO | Train Epoch: 10 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 148.558/s, 148.558/s/gpu LR: 0.000152 Logit Scale: 26.053 Contrastive_loss: 0.15948 (0.20417) Loss: 0.15948 (0.20417) 2025-03-19,09:12:35 | INFO | Train Epoch: 10 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 149.045/s, 149.045/s/gpu LR: 0.000152 Logit Scale: 26.024 Contrastive_loss: 0.19929 (0.20389) Loss: 0.19929 (0.20389) 2025-03-19,09:12:57 | INFO | Train Epoch: 10 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 148.885/s, 148.885/s/gpu LR: 0.000152 Logit Scale: 25.997 Contrastive_loss: 0.10997 (0.19867) Loss: 0.10997 (0.19867) 2025-03-19,09:13:18 | INFO | Train Epoch: 10 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 147.505/s, 147.505/s/gpu LR: 0.000152 Logit Scale: 26.040 Contrastive_loss: 0.12627 (0.19486) Loss: 0.12627 (0.19486) 2025-03-19,09:13:40 | INFO | Train Epoch: 10 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.463/s, 149.463/s/gpu LR: 0.000152 Logit Scale: 26.004 Contrastive_loss: 0.099170 (0.19007) Loss: 0.099170 (0.19007) 2025-03-19,09:14:02 | INFO | Train Epoch: 10 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 142.342/s, 142.342/s/gpu LR: 0.000152 Logit Scale: 26.000 Contrastive_loss: 0.069354 (0.18433) Loss: 0.069354 (0.18433) 2025-03-19,09:14:23 | INFO | Train Epoch: 10 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 134.257/s, 134.257/s/gpu LR: 0.000152 Logit Scale: 26.003 Contrastive_loss: 0.27309 (0.18836) Loss: 0.27309 (0.18836) 2025-03-19,09:14:45 | INFO | Train Epoch: 10 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 148.458/s, 148.458/s/gpu LR: 0.000152 Logit Scale: 26.052 Contrastive_loss: 0.33300 (0.19465) Loss: 0.33300 (0.19465) 2025-03-19,09:15:06 | INFO | Train Epoch: 10 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.840/s, 149.840/s/gpu LR: 0.000152 Logit Scale: 26.039 Contrastive_loss: 0.36320 (0.20167) Loss: 0.36320 (0.20167) 2025-03-19,09:15:28 | INFO | Train Epoch: 10 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 150.189/s, 150.189/s/gpu LR: 0.000152 Logit Scale: 26.011 Contrastive_loss: 0.082063 (0.19689) Loss: 0.082063 (0.19689) 2025-03-19,09:15:49 | INFO | Train Epoch: 10 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 148.810/s, 148.810/s/gpu LR: 0.000152 Logit Scale: 26.019 Contrastive_loss: 0.14505 (0.19489) Loss: 0.14505 (0.19489) 2025-03-19,09:16:11 | INFO | Train Epoch: 10 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 148.264/s, 148.264/s/gpu LR: 0.000152 Logit Scale: 25.955 Contrastive_loss: 0.27684 (0.19793) Loss: 0.27684 (0.19793) 2025-03-19,09:16:33 | INFO | Train Epoch: 10 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 148.281/s, 148.281/s/gpu LR: 0.000152 Logit Scale: 25.919 Contrastive_loss: 0.33329 (0.20276) Loss: 0.33329 (0.20276) 2025-03-19,09:16:54 | INFO | Train Epoch: 10 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 148.682/s, 148.682/s/gpu LR: 0.000151 Logit Scale: 25.970 Contrastive_loss: 0.13301 (0.20036) Loss: 0.13301 (0.20036) 2025-03-19,09:17:16 | INFO | Train Epoch: 10 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 148.713/s, 148.713/s/gpu LR: 0.000151 Logit Scale: 26.009 Contrastive_loss: 0.33251 (0.20476) Loss: 0.33251 (0.20476) 2025-03-19,09:17:37 | INFO | Train Epoch: 10 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.159/s, 149.159/s/gpu LR: 0.000151 Logit Scale: 26.023 Contrastive_loss: 0.11827 (0.20197) Loss: 0.11827 (0.20197) 2025-03-19,09:17:59 | INFO | Train Epoch: 10 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.767/s, 148.767/s/gpu LR: 0.000151 Logit Scale: 26.001 Contrastive_loss: 0.15102 (0.20038) Loss: 0.15102 (0.20038) 2025-03-19,09:18:20 | INFO | Train Epoch: 10 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.589/s, 148.589/s/gpu LR: 0.000151 Logit Scale: 26.001 Contrastive_loss: 0.57498 (0.21173) Loss: 0.57498 (0.21173) 2025-03-19,09:18:42 | INFO | Train Epoch: 10 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.452/s, 148.452/s/gpu LR: 0.000151 Logit Scale: 25.997 Contrastive_loss: 0.15725 (0.21013) Loss: 0.15725 (0.21013) 2025-03-19,09:19:04 | INFO | Train Epoch: 10 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.218, 146.701/s, 146.701/s/gpu LR: 0.000151 Logit Scale: 26.008 Contrastive_loss: 0.29438 (0.21254) Loss: 0.29438 (0.21254) 2025-03-19,09:19:25 | INFO | Train Epoch: 10 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.889/s, 148.889/s/gpu LR: 0.000151 Logit Scale: 25.976 Contrastive_loss: 0.37440 (0.21703) Loss: 0.37440 (0.21703) 2025-03-19,09:19:47 | INFO | Train Epoch: 10 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 146.499/s, 146.499/s/gpu LR: 0.000151 Logit Scale: 25.984 Contrastive_loss: 0.16920 (0.21574) Loss: 0.16920 (0.21574) 2025-03-19,09:20:08 | INFO | Train Epoch: 10 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.767/s, 149.767/s/gpu LR: 0.000151 Logit Scale: 26.010 Contrastive_loss: 0.20633 (0.21549) Loss: 0.20633 (0.21549) 2025-03-19,09:20:30 | INFO | Train Epoch: 10 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 150.290/s, 150.290/s/gpu LR: 0.000151 Logit Scale: 25.992 Contrastive_loss: 0.084312 (0.21213) Loss: 0.084312 (0.21213) 2025-03-19,09:20:51 | INFO | Train Epoch: 10 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 149.717/s, 149.717/s/gpu LR: 0.000151 Logit Scale: 25.991 Contrastive_loss: 0.056917 (0.20825) Loss: 0.056917 (0.20825) 2025-03-19,09:21:12 | INFO | Train Epoch: 10 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 149.956/s, 149.956/s/gpu LR: 0.000151 Logit Scale: 25.977 Contrastive_loss: 0.25529 (0.20940) Loss: 0.25529 (0.20940) 2025-03-19,09:21:34 | INFO | Train Epoch: 10 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.213, 150.228/s, 150.228/s/gpu LR: 0.000151 Logit Scale: 25.988 Contrastive_loss: 0.23854 (0.21009) Loss: 0.23854 (0.21009) 2025-03-19,09:21:55 | INFO | Train Epoch: 10 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 150.552/s, 150.552/s/gpu LR: 0.000151 Logit Scale: 25.982 Contrastive_loss: 0.14284 (0.20853) Loss: 0.14284 (0.20853) 2025-03-19,09:22:16 | INFO | Train Epoch: 10 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.915/s, 149.915/s/gpu LR: 0.000151 Logit Scale: 26.024 Contrastive_loss: 0.12969 (0.20673) Loss: 0.12969 (0.20673) 2025-03-19,09:22:38 | INFO | Train Epoch: 10 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 151.253/s, 151.253/s/gpu LR: 0.000151 Logit Scale: 25.920 Contrastive_loss: 0.23746 (0.20742) Loss: 0.23746 (0.20742) 2025-03-19,09:22:59 | INFO | Train Epoch: 10 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 150.952/s, 150.952/s/gpu LR: 0.000151 Logit Scale: 25.919 Contrastive_loss: 0.19604 (0.20717) Loss: 0.19604 (0.20717) 2025-03-19,09:23:20 | INFO | Train Epoch: 10 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.213, 147.514/s, 147.514/s/gpu LR: 0.000151 Logit Scale: 25.929 Contrastive_loss: 0.054958 (0.20393) Loss: 0.054958 (0.20393) 2025-03-19,09:23:42 | INFO | Train Epoch: 10 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 148.398/s, 148.398/s/gpu LR: 0.000151 Logit Scale: 25.914 Contrastive_loss: 0.052896 (0.20079) Loss: 0.052896 (0.20079) 2025-03-19,09:24:04 | INFO | Train Epoch: 10 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 148.480/s, 148.480/s/gpu LR: 0.000151 Logit Scale: 25.962 Contrastive_loss: 0.085547 (0.19843) Loss: 0.085547 (0.19843) 2025-03-19,09:24:25 | INFO | Train Epoch: 10 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 148.898/s, 148.898/s/gpu LR: 0.000151 Logit Scale: 25.920 Contrastive_loss: 0.15305 (0.19753) Loss: 0.15305 (0.19753) 2025-03-19,09:24:46 | INFO | Train Epoch: 10 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 148.766/s, 148.766/s/gpu LR: 0.000151 Logit Scale: 25.906 Contrastive_loss: 0.42773 (0.20204) Loss: 0.42773 (0.20204) 2025-03-19,09:25:08 | INFO | Train Epoch: 10 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 151.468/s, 151.468/s/gpu LR: 0.000151 Logit Scale: 25.927 Contrastive_loss: 0.12721 (0.20060) Loss: 0.12721 (0.20060) 2025-03-19,09:25:30 | INFO | Train Epoch: 10 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 148.707/s, 148.707/s/gpu LR: 0.000151 Logit Scale: 25.890 Contrastive_loss: 0.10154 (0.19873) Loss: 0.10154 (0.19873) 2025-03-19,09:25:51 | INFO | Train Epoch: 10 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 149.199/s, 149.199/s/gpu LR: 0.000151 Logit Scale: 25.855 Contrastive_loss: 0.13872 (0.19762) Loss: 0.13872 (0.19762) 2025-03-19,09:26:13 | INFO | Train Epoch: 10 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.097/s, 149.097/s/gpu LR: 0.000150 Logit Scale: 25.893 Contrastive_loss: 0.13321 (0.19645) Loss: 0.13321 (0.19645) 2025-03-19,09:26:34 | INFO | Train Epoch: 10 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.117/s, 149.117/s/gpu LR: 0.000150 Logit Scale: 25.867 Contrastive_loss: 0.34669 (0.19913) Loss: 0.34669 (0.19913) 2025-03-19,09:26:56 | INFO | Train Epoch: 10 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 148.252/s, 148.252/s/gpu LR: 0.000150 Logit Scale: 25.916 Contrastive_loss: 0.17341 (0.19868) Loss: 0.17341 (0.19868) 2025-03-19,09:27:17 | INFO | Train Epoch: 10 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.719/s, 149.719/s/gpu LR: 0.000150 Logit Scale: 25.915 Contrastive_loss: 0.033921 (0.19584) Loss: 0.033921 (0.19584) 2025-03-19,09:27:39 | INFO | Train Epoch: 10 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.218, 146.570/s, 146.570/s/gpu LR: 0.000150 Logit Scale: 25.909 Contrastive_loss: 0.16885 (0.19538) Loss: 0.16885 (0.19538) 2025-03-19,09:28:01 | INFO | Train Epoch: 10 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 146.476/s, 146.476/s/gpu LR: 0.000150 Logit Scale: 25.876 Contrastive_loss: 0.30443 (0.19720) Loss: 0.30443 (0.19720) 2025-03-19,09:28:23 | INFO | Train Epoch: 10 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.219, 146.580/s, 146.580/s/gpu LR: 0.000150 Logit Scale: 25.867 Contrastive_loss: 0.34420 (0.19961) Loss: 0.34420 (0.19961) 2025-03-19,09:28:44 | INFO | Train Epoch: 10 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 146.576/s, 146.576/s/gpu LR: 0.000150 Logit Scale: 25.871 Contrastive_loss: 0.15423 (0.19888) Loss: 0.15423 (0.19888) 2025-03-19,09:29:06 | INFO | Train Epoch: 10 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 148.648/s, 148.648/s/gpu LR: 0.000150 Logit Scale: 25.903 Contrastive_loss: 0.042435 (0.19639) Loss: 0.042435 (0.19639) 2025-03-19,09:29:27 | INFO | Train Epoch: 10 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 148.275/s, 148.275/s/gpu LR: 0.000150 Logit Scale: 25.918 Contrastive_loss: 0.15398 (0.19573) Loss: 0.15398 (0.19573) 2025-03-19,09:29:49 | INFO | Train Epoch: 10 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 148.132/s, 148.132/s/gpu LR: 0.000150 Logit Scale: 25.930 Contrastive_loss: 0.55178 (0.20121) Loss: 0.55178 (0.20121) 2025-03-19,09:30:10 | INFO | Train Epoch: 10 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.957/s, 149.957/s/gpu LR: 0.000150 Logit Scale: 25.972 Contrastive_loss: 0.32566 (0.20309) Loss: 0.32566 (0.20309) 2025-03-19,09:30:32 | INFO | Train Epoch: 10 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 147.800/s, 147.800/s/gpu LR: 0.000150 Logit Scale: 25.971 Contrastive_loss: 0.21956 (0.20334) Loss: 0.21956 (0.20334) 2025-03-19,09:30:54 | INFO | Train Epoch: 10 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.736/s, 148.736/s/gpu LR: 0.000150 Logit Scale: 25.987 Contrastive_loss: 0.12898 (0.20225) Loss: 0.12898 (0.20225) 2025-03-19,09:31:15 | INFO | Train Epoch: 10 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 146.710/s, 146.710/s/gpu LR: 0.000150 Logit Scale: 25.994 Contrastive_loss: 0.041168 (0.19991) Loss: 0.041168 (0.19991) 2025-03-19,09:31:37 | INFO | Train Epoch: 10 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.218, 148.749/s, 148.749/s/gpu LR: 0.000150 Logit Scale: 25.986 Contrastive_loss: 0.53513 (0.20470) Loss: 0.53513 (0.20470) 2025-03-19,09:31:59 | INFO | Train Epoch: 10 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 147.097/s, 147.097/s/gpu LR: 0.000150 Logit Scale: 25.925 Contrastive_loss: 0.22030 (0.20492) Loss: 0.22030 (0.20492) 2025-03-19,09:32:20 | INFO | Train Epoch: 10 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 148.802/s, 148.802/s/gpu LR: 0.000150 Logit Scale: 25.938 Contrastive_loss: 0.40807 (0.20774) Loss: 0.40807 (0.20774) 2025-03-19,09:32:42 | INFO | Train Epoch: 10 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 147.536/s, 147.536/s/gpu LR: 0.000150 Logit Scale: 25.916 Contrastive_loss: 0.40636 (0.21046) Loss: 0.40636 (0.21046) 2025-03-19,09:33:03 | INFO | Train Epoch: 10 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.277/s, 149.277/s/gpu LR: 0.000150 Logit Scale: 25.911 Contrastive_loss: 0.24548 (0.21094) Loss: 0.24548 (0.21094) 2025-03-19,09:33:25 | INFO | Train Epoch: 10 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 144.504/s, 144.504/s/gpu LR: 0.000150 Logit Scale: 25.912 Contrastive_loss: 0.14762 (0.21009) Loss: 0.14762 (0.21009) 2025-03-19,09:33:46 | INFO | Train Epoch: 10 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.722/s, 149.722/s/gpu LR: 0.000150 Logit Scale: 25.899 Contrastive_loss: 0.071966 (0.20827) Loss: 0.071966 (0.20827) 2025-03-19,09:34:08 | INFO | Train Epoch: 10 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 149.351/s, 149.351/s/gpu LR: 0.000150 Logit Scale: 25.899 Contrastive_loss: 0.082704 (0.20664) Loss: 0.082704 (0.20664) 2025-03-19,09:34:29 | INFO | Train Epoch: 10 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 149.398/s, 149.398/s/gpu LR: 0.000150 Logit Scale: 25.895 Contrastive_loss: 0.34879 (0.20847) Loss: 0.34879 (0.20847) 2025-03-19,09:34:51 | INFO | Train Epoch: 10 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 149.285/s, 149.285/s/gpu LR: 0.000150 Logit Scale: 25.874 Contrastive_loss: 0.11465 (0.20728) Loss: 0.11465 (0.20728) 2025-03-19,09:35:12 | INFO | Train Epoch: 10 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 149.015/s, 149.015/s/gpu LR: 0.000150 Logit Scale: 25.899 Contrastive_loss: 0.36053 (0.20919) Loss: 0.36053 (0.20919) 2025-03-19,09:35:34 | INFO | Train Epoch: 10 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.364/s, 150.364/s/gpu LR: 0.000149 Logit Scale: 25.933 Contrastive_loss: 0.23239 (0.20948) Loss: 0.23239 (0.20948) 2025-03-19,09:35:55 | INFO | Train Epoch: 10 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 149.538/s, 149.538/s/gpu LR: 0.000149 Logit Scale: 25.953 Contrastive_loss: 0.25550 (0.21004) Loss: 0.25550 (0.21004) 2025-03-19,09:36:16 | INFO | Train Epoch: 10 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.952/s, 149.952/s/gpu LR: 0.000149 Logit Scale: 25.992 Contrastive_loss: 0.52778 (0.21387) Loss: 0.52778 (0.21387) 2025-03-19,09:36:38 | INFO | Train Epoch: 10 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 149.351/s, 149.351/s/gpu LR: 0.000149 Logit Scale: 25.942 Contrastive_loss: 0.15992 (0.21323) Loss: 0.15992 (0.21323) 2025-03-19,09:36:59 | INFO | Train Epoch: 10 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 149.207/s, 149.207/s/gpu LR: 0.000149 Logit Scale: 25.953 Contrastive_loss: 0.24532 (0.21361) Loss: 0.24532 (0.21361) 2025-03-19,09:37:21 | INFO | Train Epoch: 10 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 148.628/s, 148.628/s/gpu LR: 0.000149 Logit Scale: 25.966 Contrastive_loss: 0.41997 (0.21601) Loss: 0.41997 (0.21601) 2025-03-19,09:37:42 | INFO | Train Epoch: 10 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 149.378/s, 149.378/s/gpu LR: 0.000149 Logit Scale: 25.919 Contrastive_loss: 0.10094 (0.21468) Loss: 0.10094 (0.21468) 2025-03-19,09:38:04 | INFO | Train Epoch: 10 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 148.460/s, 148.460/s/gpu LR: 0.000149 Logit Scale: 25.936 Contrastive_loss: 0.14685 (0.21391) Loss: 0.14685 (0.21391) 2025-03-19,09:38:25 | INFO | Train Epoch: 10 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.212, 148.484/s, 148.484/s/gpu LR: 0.000149 Logit Scale: 25.941 Contrastive_loss: 0.11694 (0.21282) Loss: 0.11694 (0.21282) 2025-03-19,09:38:46 | INFO | Train Epoch: 10 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.213, 149.244/s, 149.244/s/gpu LR: 0.000149 Logit Scale: 25.951 Contrastive_loss: 0.15235 (0.21215) Loss: 0.15235 (0.21215) 2025-03-19,09:39:07 | INFO | Train Epoch: 10 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.324/s, 149.324/s/gpu LR: 0.000149 Logit Scale: 25.969 Contrastive_loss: 0.18815 (0.21189) Loss: 0.18815 (0.21189) 2025-03-19,09:39:29 | INFO | Train Epoch: 10 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 146.542/s, 146.542/s/gpu LR: 0.000149 Logit Scale: 25.930 Contrastive_loss: 0.10829 (0.21076) Loss: 0.10829 (0.21076) 2025-03-19,09:39:51 | INFO | Train Epoch: 10 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.797/s, 148.797/s/gpu LR: 0.000149 Logit Scale: 25.913 Contrastive_loss: 0.17646 (0.21039) Loss: 0.17646 (0.21039) 2025-03-19,09:40:12 | INFO | Train Epoch: 10 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 150.697/s, 150.697/s/gpu LR: 0.000149 Logit Scale: 25.949 Contrastive_loss: 0.15119 (0.20976) Loss: 0.15119 (0.20976) 2025-03-19,09:40:34 | INFO | Train Epoch: 10 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.218, 147.976/s, 147.976/s/gpu LR: 0.000149 Logit Scale: 25.939 Contrastive_loss: 0.31248 (0.21084) Loss: 0.31248 (0.21084) 2025-03-19,09:40:56 | INFO | Train Epoch: 10 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.219, 146.740/s, 146.740/s/gpu LR: 0.000149 Logit Scale: 25.908 Contrastive_loss: 0.21069 (0.21084) Loss: 0.21069 (0.21084) 2025-03-19,09:41:17 | INFO | Train Epoch: 10 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.218, 143.461/s, 143.461/s/gpu LR: 0.000149 Logit Scale: 25.929 Contrastive_loss: 0.080123 (0.20949) Loss: 0.080123 (0.20949) 2025-03-19,09:41:39 | INFO | Train Epoch: 10 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 143.740/s, 143.740/s/gpu LR: 0.000149 Logit Scale: 25.905 Contrastive_loss: 0.21999 (0.20960) Loss: 0.21999 (0.20960) 2025-03-19,09:42:01 | INFO | Train Epoch: 10 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 148.758/s, 148.758/s/gpu LR: 0.000149 Logit Scale: 25.860 Contrastive_loss: 0.17505 (0.20925) Loss: 0.17505 (0.20925) 2025-03-19,09:42:23 | INFO | Train Epoch: 10 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.218, 147.443/s, 147.443/s/gpu LR: 0.000149 Logit Scale: 25.834 Contrastive_loss: 0.095934 (0.20812) Loss: 0.095934 (0.20812) 2025-03-19,09:42:44 | INFO | Train Epoch: 10 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.217, 146.119/s, 146.119/s/gpu LR: 0.000149 Logit Scale: 25.838 Contrastive_loss: 0.096315 (0.20701) Loss: 0.096315 (0.20701) 2025-03-19,09:43:06 | INFO | Train Epoch: 10 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 147.411/s, 147.411/s/gpu LR: 0.000149 Logit Scale: 25.835 Contrastive_loss: 0.10163 (0.20598) Loss: 0.10163 (0.20598) 2025-03-19,09:43:28 | INFO | Train Epoch: 10 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.219, 146.728/s, 146.728/s/gpu LR: 0.000149 Logit Scale: 25.823 Contrastive_loss: 0.56761 (0.20949) Loss: 0.56761 (0.20949) 2025-03-19,09:43:50 | INFO | Train Epoch: 10 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 147.852/s, 147.852/s/gpu LR: 0.000149 Logit Scale: 25.860 Contrastive_loss: 0.34516 (0.21079) Loss: 0.34516 (0.21079) 2025-03-19,09:44:12 | INFO | Train Epoch: 10 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.221, 146.139/s, 146.139/s/gpu LR: 0.000149 Logit Scale: 25.852 Contrastive_loss: 0.18201 (0.21052) Loss: 0.18201 (0.21052) 2025-03-19,09:44:33 | INFO | Train Epoch: 10 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.217, 148.081/s, 148.081/s/gpu LR: 0.000149 Logit Scale: 25.820 Contrastive_loss: 0.24001 (0.21080) Loss: 0.24001 (0.21080) 2025-03-19,09:44:55 | INFO | Train Epoch: 10 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 149.661/s, 149.661/s/gpu LR: 0.000148 Logit Scale: 25.800 Contrastive_loss: 0.55036 (0.21397) Loss: 0.55036 (0.21397) 2025-03-19,09:45:16 | INFO | Train Epoch: 10 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 149.303/s, 149.303/s/gpu LR: 0.000148 Logit Scale: 25.853 Contrastive_loss: 0.041158 (0.21237) Loss: 0.041158 (0.21237) 2025-03-19,09:45:38 | INFO | Train Epoch: 10 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.921/s, 149.921/s/gpu LR: 0.000148 Logit Scale: 25.875 Contrastive_loss: 0.20665 (0.21232) Loss: 0.20665 (0.21232) 2025-03-19,09:45:59 | INFO | Train Epoch: 10 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.451/s, 149.451/s/gpu LR: 0.000148 Logit Scale: 25.888 Contrastive_loss: 0.16298 (0.21187) Loss: 0.16298 (0.21187) 2025-03-19,09:46:21 | INFO | Train Epoch: 10 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.219, 147.666/s, 147.666/s/gpu LR: 0.000148 Logit Scale: 25.913 Contrastive_loss: 0.38048 (0.21339) Loss: 0.38048 (0.21339) 2025-03-19,09:46:43 | INFO | Train Epoch: 10 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.218, 145.524/s, 145.524/s/gpu LR: 0.000148 Logit Scale: 25.905 Contrastive_loss: 0.079717 (0.21220) Loss: 0.079717 (0.21220) 2025-03-19,09:47:05 | INFO | Train Epoch: 10 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.220, 147.095/s, 147.095/s/gpu LR: 0.000148 Logit Scale: 25.858 Contrastive_loss: 0.47801 (0.21455) Loss: 0.47801 (0.21455) 2025-03-19,09:47:27 | INFO | Train Epoch: 10 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 144.052/s, 144.052/s/gpu LR: 0.000148 Logit Scale: 25.883 Contrastive_loss: 0.073926 (0.21331) Loss: 0.073926 (0.21331) 2025-03-19,09:47:49 | INFO | Train Epoch: 10 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.221, 145.254/s, 145.254/s/gpu LR: 0.000148 Logit Scale: 25.906 Contrastive_loss: 0.36167 (0.21460) Loss: 0.36167 (0.21460) 2025-03-19,09:48:11 | INFO | Train Epoch: 10 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 149.913/s, 149.913/s/gpu LR: 0.000148 Logit Scale: 25.905 Contrastive_loss: 0.15579 (0.21410) Loss: 0.15579 (0.21410) 2025-03-19,09:48:32 | INFO | Train Epoch: 10 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.218, 148.568/s, 148.568/s/gpu LR: 0.000148 Logit Scale: 25.950 Contrastive_loss: 0.23158 (0.21425) Loss: 0.23158 (0.21425) 2025-03-19,09:48:54 | INFO | Train Epoch: 10 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 146.463/s, 146.463/s/gpu LR: 0.000148 Logit Scale: 25.966 Contrastive_loss: 0.41392 (0.21594) Loss: 0.41392 (0.21594) 2025-03-19,09:49:16 | INFO | Train Epoch: 10 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 146.965/s, 146.965/s/gpu LR: 0.000148 Logit Scale: 25.928 Contrastive_loss: 0.36334 (0.21718) Loss: 0.36334 (0.21718) 2025-03-19,09:49:38 | INFO | Train Epoch: 10 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.217, 147.722/s, 147.722/s/gpu LR: 0.000148 Logit Scale: 25.917 Contrastive_loss: 0.27137 (0.21763) Loss: 0.27137 (0.21763) 2025-03-19,09:49:59 | INFO | Train Epoch: 10 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 143.440/s, 143.440/s/gpu LR: 0.000148 Logit Scale: 25.889 Contrastive_loss: 0.29931 (0.21830) Loss: 0.29931 (0.21830) 2025-03-19,09:50:21 | INFO | Train Epoch: 10 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.218, 150.337/s, 150.337/s/gpu LR: 0.000148 Logit Scale: 25.865 Contrastive_loss: 0.27767 (0.21879) Loss: 0.27767 (0.21879) 2025-03-19,09:50:42 | INFO | Train Epoch: 10 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.213, 150.277/s, 150.277/s/gpu LR: 0.000148 Logit Scale: 25.913 Contrastive_loss: 0.28961 (0.21937) Loss: 0.28961 (0.21937) 2025-03-19,09:51:04 | INFO | Train Epoch: 10 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 147.366/s, 147.366/s/gpu LR: 0.000148 Logit Scale: 25.942 Contrastive_loss: 0.11946 (0.21856) Loss: 0.11946 (0.21856) 2025-03-19,09:51:25 | INFO | Train Epoch: 10 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.218, 146.310/s, 146.310/s/gpu LR: 0.000148 Logit Scale: 25.915 Contrastive_loss: 0.071889 (0.21739) Loss: 0.071889 (0.21739) 2025-03-19,09:51:47 | INFO | Train Epoch: 10 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.217, 147.475/s, 147.475/s/gpu LR: 0.000148 Logit Scale: 25.946 Contrastive_loss: 0.35149 (0.21845) Loss: 0.35149 (0.21845) 2025-03-19,09:52:09 | INFO | Train Epoch: 10 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 146.702/s, 146.702/s/gpu LR: 0.000148 Logit Scale: 25.924 Contrastive_loss: 0.019374 (0.21688) Loss: 0.019374 (0.21688) 2025-03-19,09:52:30 | INFO | Train Epoch: 10 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 148.681/s, 148.681/s/gpu LR: 0.000148 Logit Scale: 25.912 Contrastive_loss: 0.43649 (0.21860) Loss: 0.43649 (0.21860) 2025-03-19,09:52:52 | INFO | Train Epoch: 10 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 145.282/s, 145.282/s/gpu LR: 0.000148 Logit Scale: 25.947 Contrastive_loss: 0.24816 (0.21883) Loss: 0.24816 (0.21883) 2025-03-19,09:53:13 | INFO | Train Epoch: 10 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 144.592/s, 144.592/s/gpu LR: 0.000148 Logit Scale: 25.867 Contrastive_loss: 0.33955 (0.21976) Loss: 0.33955 (0.21976) 2025-03-19,09:53:35 | INFO | Train Epoch: 10 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.220, 145.447/s, 145.447/s/gpu LR: 0.000148 Logit Scale: 25.836 Contrastive_loss: 0.41907 (0.22128) Loss: 0.41907 (0.22128) 2025-03-19,09:53:57 | INFO | Train Epoch: 10 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.220, 149.838/s, 149.838/s/gpu LR: 0.000148 Logit Scale: 25.897 Contrastive_loss: 0.22195 (0.22128) Loss: 0.22195 (0.22128) 2025-03-19,09:54:19 | INFO | Train Epoch: 10 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 147.217/s, 147.217/s/gpu LR: 0.000147 Logit Scale: 25.857 Contrastive_loss: 0.17922 (0.22097) Loss: 0.17922 (0.22097) 2025-03-19,09:54:40 | INFO | Train Epoch: 10 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 152.041/s, 152.041/s/gpu LR: 0.000147 Logit Scale: 25.860 Contrastive_loss: 0.43749 (0.22258) Loss: 0.43749 (0.22258) 2025-03-19,09:55:01 | INFO | Train Epoch: 10 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 149.781/s, 149.781/s/gpu LR: 0.000147 Logit Scale: 25.866 Contrastive_loss: 0.37064 (0.22368) Loss: 0.37064 (0.22368) 2025-03-19,09:55:23 | INFO | Train Epoch: 10 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 150.089/s, 150.089/s/gpu LR: 0.000147 Logit Scale: 25.897 Contrastive_loss: 0.23926 (0.22380) Loss: 0.23926 (0.22380) 2025-03-19,09:55:44 | INFO | Train Epoch: 10 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 143.823/s, 143.823/s/gpu LR: 0.000147 Logit Scale: 25.923 Contrastive_loss: 0.27181 (0.22415) Loss: 0.27181 (0.22415) 2025-03-19,09:56:05 | INFO | Train Epoch: 10 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 151.398/s, 151.398/s/gpu LR: 0.000147 Logit Scale: 25.885 Contrastive_loss: 0.10015 (0.22325) Loss: 0.10015 (0.22325) 2025-03-19,09:56:27 | INFO | Train Epoch: 10 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 151.318/s, 151.318/s/gpu LR: 0.000147 Logit Scale: 25.899 Contrastive_loss: 0.084935 (0.22225) Loss: 0.084935 (0.22225) 2025-03-19,09:56:48 | INFO | Train Epoch: 10 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 151.354/s, 151.354/s/gpu LR: 0.000147 Logit Scale: 25.894 Contrastive_loss: 0.14229 (0.22168) Loss: 0.14229 (0.22168) 2025-03-19,09:57:10 | INFO | Train Epoch: 10 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.213, 150.892/s, 150.892/s/gpu LR: 0.000147 Logit Scale: 25.908 Contrastive_loss: 0.39990 (0.22295) Loss: 0.39990 (0.22295) 2025-03-19,09:57:31 | INFO | Train Epoch: 10 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.213, 146.662/s, 146.662/s/gpu LR: 0.000147 Logit Scale: 25.912 Contrastive_loss: 0.015455 (0.22148) Loss: 0.015455 (0.22148) 2025-03-19,09:57:53 | INFO | Train Epoch: 10 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 146.701/s, 146.701/s/gpu LR: 0.000147 Logit Scale: 25.887 Contrastive_loss: 0.30967 (0.22210) Loss: 0.30967 (0.22210) 2025-03-19,09:58:14 | INFO | Train Epoch: 10 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 151.186/s, 151.186/s/gpu LR: 0.000147 Logit Scale: 25.892 Contrastive_loss: 0.16123 (0.22168) Loss: 0.16123 (0.22168) 2025-03-19,09:58:36 | INFO | Train Epoch: 10 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 148.295/s, 148.295/s/gpu LR: 0.000147 Logit Scale: 25.891 Contrastive_loss: 0.28521 (0.22212) Loss: 0.28521 (0.22212) 2025-03-19,09:58:57 | INFO | Train Epoch: 10 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.992/s, 149.992/s/gpu LR: 0.000147 Logit Scale: 25.879 Contrastive_loss: 0.17754 (0.22181) Loss: 0.17754 (0.22181) 2025-03-19,09:59:19 | INFO | Train Epoch: 10 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 147.769/s, 147.769/s/gpu LR: 0.000147 Logit Scale: 25.878 Contrastive_loss: 0.25795 (0.22206) Loss: 0.25795 (0.22206) 2025-03-19,09:59:40 | INFO | Train Epoch: 10 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 149.615/s, 149.615/s/gpu LR: 0.000147 Logit Scale: 25.891 Contrastive_loss: 0.11082 (0.22131) Loss: 0.11082 (0.22131) 2025-03-19,10:00:02 | INFO | Train Epoch: 10 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.683/s, 149.683/s/gpu LR: 0.000147 Logit Scale: 25.894 Contrastive_loss: 0.12213 (0.22064) Loss: 0.12213 (0.22064) 2025-03-19,10:00:23 | INFO | Train Epoch: 10 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 151.418/s, 151.418/s/gpu LR: 0.000147 Logit Scale: 25.907 Contrastive_loss: 0.15058 (0.22017) Loss: 0.15058 (0.22017) 2025-03-19,10:00:45 | INFO | Train Epoch: 10 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 148.903/s, 148.903/s/gpu LR: 0.000147 Logit Scale: 25.861 Contrastive_loss: 0.065955 (0.21915) Loss: 0.065955 (0.21915) 2025-03-19,10:01:06 | INFO | Train Epoch: 10 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.808/s, 148.808/s/gpu LR: 0.000147 Logit Scale: 25.866 Contrastive_loss: 0.023395 (0.21786) Loss: 0.023395 (0.21786) 2025-03-19,10:01:28 | INFO | Train Epoch: 10 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 149.225/s, 149.225/s/gpu LR: 0.000147 Logit Scale: 25.855 Contrastive_loss: 0.25077 (0.21808) Loss: 0.25077 (0.21808) 2025-03-19,10:01:49 | INFO | Train Epoch: 10 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 148.443/s, 148.443/s/gpu LR: 0.000147 Logit Scale: 25.845 Contrastive_loss: 0.053735 (0.21701) Loss: 0.053735 (0.21701) 2025-03-19,10:02:11 | INFO | Train Epoch: 10 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 151.425/s, 151.425/s/gpu LR: 0.000147 Logit Scale: 25.870 Contrastive_loss: 0.097335 (0.21624) Loss: 0.097335 (0.21624) 2025-03-19,10:02:32 | INFO | Train Epoch: 10 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 149.198/s, 149.198/s/gpu LR: 0.000147 Logit Scale: 25.852 Contrastive_loss: 0.32844 (0.21696) Loss: 0.32844 (0.21696) 2025-03-19,10:02:53 | INFO | Train Epoch: 10 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 149.379/s, 149.379/s/gpu LR: 0.000147 Logit Scale: 25.861 Contrastive_loss: 0.22792 (0.21703) Loss: 0.22792 (0.21703) 2025-03-19,10:03:14 | INFO | Train Epoch: 10 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.211, 151.514/s, 151.514/s/gpu LR: 0.000146 Logit Scale: 25.853 Contrastive_loss: 0.33336 (0.21776) Loss: 0.33336 (0.21776) 2025-03-19,10:03:36 | INFO | Train Epoch: 10 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 148.776/s, 148.776/s/gpu LR: 0.000146 Logit Scale: 25.865 Contrastive_loss: 0.31238 (0.21836) Loss: 0.31238 (0.21836) 2025-03-19,10:03:57 | INFO | Train Epoch: 10 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 151.647/s, 151.647/s/gpu LR: 0.000146 Logit Scale: 25.871 Contrastive_loss: 0.26778 (0.21867) Loss: 0.26778 (0.21867) 2025-03-19,10:04:19 | INFO | Train Epoch: 10 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 148.439/s, 148.439/s/gpu LR: 0.000146 Logit Scale: 25.898 Contrastive_loss: 0.15152 (0.21825) Loss: 0.15152 (0.21825) 2025-03-19,10:04:40 | INFO | Train Epoch: 10 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.218, 147.576/s, 147.576/s/gpu LR: 0.000146 Logit Scale: 25.932 Contrastive_loss: 0.19683 (0.21812) Loss: 0.19683 (0.21812) 2025-03-19,10:05:02 | INFO | Train Epoch: 10 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 148.587/s, 148.587/s/gpu LR: 0.000146 Logit Scale: 25.961 Contrastive_loss: 0.19321 (0.21797) Loss: 0.19321 (0.21797) 2025-03-19,10:05:24 | INFO | Train Epoch: 10 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 149.960/s, 149.960/s/gpu LR: 0.000146 Logit Scale: 25.905 Contrastive_loss: 0.22669 (0.21802) Loss: 0.22669 (0.21802) 2025-03-19,10:05:45 | INFO | Train Epoch: 10 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 146.280/s, 146.280/s/gpu LR: 0.000146 Logit Scale: 25.956 Contrastive_loss: 0.19405 (0.21787) Loss: 0.19405 (0.21787) 2025-03-19,10:06:07 | INFO | Train Epoch: 10 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 146.693/s, 146.693/s/gpu LR: 0.000146 Logit Scale: 25.940 Contrastive_loss: 0.11023 (0.21723) Loss: 0.11023 (0.21723) 2025-03-19,10:06:29 | INFO | Train Epoch: 10 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 149.264/s, 149.264/s/gpu LR: 0.000146 Logit Scale: 25.949 Contrastive_loss: 0.16936 (0.21694) Loss: 0.16936 (0.21694) 2025-03-19,10:06:50 | INFO | Train Epoch: 10 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 149.187/s, 149.187/s/gpu LR: 0.000146 Logit Scale: 25.905 Contrastive_loss: 0.091416 (0.21619) Loss: 0.091416 (0.21619) 2025-03-19,10:07:12 | INFO | Train Epoch: 10 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 149.060/s, 149.060/s/gpu LR: 0.000146 Logit Scale: 25.939 Contrastive_loss: 0.16240 (0.21587) Loss: 0.16240 (0.21587) 2025-03-19,10:07:33 | INFO | Train Epoch: 10 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 149.674/s, 149.674/s/gpu LR: 0.000146 Logit Scale: 25.979 Contrastive_loss: 0.24233 (0.21603) Loss: 0.24233 (0.21603) 2025-03-19,10:07:55 | INFO | Train Epoch: 10 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 147.372/s, 147.372/s/gpu LR: 0.000146 Logit Scale: 25.981 Contrastive_loss: 0.27433 (0.21637) Loss: 0.27433 (0.21637) 2025-03-19,10:08:17 | INFO | Train Epoch: 10 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 149.292/s, 149.292/s/gpu LR: 0.000146 Logit Scale: 25.974 Contrastive_loss: 0.054547 (0.21543) Loss: 0.054547 (0.21543) 2025-03-19,10:08:38 | INFO | Train Epoch: 10 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 151.218/s, 151.218/s/gpu LR: 0.000146 Logit Scale: 25.981 Contrastive_loss: 0.079613 (0.21464) Loss: 0.079613 (0.21464) 2025-03-19,10:08:59 | INFO | Train Epoch: 10 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 148.947/s, 148.947/s/gpu LR: 0.000146 Logit Scale: 25.932 Contrastive_loss: 0.50676 (0.21632) Loss: 0.50676 (0.21632) 2025-03-19,10:09:21 | INFO | Train Epoch: 10 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.180/s, 149.180/s/gpu LR: 0.000146 Logit Scale: 25.959 Contrastive_loss: 0.14678 (0.21593) Loss: 0.14678 (0.21593) 2025-03-19,10:09:42 | INFO | Train Epoch: 10 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 148.614/s, 148.614/s/gpu LR: 0.000146 Logit Scale: 25.978 Contrastive_loss: 0.58591 (0.21803) Loss: 0.58591 (0.21803) 2025-03-19,10:10:04 | INFO | Train Epoch: 10 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.213, 149.053/s, 149.053/s/gpu LR: 0.000146 Logit Scale: 25.937 Contrastive_loss: 0.076003 (0.21723) Loss: 0.076003 (0.21723) 2025-03-19,10:10:25 | INFO | Train Epoch: 10 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.213, 149.292/s, 149.292/s/gpu LR: 0.000146 Logit Scale: 25.953 Contrastive_loss: 0.39058 (0.21820) Loss: 0.39058 (0.21820) 2025-03-19,10:10:46 | INFO | Train Epoch: 10 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 149.704/s, 149.704/s/gpu LR: 0.000146 Logit Scale: 25.959 Contrastive_loss: 0.35353 (0.21896) Loss: 0.35353 (0.21896) 2025-03-19,10:11:08 | INFO | Train Epoch: 10 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 149.528/s, 149.528/s/gpu LR: 0.000146 Logit Scale: 25.948 Contrastive_loss: 0.40118 (0.21997) Loss: 0.40118 (0.21997) 2025-03-19,10:11:30 | INFO | Train Epoch: 10 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.218, 147.765/s, 147.765/s/gpu LR: 0.000146 Logit Scale: 25.960 Contrastive_loss: 0.14762 (0.21957) Loss: 0.14762 (0.21957) 2025-03-19,10:11:51 | INFO | Train Epoch: 10 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.501/s, 149.501/s/gpu LR: 0.000146 Logit Scale: 25.942 Contrastive_loss: 0.14371 (0.21915) Loss: 0.14371 (0.21915) 2025-03-19,10:12:13 | INFO | Train Epoch: 10 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.167/s, 149.167/s/gpu LR: 0.000146 Logit Scale: 25.928 Contrastive_loss: 0.14090 (0.21872) Loss: 0.14090 (0.21872) 2025-03-19,10:12:34 | INFO | Train Epoch: 10 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 150.002/s, 150.002/s/gpu LR: 0.000145 Logit Scale: 25.927 Contrastive_loss: 0.22836 (0.21878) Loss: 0.22836 (0.21878) 2025-03-19,10:12:56 | INFO | Train Epoch: 10 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 148.912/s, 148.912/s/gpu LR: 0.000145 Logit Scale: 25.935 Contrastive_loss: 0.21500 (0.21876) Loss: 0.21500 (0.21876) 2025-03-19,10:13:17 | INFO | Train Epoch: 10 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 148.598/s, 148.598/s/gpu LR: 0.000145 Logit Scale: 25.964 Contrastive_loss: 0.29005 (0.21914) Loss: 0.29005 (0.21914) 2025-03-19,10:13:39 | INFO | Train Epoch: 10 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 148.994/s, 148.994/s/gpu LR: 0.000145 Logit Scale: 25.922 Contrastive_loss: 0.21602 (0.21912) Loss: 0.21602 (0.21912) 2025-03-19,10:14:00 | INFO | Train Epoch: 10 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.959/s, 149.959/s/gpu LR: 0.000145 Logit Scale: 25.926 Contrastive_loss: 0.24624 (0.21927) Loss: 0.24624 (0.21927) 2025-03-19,10:14:22 | INFO | Train Epoch: 10 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 147.798/s, 147.798/s/gpu LR: 0.000145 Logit Scale: 25.956 Contrastive_loss: 0.081665 (0.21854) Loss: 0.081665 (0.21854) 2025-03-19,10:14:43 | INFO | Train Epoch: 10 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.217, 148.480/s, 148.480/s/gpu LR: 0.000145 Logit Scale: 25.969 Contrastive_loss: 0.15724 (0.21822) Loss: 0.15724 (0.21822) 2025-03-19,10:15:05 | INFO | Train Epoch: 10 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.936/s, 148.936/s/gpu LR: 0.000145 Logit Scale: 25.993 Contrastive_loss: 0.37273 (0.21902) Loss: 0.37273 (0.21902) 2025-03-19,10:15:27 | INFO | Train Epoch: 10 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 149.928/s, 149.928/s/gpu LR: 0.000145 Logit Scale: 26.016 Contrastive_loss: 0.19835 (0.21892) Loss: 0.19835 (0.21892) 2025-03-19,10:15:48 | INFO | Train Epoch: 10 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 147.890/s, 147.890/s/gpu LR: 0.000145 Logit Scale: 26.047 Contrastive_loss: 0.12722 (0.21844) Loss: 0.12722 (0.21844) 2025-03-19,10:16:09 | INFO | Train Epoch: 10 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.213, 147.281/s, 147.281/s/gpu LR: 0.000145 Logit Scale: 26.033 Contrastive_loss: 0.32210 (0.21898) Loss: 0.32210 (0.21898) 2025-03-19,10:16:31 | INFO | Train Epoch: 10 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 141.885/s, 141.885/s/gpu LR: 0.000145 Logit Scale: 26.037 Contrastive_loss: 0.13808 (0.21856) Loss: 0.13808 (0.21856) 2025-03-19,10:16:53 | INFO | Train Epoch: 10 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 147.960/s, 147.960/s/gpu LR: 0.000145 Logit Scale: 26.039 Contrastive_loss: 0.10312 (0.21797) Loss: 0.10312 (0.21797) 2025-03-19,10:17:15 | INFO | Train Epoch: 10 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.217, 149.538/s, 149.538/s/gpu LR: 0.000145 Logit Scale: 26.064 Contrastive_loss: 0.25246 (0.21815) Loss: 0.25246 (0.21815) 2025-03-19,10:17:36 | INFO | Train Epoch: 10 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.478/s, 149.478/s/gpu LR: 0.000145 Logit Scale: 26.015 Contrastive_loss: 0.11950 (0.21765) Loss: 0.11950 (0.21765) 2025-03-19,10:17:58 | INFO | Train Epoch: 10 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 150.342/s, 150.342/s/gpu LR: 0.000145 Logit Scale: 26.021 Contrastive_loss: 0.34306 (0.21828) Loss: 0.34306 (0.21828) 2025-03-19,10:18:19 | INFO | Train Epoch: 10 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.791/s, 149.791/s/gpu LR: 0.000145 Logit Scale: 25.991 Contrastive_loss: 0.21280 (0.21825) Loss: 0.21280 (0.21825) 2025-03-19,10:18:41 | INFO | Train Epoch: 10 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 149.221/s, 149.221/s/gpu LR: 0.000145 Logit Scale: 26.030 Contrastive_loss: 0.098794 (0.21766) Loss: 0.098794 (0.21766) 2025-03-19,10:19:02 | INFO | Train Epoch: 10 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 153.860/s, 153.860/s/gpu LR: 0.000145 Logit Scale: 26.022 Contrastive_loss: 0.50513 (0.21908) Loss: 0.50513 (0.21908) 2025-03-19,10:19:24 | INFO | Train Epoch: 10 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.213, 148.111/s, 148.111/s/gpu LR: 0.000145 Logit Scale: 25.994 Contrastive_loss: 0.19718 (0.21897) Loss: 0.19718 (0.21897) 2025-03-19,10:19:45 | INFO | Train Epoch: 10 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 149.289/s, 149.289/s/gpu LR: 0.000145 Logit Scale: 25.975 Contrastive_loss: 0.58196 (0.22075) Loss: 0.58196 (0.22075) 2025-03-19,10:20:06 | INFO | Train Epoch: 10 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.213, 150.999/s, 150.999/s/gpu LR: 0.000145 Logit Scale: 26.016 Contrastive_loss: 0.24875 (0.22089) Loss: 0.24875 (0.22089) 2025-03-19,10:20:28 | INFO | Train Epoch: 10 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 150.428/s, 150.428/s/gpu LR: 0.000145 Logit Scale: 26.047 Contrastive_loss: 0.13675 (0.22048) Loss: 0.13675 (0.22048) 2025-03-19,10:20:49 | INFO | Train Epoch: 10 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.406/s, 149.406/s/gpu LR: 0.000145 Logit Scale: 26.026 Contrastive_loss: 0.058341 (0.21970) Loss: 0.058341 (0.21970) 2025-03-19,10:21:11 | INFO | Train Epoch: 10 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 151.935/s, 151.935/s/gpu LR: 0.000145 Logit Scale: 25.991 Contrastive_loss: 0.10952 (0.21917) Loss: 0.10952 (0.21917) 2025-03-19,10:21:32 | INFO | Train Epoch: 10 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 150.996/s, 150.996/s/gpu LR: 0.000144 Logit Scale: 26.021 Contrastive_loss: 0.31656 (0.21963) Loss: 0.31656 (0.21963) 2025-03-19,10:21:53 | INFO | Train Epoch: 10 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.213, 150.788/s, 150.788/s/gpu LR: 0.000144 Logit Scale: 26.003 Contrastive_loss: 0.27398 (0.21989) Loss: 0.27398 (0.21989) 2025-03-19,10:22:15 | INFO | Train Epoch: 10 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.873/s, 149.873/s/gpu LR: 0.000144 Logit Scale: 25.972 Contrastive_loss: 0.089131 (0.21927) Loss: 0.089131 (0.21927) 2025-03-19,10:22:36 | INFO | Train Epoch: 10 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 146.366/s, 146.366/s/gpu LR: 0.000144 Logit Scale: 25.943 Contrastive_loss: 0.23130 (0.21933) Loss: 0.23130 (0.21933) 2025-03-19,10:22:58 | INFO | Train Epoch: 10 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 150.767/s, 150.767/s/gpu LR: 0.000144 Logit Scale: 25.951 Contrastive_loss: 0.21524 (0.21931) Loss: 0.21524 (0.21931) 2025-03-19,10:23:19 | INFO | Train Epoch: 10 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 145.980/s, 145.980/s/gpu LR: 0.000144 Logit Scale: 25.965 Contrastive_loss: 0.26999 (0.21955) Loss: 0.26999 (0.21955) 2025-03-19,10:23:41 | INFO | Train Epoch: 10 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.213, 151.170/s, 151.170/s/gpu LR: 0.000144 Logit Scale: 25.950 Contrastive_loss: 0.22388 (0.21957) Loss: 0.22388 (0.21957) 2025-03-19,10:24:02 | INFO | Train Epoch: 10 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.212, 149.617/s, 149.617/s/gpu LR: 0.000144 Logit Scale: 25.970 Contrastive_loss: 0.28550 (0.21987) Loss: 0.28550 (0.21987) 2025-03-19,10:24:23 | INFO | Train Epoch: 10 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.212, 150.836/s, 150.836/s/gpu LR: 0.000144 Logit Scale: 25.982 Contrastive_loss: 0.10841 (0.21936) Loss: 0.10841 (0.21936) 2025-03-19,10:24:45 | INFO | Train Epoch: 10 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 150.935/s, 150.935/s/gpu LR: 0.000144 Logit Scale: 25.981 Contrastive_loss: 0.25885 (0.21954) Loss: 0.25885 (0.21954) 2025-03-19,10:25:06 | INFO | Train Epoch: 10 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 150.239/s, 150.239/s/gpu LR: 0.000144 Logit Scale: 25.981 Contrastive_loss: 0.26000 (0.21972) Loss: 0.26000 (0.21972) 2025-03-19,10:25:28 | INFO | Train Epoch: 10 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 149.133/s, 149.133/s/gpu LR: 0.000144 Logit Scale: 25.999 Contrastive_loss: 0.085245 (0.21911) Loss: 0.085245 (0.21911) 2025-03-19,10:25:49 | INFO | Train Epoch: 10 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 148.171/s, 148.171/s/gpu LR: 0.000144 Logit Scale: 25.943 Contrastive_loss: 0.11905 (0.21866) Loss: 0.11905 (0.21866) 2025-03-19,10:26:11 | INFO | Train Epoch: 10 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 147.814/s, 147.814/s/gpu LR: 0.000144 Logit Scale: 25.968 Contrastive_loss: 0.26267 (0.21886) Loss: 0.26267 (0.21886) 2025-03-19,10:26:32 | INFO | Train Epoch: 10 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.213, 152.010/s, 152.010/s/gpu LR: 0.000144 Logit Scale: 25.950 Contrastive_loss: 0.20224 (0.21878) Loss: 0.20224 (0.21878) 2025-03-19,10:26:53 | INFO | Train Epoch: 10 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.212, 147.836/s, 147.836/s/gpu LR: 0.000144 Logit Scale: 25.935 Contrastive_loss: 0.34197 (0.21933) Loss: 0.34197 (0.21933) 2025-03-19,10:27:15 | INFO | Train Epoch: 10 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.250/s, 148.250/s/gpu LR: 0.000144 Logit Scale: 25.945 Contrastive_loss: 0.11283 (0.21886) Loss: 0.11283 (0.21886) 2025-03-19,10:27:36 | INFO | Train Epoch: 10 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 149.058/s, 149.058/s/gpu LR: 0.000144 Logit Scale: 25.946 Contrastive_loss: 0.19751 (0.21877) Loss: 0.19751 (0.21877) 2025-03-19,10:27:58 | INFO | Train Epoch: 10 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 151.803/s, 151.803/s/gpu LR: 0.000144 Logit Scale: 25.932 Contrastive_loss: 0.10630 (0.21827) Loss: 0.10630 (0.21827) 2025-03-19,10:28:19 | INFO | Train Epoch: 10 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 149.181/s, 149.181/s/gpu LR: 0.000144 Logit Scale: 25.946 Contrastive_loss: 0.15169 (0.21798) Loss: 0.15169 (0.21798) 2025-03-19,10:28:41 | INFO | Train Epoch: 10 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 150.989/s, 150.989/s/gpu LR: 0.000144 Logit Scale: 25.970 Contrastive_loss: 0.12455 (0.21757) Loss: 0.12455 (0.21757) 2025-03-19,10:29:02 | INFO | Train Epoch: 10 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.213, 149.931/s, 149.931/s/gpu LR: 0.000144 Logit Scale: 25.929 Contrastive_loss: 0.19868 (0.21749) Loss: 0.19868 (0.21749) 2025-03-19,10:29:24 | INFO | Train Epoch: 10 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 148.979/s, 148.979/s/gpu LR: 0.000144 Logit Scale: 25.966 Contrastive_loss: 0.11184 (0.21703) Loss: 0.11184 (0.21703) 2025-03-19,10:29:45 | INFO | Train Epoch: 10 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 148.968/s, 148.968/s/gpu LR: 0.000144 Logit Scale: 25.997 Contrastive_loss: 0.50553 (0.21828) Loss: 0.50553 (0.21828) 2025-03-19,10:30:06 | INFO | Train Epoch: 10 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 151.052/s, 151.052/s/gpu LR: 0.000144 Logit Scale: 25.988 Contrastive_loss: 0.18622 (0.21814) Loss: 0.18622 (0.21814) 2025-03-19,10:30:28 | INFO | Train Epoch: 10 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.093/s, 149.093/s/gpu LR: 0.000143 Logit Scale: 26.001 Contrastive_loss: 0.38773 (0.21886) Loss: 0.38773 (0.21886) 2025-03-19,10:30:49 | INFO | Train Epoch: 10 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 149.615/s, 149.615/s/gpu LR: 0.000143 Logit Scale: 25.982 Contrastive_loss: 0.25917 (0.21903) Loss: 0.25917 (0.21903) 2025-03-19,10:31:11 | INFO | Train Epoch: 10 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 149.592/s, 149.592/s/gpu LR: 0.000143 Logit Scale: 26.017 Contrastive_loss: 0.43754 (0.21996) Loss: 0.43754 (0.21996) 2025-03-19,10:31:32 | INFO | Train Epoch: 10 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 148.780/s, 148.780/s/gpu LR: 0.000143 Logit Scale: 26.024 Contrastive_loss: 0.22742 (0.21999) Loss: 0.22742 (0.21999) 2025-03-19,10:31:54 | INFO | Train Epoch: 10 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.174/s, 149.174/s/gpu LR: 0.000143 Logit Scale: 26.024 Contrastive_loss: 0.60265 (0.22160) Loss: 0.60265 (0.22160) 2025-03-19,10:32:15 | INFO | Train Epoch: 10 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 148.691/s, 148.691/s/gpu LR: 0.000143 Logit Scale: 26.047 Contrastive_loss: 0.12134 (0.22118) Loss: 0.12134 (0.22118) 2025-03-19,10:32:37 | INFO | Train Epoch: 10 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.215, 149.785/s, 149.785/s/gpu LR: 0.000143 Logit Scale: 26.026 Contrastive_loss: 0.10822 (0.22071) Loss: 0.10822 (0.22071) 2025-03-19,10:32:45 | INFO | Train Epoch: 10 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.215, 149.048/s, 149.048/s/gpu LR: 0.000143 Logit Scale: 25.999 Contrastive_loss: 0.17242 (0.22051) Loss: 0.17242 (0.22051) 2025-03-19,10:32:45 | INFO | Eval Epoch: 11 [32 / 7443] Clip Loss: 3.341675 2025-03-19,10:32:51 | INFO | Eval Epoch: 11 [3232 / 7443] Clip Loss: 0.938173 2025-03-19,10:32:56 | INFO | Eval Epoch: 11 [6432 / 7443] Clip Loss: 0.707187 2025-03-19,10:32:59 | INFO | Eval Epoch: 11 image_to_text_mean_rank: 105.5874 image_to_text_median_rank: 7.0000 image_to_text_R@1: 0.1252 image_to_text_R@5: 0.4234 image_to_text_R@10: 0.5979 text_to_image_mean_rank: 65.3461 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1240 text_to_image_R@5: 0.4204 text_to_image_R@10: 0.5933 clip_val_loss: 0.6634 epoch: 11.0000 num_samples: 7443.0000 2025-03-19,10:33:32 | INFO | Start epoch 11 2025-03-19,10:33:33 | INFO | Train Epoch: 11 [ 32/766009 (0%)] Data (t): 0.170 Batch (t): 0.375, 85.2411/s, 85.2411/s/gpu LR: 0.000143 Logit Scale: 26.001 Contrastive_loss: 0.15698 (0.15698) Loss: 0.15698 (0.15698) 2025-03-19,10:33:54 | INFO | Train Epoch: 11 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.216, 151.196/s, 151.196/s/gpu LR: 0.000143 Logit Scale: 26.069 Contrastive_loss: 0.25511 (0.20605) Loss: 0.25511 (0.20605) 2025-03-19,10:34:16 | INFO | Train Epoch: 11 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 151.326/s, 151.326/s/gpu LR: 0.000143 Logit Scale: 26.099 Contrastive_loss: 0.13744 (0.18318) Loss: 0.13744 (0.18318) 2025-03-19,10:34:37 | INFO | Train Epoch: 11 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.213, 149.666/s, 149.666/s/gpu LR: 0.000143 Logit Scale: 26.114 Contrastive_loss: 0.25617 (0.20143) Loss: 0.25617 (0.20143) 2025-03-19,10:34:59 | INFO | Train Epoch: 11 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 148.170/s, 148.170/s/gpu LR: 0.000143 Logit Scale: 26.109 Contrastive_loss: 0.37811 (0.23676) Loss: 0.37811 (0.23676) 2025-03-19,10:35:20 | INFO | Train Epoch: 11 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 150.154/s, 150.154/s/gpu LR: 0.000143 Logit Scale: 26.094 Contrastive_loss: 0.19078 (0.22910) Loss: 0.19078 (0.22910) 2025-03-19,10:35:41 | INFO | Train Epoch: 11 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 151.387/s, 151.387/s/gpu LR: 0.000143 Logit Scale: 26.101 Contrastive_loss: 0.39606 (0.25295) Loss: 0.39606 (0.25295) 2025-03-19,10:36:03 | INFO | Train Epoch: 11 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 147.356/s, 147.356/s/gpu LR: 0.000143 Logit Scale: 26.117 Contrastive_loss: 0.061496 (0.22902) Loss: 0.061496 (0.22902) 2025-03-19,10:36:25 | INFO | Train Epoch: 11 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 146.293/s, 146.293/s/gpu LR: 0.000143 Logit Scale: 26.101 Contrastive_loss: 0.14926 (0.22016) Loss: 0.14926 (0.22016) 2025-03-19,10:36:46 | INFO | Train Epoch: 11 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 149.367/s, 149.367/s/gpu LR: 0.000143 Logit Scale: 26.105 Contrastive_loss: 0.15072 (0.21321) Loss: 0.15072 (0.21321) 2025-03-19,10:37:07 | INFO | Train Epoch: 11 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 151.098/s, 151.098/s/gpu LR: 0.000143 Logit Scale: 26.118 Contrastive_loss: 0.20872 (0.21281) Loss: 0.20872 (0.21281) 2025-03-19,10:37:29 | INFO | Train Epoch: 11 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 147.823/s, 147.823/s/gpu LR: 0.000143 Logit Scale: 26.150 Contrastive_loss: 0.15835 (0.20827) Loss: 0.15835 (0.20827) 2025-03-19,10:37:51 | INFO | Train Epoch: 11 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 151.435/s, 151.435/s/gpu LR: 0.000143 Logit Scale: 26.110 Contrastive_loss: 0.12614 (0.20195) Loss: 0.12614 (0.20195) 2025-03-19,10:38:12 | INFO | Train Epoch: 11 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.212, 151.234/s, 151.234/s/gpu LR: 0.000143 Logit Scale: 26.124 Contrastive_loss: 0.092493 (0.19413) Loss: 0.092493 (0.19413) 2025-03-19,10:38:33 | INFO | Train Epoch: 11 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.212, 149.470/s, 149.470/s/gpu LR: 0.000143 Logit Scale: 26.126 Contrastive_loss: 0.22930 (0.19648) Loss: 0.22930 (0.19648) 2025-03-19,10:38:55 | INFO | Train Epoch: 11 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 137.983/s, 137.983/s/gpu LR: 0.000143 Logit Scale: 26.171 Contrastive_loss: 0.053029 (0.18751) Loss: 0.053029 (0.18751) 2025-03-19,10:39:16 | INFO | Train Epoch: 11 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 149.495/s, 149.495/s/gpu LR: 0.000143 Logit Scale: 26.203 Contrastive_loss: 0.10944 (0.18292) Loss: 0.10944 (0.18292) 2025-03-19,10:39:38 | INFO | Train Epoch: 11 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.591/s, 149.591/s/gpu LR: 0.000143 Logit Scale: 26.192 Contrastive_loss: 0.13932 (0.18050) Loss: 0.13932 (0.18050) 2025-03-19,10:39:59 | INFO | Train Epoch: 11 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 148.945/s, 148.945/s/gpu LR: 0.000143 Logit Scale: 26.189 Contrastive_loss: 0.11873 (0.17725) Loss: 0.11873 (0.17725) 2025-03-19,10:40:21 | INFO | Train Epoch: 11 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 151.332/s, 151.332/s/gpu LR: 0.000142 Logit Scale: 26.192 Contrastive_loss: 0.094711 (0.17312) Loss: 0.094711 (0.17312) 2025-03-19,10:40:43 | INFO | Train Epoch: 11 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.222, 143.362/s, 143.362/s/gpu LR: 0.000142 Logit Scale: 26.195 Contrastive_loss: 0.64577 (0.19563) Loss: 0.64577 (0.19563) 2025-03-19,10:41:05 | INFO | Train Epoch: 11 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.221, 143.792/s, 143.792/s/gpu LR: 0.000142 Logit Scale: 26.216 Contrastive_loss: 0.24313 (0.19779) Loss: 0.24313 (0.19779) 2025-03-19,10:41:27 | INFO | Train Epoch: 11 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.221, 145.151/s, 145.151/s/gpu LR: 0.000142 Logit Scale: 26.254 Contrastive_loss: 0.18997 (0.19745) Loss: 0.18997 (0.19745) 2025-03-19,10:41:49 | INFO | Train Epoch: 11 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 146.574/s, 146.574/s/gpu LR: 0.000142 Logit Scale: 26.272 Contrastive_loss: 0.38232 (0.20515) Loss: 0.38232 (0.20515) 2025-03-19,10:42:11 | INFO | Train Epoch: 11 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 149.612/s, 149.612/s/gpu LR: 0.000142 Logit Scale: 26.273 Contrastive_loss: 0.34971 (0.21093) Loss: 0.34971 (0.21093) 2025-03-19,10:42:32 | INFO | Train Epoch: 11 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 146.064/s, 146.064/s/gpu LR: 0.000142 Logit Scale: 26.319 Contrastive_loss: 0.52199 (0.22289) Loss: 0.52199 (0.22289) 2025-03-19,10:42:54 | INFO | Train Epoch: 11 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.218, 146.641/s, 146.641/s/gpu LR: 0.000142 Logit Scale: 26.249 Contrastive_loss: 0.23910 (0.22350) Loss: 0.23910 (0.22350) 2025-03-19,10:43:16 | INFO | Train Epoch: 11 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 146.835/s, 146.835/s/gpu LR: 0.000142 Logit Scale: 26.249 Contrastive_loss: 0.26175 (0.22486) Loss: 0.26175 (0.22486) 2025-03-19,10:43:37 | INFO | Train Epoch: 11 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 146.716/s, 146.716/s/gpu LR: 0.000142 Logit Scale: 26.250 Contrastive_loss: 0.17156 (0.22302) Loss: 0.17156 (0.22302) 2025-03-19,10:43:59 | INFO | Train Epoch: 11 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.746/s, 148.746/s/gpu LR: 0.000142 Logit Scale: 26.214 Contrastive_loss: 0.029915 (0.21659) Loss: 0.029915 (0.21659) 2025-03-19,10:44:21 | INFO | Train Epoch: 11 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 148.664/s, 148.664/s/gpu LR: 0.000142 Logit Scale: 26.210 Contrastive_loss: 0.081453 (0.21223) Loss: 0.081453 (0.21223) 2025-03-19,10:44:42 | INFO | Train Epoch: 11 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 148.748/s, 148.748/s/gpu LR: 0.000142 Logit Scale: 26.185 Contrastive_loss: 0.29821 (0.21491) Loss: 0.29821 (0.21491) 2025-03-19,10:45:04 | INFO | Train Epoch: 11 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 148.893/s, 148.893/s/gpu LR: 0.000142 Logit Scale: 26.206 Contrastive_loss: 0.30983 (0.21779) Loss: 0.30983 (0.21779) 2025-03-19,10:45:26 | INFO | Train Epoch: 11 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 148.827/s, 148.827/s/gpu LR: 0.000142 Logit Scale: 26.233 Contrastive_loss: 0.28279 (0.21970) Loss: 0.28279 (0.21970) 2025-03-19,10:45:47 | INFO | Train Epoch: 11 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 149.113/s, 149.113/s/gpu LR: 0.000142 Logit Scale: 26.197 Contrastive_loss: 0.026901 (0.21419) Loss: 0.026901 (0.21419) 2025-03-19,10:46:08 | INFO | Train Epoch: 11 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.985/s, 148.985/s/gpu LR: 0.000142 Logit Scale: 26.172 Contrastive_loss: 0.25405 (0.21530) Loss: 0.25405 (0.21530) 2025-03-19,10:46:30 | INFO | Train Epoch: 11 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.756/s, 148.756/s/gpu LR: 0.000142 Logit Scale: 26.214 Contrastive_loss: 0.10366 (0.21228) Loss: 0.10366 (0.21228) 2025-03-19,10:46:52 | INFO | Train Epoch: 11 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.921/s, 148.921/s/gpu LR: 0.000142 Logit Scale: 26.253 Contrastive_loss: 0.24518 (0.21315) Loss: 0.24518 (0.21315) 2025-03-19,10:47:13 | INFO | Train Epoch: 11 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.219, 143.387/s, 143.387/s/gpu LR: 0.000142 Logit Scale: 26.226 Contrastive_loss: 0.11923 (0.21074) Loss: 0.11923 (0.21074) 2025-03-19,10:47:35 | INFO | Train Epoch: 11 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 148.150/s, 148.150/s/gpu LR: 0.000142 Logit Scale: 26.233 Contrastive_loss: 0.21309 (0.21080) Loss: 0.21309 (0.21080) 2025-03-19,10:47:56 | INFO | Train Epoch: 11 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 150.904/s, 150.904/s/gpu LR: 0.000142 Logit Scale: 26.245 Contrastive_loss: 0.13125 (0.20886) Loss: 0.13125 (0.20886) 2025-03-19,10:48:18 | INFO | Train Epoch: 11 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 150.457/s, 150.457/s/gpu LR: 0.000142 Logit Scale: 26.206 Contrastive_loss: 0.037834 (0.20479) Loss: 0.037834 (0.20479) 2025-03-19,10:48:40 | INFO | Train Epoch: 11 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 134.690/s, 134.690/s/gpu LR: 0.000142 Logit Scale: 26.234 Contrastive_loss: 0.11555 (0.20271) Loss: 0.11555 (0.20271) 2025-03-19,10:49:01 | INFO | Train Epoch: 11 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 152.335/s, 152.335/s/gpu LR: 0.000141 Logit Scale: 26.199 Contrastive_loss: 0.32319 (0.20545) Loss: 0.32319 (0.20545) 2025-03-19,10:49:22 | INFO | Train Epoch: 11 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.212, 151.279/s, 151.279/s/gpu LR: 0.000141 Logit Scale: 26.198 Contrastive_loss: 0.18259 (0.20494) Loss: 0.18259 (0.20494) 2025-03-19,10:49:44 | INFO | Train Epoch: 11 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 147.481/s, 147.481/s/gpu LR: 0.000141 Logit Scale: 26.200 Contrastive_loss: 0.29538 (0.20691) Loss: 0.29538 (0.20691) 2025-03-19,10:50:06 | INFO | Train Epoch: 11 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.219, 150.020/s, 150.020/s/gpu LR: 0.000141 Logit Scale: 26.202 Contrastive_loss: 0.094226 (0.20451) Loss: 0.094226 (0.20451) 2025-03-19,10:50:27 | INFO | Train Epoch: 11 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 147.840/s, 147.840/s/gpu LR: 0.000141 Logit Scale: 26.237 Contrastive_loss: 0.085822 (0.20204) Loss: 0.085822 (0.20204) 2025-03-19,10:50:49 | INFO | Train Epoch: 11 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.219, 145.824/s, 145.824/s/gpu LR: 0.000141 Logit Scale: 26.200 Contrastive_loss: 0.46762 (0.20746) Loss: 0.46762 (0.20746) 2025-03-19,10:51:11 | INFO | Train Epoch: 11 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 143.593/s, 143.593/s/gpu LR: 0.000141 Logit Scale: 26.169 Contrastive_loss: 0.13203 (0.20595) Loss: 0.13203 (0.20595) 2025-03-19,10:51:33 | INFO | Train Epoch: 11 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.221, 146.048/s, 146.048/s/gpu LR: 0.000141 Logit Scale: 26.167 Contrastive_loss: 0.14571 (0.20477) Loss: 0.14571 (0.20477) 2025-03-19,10:51:55 | INFO | Train Epoch: 11 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.221, 145.552/s, 145.552/s/gpu LR: 0.000141 Logit Scale: 26.200 Contrastive_loss: 0.41522 (0.20882) Loss: 0.41522 (0.20882) 2025-03-19,10:52:17 | INFO | Train Epoch: 11 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.218, 145.159/s, 145.159/s/gpu LR: 0.000141 Logit Scale: 26.187 Contrastive_loss: 0.19725 (0.20860) Loss: 0.19725 (0.20860) 2025-03-19,10:52:38 | INFO | Train Epoch: 11 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.217, 148.460/s, 148.460/s/gpu LR: 0.000141 Logit Scale: 26.143 Contrastive_loss: 0.071256 (0.20605) Loss: 0.071256 (0.20605) 2025-03-19,10:53:00 | INFO | Train Epoch: 11 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 147.674/s, 147.674/s/gpu LR: 0.000141 Logit Scale: 26.166 Contrastive_loss: 0.26737 (0.20717) Loss: 0.26737 (0.20717) 2025-03-19,10:53:22 | INFO | Train Epoch: 11 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 148.771/s, 148.771/s/gpu LR: 0.000141 Logit Scale: 26.156 Contrastive_loss: 0.40633 (0.21073) Loss: 0.40633 (0.21073) 2025-03-19,10:53:43 | INFO | Train Epoch: 11 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 148.811/s, 148.811/s/gpu LR: 0.000141 Logit Scale: 26.173 Contrastive_loss: 0.077414 (0.20839) Loss: 0.077414 (0.20839) 2025-03-19,10:54:05 | INFO | Train Epoch: 11 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 150.103/s, 150.103/s/gpu LR: 0.000141 Logit Scale: 26.210 Contrastive_loss: 0.25663 (0.20922) Loss: 0.25663 (0.20922) 2025-03-19,10:54:26 | INFO | Train Epoch: 11 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 148.154/s, 148.154/s/gpu LR: 0.000141 Logit Scale: 26.186 Contrastive_loss: 0.10426 (0.20744) Loss: 0.10426 (0.20744) 2025-03-19,10:54:48 | INFO | Train Epoch: 11 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 151.940/s, 151.940/s/gpu LR: 0.000141 Logit Scale: 26.199 Contrastive_loss: 0.12378 (0.20605) Loss: 0.12378 (0.20605) 2025-03-19,10:55:09 | INFO | Train Epoch: 11 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 149.754/s, 149.754/s/gpu LR: 0.000141 Logit Scale: 26.175 Contrastive_loss: 0.16518 (0.20538) Loss: 0.16518 (0.20538) 2025-03-19,10:55:31 | INFO | Train Epoch: 11 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 149.356/s, 149.356/s/gpu LR: 0.000141 Logit Scale: 26.176 Contrastive_loss: 0.54872 (0.21091) Loss: 0.54872 (0.21091) 2025-03-19,10:55:52 | INFO | Train Epoch: 11 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 150.397/s, 150.397/s/gpu LR: 0.000141 Logit Scale: 26.160 Contrastive_loss: 0.22798 (0.21118) Loss: 0.22798 (0.21118) 2025-03-19,10:56:14 | INFO | Train Epoch: 11 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.217, 148.695/s, 148.695/s/gpu LR: 0.000141 Logit Scale: 26.164 Contrastive_loss: 0.26704 (0.21206) Loss: 0.26704 (0.21206) 2025-03-19,10:56:35 | INFO | Train Epoch: 11 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.475/s, 149.475/s/gpu LR: 0.000141 Logit Scale: 26.178 Contrastive_loss: 0.21307 (0.21207) Loss: 0.21307 (0.21207) 2025-03-19,10:56:57 | INFO | Train Epoch: 11 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 148.973/s, 148.973/s/gpu LR: 0.000141 Logit Scale: 26.178 Contrastive_loss: 0.30621 (0.21350) Loss: 0.30621 (0.21350) 2025-03-19,10:57:18 | INFO | Train Epoch: 11 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 150.799/s, 150.799/s/gpu LR: 0.000141 Logit Scale: 26.215 Contrastive_loss: 0.25112 (0.21406) Loss: 0.25112 (0.21406) 2025-03-19,10:57:40 | INFO | Train Epoch: 11 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 146.415/s, 146.415/s/gpu LR: 0.000141 Logit Scale: 26.252 Contrastive_loss: 0.31182 (0.21550) Loss: 0.31182 (0.21550) 2025-03-19,10:58:02 | INFO | Train Epoch: 11 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 146.454/s, 146.454/s/gpu LR: 0.000140 Logit Scale: 26.239 Contrastive_loss: 0.43499 (0.21868) Loss: 0.43499 (0.21868) 2025-03-19,10:58:24 | INFO | Train Epoch: 11 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.218, 147.639/s, 147.639/s/gpu LR: 0.000140 Logit Scale: 26.257 Contrastive_loss: 0.19492 (0.21834) Loss: 0.19492 (0.21834) 2025-03-19,10:58:45 | INFO | Train Epoch: 11 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.218, 145.502/s, 145.502/s/gpu LR: 0.000140 Logit Scale: 26.267 Contrastive_loss: 0.13246 (0.21713) Loss: 0.13246 (0.21713) 2025-03-19,10:59:07 | INFO | Train Epoch: 11 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 146.302/s, 146.302/s/gpu LR: 0.000140 Logit Scale: 26.222 Contrastive_loss: 0.19638 (0.21684) Loss: 0.19638 (0.21684) 2025-03-19,10:59:28 | INFO | Train Epoch: 11 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 151.955/s, 151.955/s/gpu LR: 0.000140 Logit Scale: 26.230 Contrastive_loss: 0.061092 (0.21471) Loss: 0.061092 (0.21471) 2025-03-19,10:59:50 | INFO | Train Epoch: 11 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 148.433/s, 148.433/s/gpu LR: 0.000140 Logit Scale: 26.213 Contrastive_loss: 0.15105 (0.21385) Loss: 0.15105 (0.21385) 2025-03-19,11:00:12 | INFO | Train Epoch: 11 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.245/s, 148.245/s/gpu LR: 0.000140 Logit Scale: 26.220 Contrastive_loss: 0.14562 (0.21294) Loss: 0.14562 (0.21294) 2025-03-19,11:00:33 | INFO | Train Epoch: 11 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 148.778/s, 148.778/s/gpu LR: 0.000140 Logit Scale: 26.205 Contrastive_loss: 0.22758 (0.21313) Loss: 0.22758 (0.21313) 2025-03-19,11:00:54 | INFO | Train Epoch: 11 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 148.799/s, 148.799/s/gpu LR: 0.000140 Logit Scale: 26.198 Contrastive_loss: 0.16116 (0.21246) Loss: 0.16116 (0.21246) 2025-03-19,11:01:16 | INFO | Train Epoch: 11 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 150.890/s, 150.890/s/gpu LR: 0.000140 Logit Scale: 26.177 Contrastive_loss: 0.14015 (0.21153) Loss: 0.14015 (0.21153) 2025-03-19,11:01:38 | INFO | Train Epoch: 11 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 147.491/s, 147.491/s/gpu LR: 0.000140 Logit Scale: 26.157 Contrastive_loss: 0.079923 (0.20986) Loss: 0.079923 (0.20986) 2025-03-19,11:01:59 | INFO | Train Epoch: 11 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 147.233/s, 147.233/s/gpu LR: 0.000140 Logit Scale: 26.173 Contrastive_loss: 0.12760 (0.20883) Loss: 0.12760 (0.20883) 2025-03-19,11:02:21 | INFO | Train Epoch: 11 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 149.478/s, 149.478/s/gpu LR: 0.000140 Logit Scale: 26.161 Contrastive_loss: 0.26565 (0.20954) Loss: 0.26565 (0.20954) 2025-03-19,11:02:43 | INFO | Train Epoch: 11 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 147.123/s, 147.123/s/gpu LR: 0.000140 Logit Scale: 26.167 Contrastive_loss: 0.24417 (0.20996) Loss: 0.24417 (0.20996) 2025-03-19,11:03:04 | INFO | Train Epoch: 11 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 149.075/s, 149.075/s/gpu LR: 0.000140 Logit Scale: 26.167 Contrastive_loss: 0.14123 (0.20913) Loss: 0.14123 (0.20913) 2025-03-19,11:03:26 | INFO | Train Epoch: 11 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.915/s, 148.915/s/gpu LR: 0.000140 Logit Scale: 26.211 Contrastive_loss: 0.13271 (0.20822) Loss: 0.13271 (0.20822) 2025-03-19,11:03:47 | INFO | Train Epoch: 11 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 149.217/s, 149.217/s/gpu LR: 0.000140 Logit Scale: 26.186 Contrastive_loss: 0.19833 (0.20810) Loss: 0.19833 (0.20810) 2025-03-19,11:04:09 | INFO | Train Epoch: 11 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 149.236/s, 149.236/s/gpu LR: 0.000140 Logit Scale: 26.119 Contrastive_loss: 0.28032 (0.20894) Loss: 0.28032 (0.20894) 2025-03-19,11:04:30 | INFO | Train Epoch: 11 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.189/s, 149.189/s/gpu LR: 0.000140 Logit Scale: 26.114 Contrastive_loss: 0.13933 (0.20814) Loss: 0.13933 (0.20814) 2025-03-19,11:04:52 | INFO | Train Epoch: 11 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.494/s, 149.494/s/gpu LR: 0.000140 Logit Scale: 26.147 Contrastive_loss: 0.41542 (0.21050) Loss: 0.41542 (0.21050) 2025-03-19,11:05:13 | INFO | Train Epoch: 11 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 147.050/s, 147.050/s/gpu LR: 0.000140 Logit Scale: 26.128 Contrastive_loss: 0.24123 (0.21084) Loss: 0.24123 (0.21084) 2025-03-19,11:05:35 | INFO | Train Epoch: 11 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 148.310/s, 148.310/s/gpu LR: 0.000140 Logit Scale: 26.123 Contrastive_loss: 0.21413 (0.21088) Loss: 0.21413 (0.21088) 2025-03-19,11:05:57 | INFO | Train Epoch: 11 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.952/s, 148.952/s/gpu LR: 0.000140 Logit Scale: 26.133 Contrastive_loss: 0.10624 (0.20973) Loss: 0.10624 (0.20973) 2025-03-19,11:06:18 | INFO | Train Epoch: 11 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.866/s, 149.866/s/gpu LR: 0.000140 Logit Scale: 26.169 Contrastive_loss: 0.24725 (0.21014) Loss: 0.24725 (0.21014) 2025-03-19,11:06:40 | INFO | Train Epoch: 11 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.677/s, 148.677/s/gpu LR: 0.000140 Logit Scale: 26.144 Contrastive_loss: 0.12325 (0.20920) Loss: 0.12325 (0.20920) 2025-03-19,11:07:01 | INFO | Train Epoch: 11 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.680/s, 149.680/s/gpu LR: 0.000139 Logit Scale: 26.126 Contrastive_loss: 0.25294 (0.20967) Loss: 0.25294 (0.20967) 2025-03-19,11:07:23 | INFO | Train Epoch: 11 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.575/s, 149.575/s/gpu LR: 0.000139 Logit Scale: 26.124 Contrastive_loss: 0.20646 (0.20964) Loss: 0.20646 (0.20964) 2025-03-19,11:07:44 | INFO | Train Epoch: 11 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 147.817/s, 147.817/s/gpu LR: 0.000139 Logit Scale: 26.114 Contrastive_loss: 0.20737 (0.20961) Loss: 0.20737 (0.20961) 2025-03-19,11:08:06 | INFO | Train Epoch: 11 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 147.817/s, 147.817/s/gpu LR: 0.000139 Logit Scale: 26.079 Contrastive_loss: 0.22817 (0.20980) Loss: 0.22817 (0.20980) 2025-03-19,11:08:27 | INFO | Train Epoch: 11 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 149.591/s, 149.591/s/gpu LR: 0.000139 Logit Scale: 26.134 Contrastive_loss: 0.20019 (0.20971) Loss: 0.20019 (0.20971) 2025-03-19,11:08:49 | INFO | Train Epoch: 11 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 148.836/s, 148.836/s/gpu LR: 0.000139 Logit Scale: 26.139 Contrastive_loss: 0.056351 (0.20816) Loss: 0.056351 (0.20816) 2025-03-19,11:09:10 | INFO | Train Epoch: 11 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.212, 148.218/s, 148.218/s/gpu LR: 0.000139 Logit Scale: 26.158 Contrastive_loss: 0.34858 (0.20956) Loss: 0.34858 (0.20956) 2025-03-19,11:09:32 | INFO | Train Epoch: 11 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.215, 149.756/s, 149.756/s/gpu LR: 0.000139 Logit Scale: 26.172 Contrastive_loss: 0.25759 (0.21004) Loss: 0.25759 (0.21004) 2025-03-19,11:09:53 | INFO | Train Epoch: 11 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.215, 146.870/s, 146.870/s/gpu LR: 0.000139 Logit Scale: 26.154 Contrastive_loss: 0.051195 (0.20848) Loss: 0.051195 (0.20848) 2025-03-19,11:10:15 | INFO | Train Epoch: 11 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 152.107/s, 152.107/s/gpu LR: 0.000139 Logit Scale: 26.197 Contrastive_loss: 0.079637 (0.20723) Loss: 0.079637 (0.20723) 2025-03-19,11:10:36 | INFO | Train Epoch: 11 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.212, 151.156/s, 151.156/s/gpu LR: 0.000139 Logit Scale: 26.173 Contrastive_loss: 0.27156 (0.20785) Loss: 0.27156 (0.20785) 2025-03-19,11:10:57 | INFO | Train Epoch: 11 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.213, 149.788/s, 149.788/s/gpu LR: 0.000139 Logit Scale: 26.152 Contrastive_loss: 0.19449 (0.20772) Loss: 0.19449 (0.20772) 2025-03-19,11:11:19 | INFO | Train Epoch: 11 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 149.008/s, 149.008/s/gpu LR: 0.000139 Logit Scale: 26.151 Contrastive_loss: 0.088941 (0.20660) Loss: 0.088941 (0.20660) 2025-03-19,11:11:40 | INFO | Train Epoch: 11 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 147.940/s, 147.940/s/gpu LR: 0.000139 Logit Scale: 26.156 Contrastive_loss: 0.077576 (0.20539) Loss: 0.077576 (0.20539) 2025-03-19,11:12:02 | INFO | Train Epoch: 11 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.220, 144.334/s, 144.334/s/gpu LR: 0.000139 Logit Scale: 26.159 Contrastive_loss: 0.13840 (0.20477) Loss: 0.13840 (0.20477) 2025-03-19,11:12:24 | INFO | Train Epoch: 11 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.219, 147.875/s, 147.875/s/gpu LR: 0.000139 Logit Scale: 26.152 Contrastive_loss: 0.27325 (0.20540) Loss: 0.27325 (0.20540) 2025-03-19,11:12:46 | INFO | Train Epoch: 11 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.218, 147.140/s, 147.140/s/gpu LR: 0.000139 Logit Scale: 26.183 Contrastive_loss: 0.27208 (0.20601) Loss: 0.27208 (0.20601) 2025-03-19,11:13:07 | INFO | Train Epoch: 11 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 146.824/s, 146.824/s/gpu LR: 0.000139 Logit Scale: 26.157 Contrastive_loss: 0.13878 (0.20540) Loss: 0.13878 (0.20540) 2025-03-19,11:13:29 | INFO | Train Epoch: 11 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 150.608/s, 150.608/s/gpu LR: 0.000139 Logit Scale: 26.203 Contrastive_loss: 0.29679 (0.20622) Loss: 0.29679 (0.20622) 2025-03-19,11:13:51 | INFO | Train Epoch: 11 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 148.910/s, 148.910/s/gpu LR: 0.000139 Logit Scale: 26.143 Contrastive_loss: 0.33052 (0.20732) Loss: 0.33052 (0.20732) 2025-03-19,11:14:12 | INFO | Train Epoch: 11 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 148.391/s, 148.391/s/gpu LR: 0.000139 Logit Scale: 26.105 Contrastive_loss: 0.055197 (0.20598) Loss: 0.055197 (0.20598) 2025-03-19,11:14:34 | INFO | Train Epoch: 11 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.080/s, 148.080/s/gpu LR: 0.000139 Logit Scale: 26.077 Contrastive_loss: 0.44012 (0.20802) Loss: 0.44012 (0.20802) 2025-03-19,11:14:55 | INFO | Train Epoch: 11 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 149.389/s, 149.389/s/gpu LR: 0.000139 Logit Scale: 26.068 Contrastive_loss: 0.28358 (0.20867) Loss: 0.28358 (0.20867) 2025-03-19,11:15:17 | INFO | Train Epoch: 11 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 150.339/s, 150.339/s/gpu LR: 0.000139 Logit Scale: 26.073 Contrastive_loss: 0.27016 (0.20920) Loss: 0.27016 (0.20920) 2025-03-19,11:15:38 | INFO | Train Epoch: 11 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 146.194/s, 146.194/s/gpu LR: 0.000138 Logit Scale: 26.081 Contrastive_loss: 0.39050 (0.21073) Loss: 0.39050 (0.21073) 2025-03-19,11:16:00 | INFO | Train Epoch: 11 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.220, 146.660/s, 146.660/s/gpu LR: 0.000138 Logit Scale: 26.088 Contrastive_loss: 0.27028 (0.21123) Loss: 0.27028 (0.21123) 2025-03-19,11:16:22 | INFO | Train Epoch: 11 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.219, 147.054/s, 147.054/s/gpu LR: 0.000138 Logit Scale: 26.126 Contrastive_loss: 0.38905 (0.21271) Loss: 0.38905 (0.21271) 2025-03-19,11:16:44 | INFO | Train Epoch: 11 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.222, 144.876/s, 144.876/s/gpu LR: 0.000138 Logit Scale: 26.154 Contrastive_loss: 0.23300 (0.21288) Loss: 0.23300 (0.21288) 2025-03-19,11:17:06 | INFO | Train Epoch: 11 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.219, 147.327/s, 147.327/s/gpu LR: 0.000138 Logit Scale: 26.160 Contrastive_loss: 0.12652 (0.21217) Loss: 0.12652 (0.21217) 2025-03-19,11:17:27 | INFO | Train Epoch: 11 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.871/s, 149.871/s/gpu LR: 0.000138 Logit Scale: 26.155 Contrastive_loss: 0.16254 (0.21177) Loss: 0.16254 (0.21177) 2025-03-19,11:17:49 | INFO | Train Epoch: 11 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 147.809/s, 147.809/s/gpu LR: 0.000138 Logit Scale: 26.136 Contrastive_loss: 0.26755 (0.21222) Loss: 0.26755 (0.21222) 2025-03-19,11:18:11 | INFO | Train Epoch: 11 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.217, 148.776/s, 148.776/s/gpu LR: 0.000138 Logit Scale: 26.112 Contrastive_loss: 0.14317 (0.21167) Loss: 0.14317 (0.21167) 2025-03-19,11:18:32 | INFO | Train Epoch: 11 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 151.400/s, 151.400/s/gpu LR: 0.000138 Logit Scale: 26.131 Contrastive_loss: 0.16397 (0.21129) Loss: 0.16397 (0.21129) 2025-03-19,11:18:53 | INFO | Train Epoch: 11 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.213, 150.982/s, 150.982/s/gpu LR: 0.000138 Logit Scale: 26.107 Contrastive_loss: 0.30467 (0.21203) Loss: 0.30467 (0.21203) 2025-03-19,11:19:15 | INFO | Train Epoch: 11 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.212, 150.482/s, 150.482/s/gpu LR: 0.000138 Logit Scale: 26.112 Contrastive_loss: 0.26826 (0.21246) Loss: 0.26826 (0.21246) 2025-03-19,11:19:36 | INFO | Train Epoch: 11 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 147.996/s, 147.996/s/gpu LR: 0.000138 Logit Scale: 26.099 Contrastive_loss: 0.12209 (0.21176) Loss: 0.12209 (0.21176) 2025-03-19,11:19:58 | INFO | Train Epoch: 11 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 148.320/s, 148.320/s/gpu LR: 0.000138 Logit Scale: 26.079 Contrastive_loss: 0.16595 (0.21141) Loss: 0.16595 (0.21141) 2025-03-19,11:20:19 | INFO | Train Epoch: 11 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 149.375/s, 149.375/s/gpu LR: 0.000138 Logit Scale: 26.035 Contrastive_loss: 0.15977 (0.21102) Loss: 0.15977 (0.21102) 2025-03-19,11:20:41 | INFO | Train Epoch: 11 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 149.852/s, 149.852/s/gpu LR: 0.000138 Logit Scale: 26.021 Contrastive_loss: 0.25276 (0.21133) Loss: 0.25276 (0.21133) 2025-03-19,11:21:02 | INFO | Train Epoch: 11 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 144.306/s, 144.306/s/gpu LR: 0.000138 Logit Scale: 26.040 Contrastive_loss: 0.19817 (0.21123) Loss: 0.19817 (0.21123) 2025-03-19,11:21:24 | INFO | Train Epoch: 11 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 147.717/s, 147.717/s/gpu LR: 0.000138 Logit Scale: 26.025 Contrastive_loss: 0.064366 (0.21014) Loss: 0.064366 (0.21014) 2025-03-19,11:21:46 | INFO | Train Epoch: 11 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 148.371/s, 148.371/s/gpu LR: 0.000138 Logit Scale: 26.057 Contrastive_loss: 0.23627 (0.21033) Loss: 0.23627 (0.21033) 2025-03-19,11:22:08 | INFO | Train Epoch: 11 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.219, 146.570/s, 146.570/s/gpu LR: 0.000138 Logit Scale: 26.051 Contrastive_loss: 0.26622 (0.21074) Loss: 0.26622 (0.21074) 2025-03-19,11:22:30 | INFO | Train Epoch: 11 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.219, 146.015/s, 146.015/s/gpu LR: 0.000138 Logit Scale: 26.056 Contrastive_loss: 0.20894 (0.21073) Loss: 0.20894 (0.21073) 2025-03-19,11:22:52 | INFO | Train Epoch: 11 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.219, 148.056/s, 148.056/s/gpu LR: 0.000138 Logit Scale: 26.071 Contrastive_loss: 0.24342 (0.21097) Loss: 0.24342 (0.21097) 2025-03-19,11:23:13 | INFO | Train Epoch: 11 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.494/s, 147.494/s/gpu LR: 0.000138 Logit Scale: 26.060 Contrastive_loss: 0.67495 (0.21430) Loss: 0.67495 (0.21430) 2025-03-19,11:23:35 | INFO | Train Epoch: 11 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.556/s, 149.556/s/gpu LR: 0.000138 Logit Scale: 26.084 Contrastive_loss: 0.20538 (0.21424) Loss: 0.20538 (0.21424) 2025-03-19,11:23:56 | INFO | Train Epoch: 11 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.316/s, 149.316/s/gpu LR: 0.000138 Logit Scale: 26.102 Contrastive_loss: 0.14179 (0.21373) Loss: 0.14179 (0.21373) 2025-03-19,11:24:18 | INFO | Train Epoch: 11 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 148.353/s, 148.353/s/gpu LR: 0.000138 Logit Scale: 26.097 Contrastive_loss: 0.26003 (0.21405) Loss: 0.26003 (0.21405) 2025-03-19,11:24:39 | INFO | Train Epoch: 11 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 151.171/s, 151.171/s/gpu LR: 0.000137 Logit Scale: 26.086 Contrastive_loss: 0.15744 (0.21366) Loss: 0.15744 (0.21366) 2025-03-19,11:25:00 | INFO | Train Epoch: 11 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 146.287/s, 146.287/s/gpu LR: 0.000137 Logit Scale: 26.028 Contrastive_loss: 0.20869 (0.21362) Loss: 0.20869 (0.21362) 2025-03-19,11:25:22 | INFO | Train Epoch: 11 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 147.696/s, 147.696/s/gpu LR: 0.000137 Logit Scale: 26.046 Contrastive_loss: 0.038987 (0.21242) Loss: 0.038987 (0.21242) 2025-03-19,11:25:44 | INFO | Train Epoch: 11 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.641/s, 148.641/s/gpu LR: 0.000137 Logit Scale: 26.041 Contrastive_loss: 0.13790 (0.21191) Loss: 0.13790 (0.21191) 2025-03-19,11:26:05 | INFO | Train Epoch: 11 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.431/s, 148.431/s/gpu LR: 0.000137 Logit Scale: 26.051 Contrastive_loss: 0.26651 (0.21228) Loss: 0.26651 (0.21228) 2025-03-19,11:26:26 | INFO | Train Epoch: 11 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 149.187/s, 149.187/s/gpu LR: 0.000137 Logit Scale: 26.088 Contrastive_loss: 0.16100 (0.21193) Loss: 0.16100 (0.21193) 2025-03-19,11:26:48 | INFO | Train Epoch: 11 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 148.671/s, 148.671/s/gpu LR: 0.000137 Logit Scale: 26.085 Contrastive_loss: 0.027802 (0.21070) Loss: 0.027802 (0.21070) 2025-03-19,11:27:10 | INFO | Train Epoch: 11 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 146.990/s, 146.990/s/gpu LR: 0.000137 Logit Scale: 26.071 Contrastive_loss: 0.10711 (0.21001) Loss: 0.10711 (0.21001) 2025-03-19,11:27:31 | INFO | Train Epoch: 11 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.217, 148.525/s, 148.525/s/gpu LR: 0.000137 Logit Scale: 26.063 Contrastive_loss: 0.31131 (0.21068) Loss: 0.31131 (0.21068) 2025-03-19,11:27:53 | INFO | Train Epoch: 11 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 147.959/s, 147.959/s/gpu LR: 0.000137 Logit Scale: 26.040 Contrastive_loss: 0.12607 (0.21012) Loss: 0.12607 (0.21012) 2025-03-19,11:28:15 | INFO | Train Epoch: 11 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.045/s, 148.045/s/gpu LR: 0.000137 Logit Scale: 26.047 Contrastive_loss: 0.082551 (0.20929) Loss: 0.082551 (0.20929) 2025-03-19,11:28:36 | INFO | Train Epoch: 11 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.664/s, 148.664/s/gpu LR: 0.000137 Logit Scale: 26.011 Contrastive_loss: 0.17242 (0.20905) Loss: 0.17242 (0.20905) 2025-03-19,11:28:58 | INFO | Train Epoch: 11 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 149.215/s, 149.215/s/gpu LR: 0.000137 Logit Scale: 26.046 Contrastive_loss: 0.14583 (0.20864) Loss: 0.14583 (0.20864) 2025-03-19,11:29:19 | INFO | Train Epoch: 11 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.028/s, 149.028/s/gpu LR: 0.000137 Logit Scale: 26.070 Contrastive_loss: 0.13630 (0.20818) Loss: 0.13630 (0.20818) 2025-03-19,11:29:41 | INFO | Train Epoch: 11 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.213, 151.629/s, 151.629/s/gpu LR: 0.000137 Logit Scale: 26.064 Contrastive_loss: 0.14375 (0.20777) Loss: 0.14375 (0.20777) 2025-03-19,11:30:02 | INFO | Train Epoch: 11 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 147.584/s, 147.584/s/gpu LR: 0.000137 Logit Scale: 26.007 Contrastive_loss: 0.11876 (0.20720) Loss: 0.11876 (0.20720) 2025-03-19,11:30:24 | INFO | Train Epoch: 11 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 149.077/s, 149.077/s/gpu LR: 0.000137 Logit Scale: 25.967 Contrastive_loss: 0.30852 (0.20784) Loss: 0.30852 (0.20784) 2025-03-19,11:30:46 | INFO | Train Epoch: 11 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 146.693/s, 146.693/s/gpu LR: 0.000137 Logit Scale: 25.960 Contrastive_loss: 0.14707 (0.20746) Loss: 0.14707 (0.20746) 2025-03-19,11:31:07 | INFO | Train Epoch: 11 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 145.508/s, 145.508/s/gpu LR: 0.000137 Logit Scale: 25.983 Contrastive_loss: 0.039952 (0.20642) Loss: 0.039952 (0.20642) 2025-03-19,11:31:29 | INFO | Train Epoch: 11 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.218, 151.241/s, 151.241/s/gpu LR: 0.000137 Logit Scale: 26.019 Contrastive_loss: 0.18344 (0.20628) Loss: 0.18344 (0.20628) 2025-03-19,11:31:51 | INFO | Train Epoch: 11 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 145.743/s, 145.743/s/gpu LR: 0.000137 Logit Scale: 25.990 Contrastive_loss: 0.21693 (0.20634) Loss: 0.21693 (0.20634) 2025-03-19,11:32:13 | INFO | Train Epoch: 11 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.220, 146.055/s, 146.055/s/gpu LR: 0.000137 Logit Scale: 25.989 Contrastive_loss: 0.19307 (0.20626) Loss: 0.19307 (0.20626) 2025-03-19,11:32:35 | INFO | Train Epoch: 11 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 145.435/s, 145.435/s/gpu LR: 0.000137 Logit Scale: 25.991 Contrastive_loss: 0.22801 (0.20639) Loss: 0.22801 (0.20639) 2025-03-19,11:32:57 | INFO | Train Epoch: 11 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 146.283/s, 146.283/s/gpu LR: 0.000137 Logit Scale: 26.005 Contrastive_loss: 0.15792 (0.20610) Loss: 0.15792 (0.20610) 2025-03-19,11:33:19 | INFO | Train Epoch: 11 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.220, 147.422/s, 147.422/s/gpu LR: 0.000136 Logit Scale: 26.012 Contrastive_loss: 0.10020 (0.20547) Loss: 0.10020 (0.20547) 2025-03-19,11:33:40 | INFO | Train Epoch: 11 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 149.015/s, 149.015/s/gpu LR: 0.000136 Logit Scale: 26.011 Contrastive_loss: 0.12436 (0.20498) Loss: 0.12436 (0.20498) 2025-03-19,11:34:02 | INFO | Train Epoch: 11 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 148.366/s, 148.366/s/gpu LR: 0.000136 Logit Scale: 26.002 Contrastive_loss: 0.020220 (0.20389) Loss: 0.020220 (0.20389) 2025-03-19,11:34:24 | INFO | Train Epoch: 11 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 150.047/s, 150.047/s/gpu LR: 0.000136 Logit Scale: 25.996 Contrastive_loss: 0.10223 (0.20329) Loss: 0.10223 (0.20329) 2025-03-19,11:34:45 | INFO | Train Epoch: 11 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 150.229/s, 150.229/s/gpu LR: 0.000136 Logit Scale: 25.963 Contrastive_loss: 0.045021 (0.20237) Loss: 0.045021 (0.20237) 2025-03-19,11:35:06 | INFO | Train Epoch: 11 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 149.254/s, 149.254/s/gpu LR: 0.000136 Logit Scale: 25.963 Contrastive_loss: 0.23843 (0.20258) Loss: 0.23843 (0.20258) 2025-03-19,11:35:28 | INFO | Train Epoch: 11 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 149.324/s, 149.324/s/gpu LR: 0.000136 Logit Scale: 25.975 Contrastive_loss: 0.086296 (0.20191) Loss: 0.086296 (0.20191) 2025-03-19,11:35:49 | INFO | Train Epoch: 11 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 148.664/s, 148.664/s/gpu LR: 0.000136 Logit Scale: 26.004 Contrastive_loss: 0.22720 (0.20205) Loss: 0.22720 (0.20205) 2025-03-19,11:36:11 | INFO | Train Epoch: 11 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.126/s, 149.126/s/gpu LR: 0.000136 Logit Scale: 26.013 Contrastive_loss: 0.26526 (0.20241) Loss: 0.26526 (0.20241) 2025-03-19,11:36:33 | INFO | Train Epoch: 11 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 145.136/s, 145.136/s/gpu LR: 0.000136 Logit Scale: 25.997 Contrastive_loss: 0.38525 (0.20345) Loss: 0.38525 (0.20345) 2025-03-19,11:36:54 | INFO | Train Epoch: 11 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 146.903/s, 146.903/s/gpu LR: 0.000136 Logit Scale: 25.955 Contrastive_loss: 0.44742 (0.20483) Loss: 0.44742 (0.20483) 2025-03-19,11:37:16 | INFO | Train Epoch: 11 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 148.054/s, 148.054/s/gpu LR: 0.000136 Logit Scale: 25.945 Contrastive_loss: 0.13640 (0.20444) Loss: 0.13640 (0.20444) 2025-03-19,11:37:37 | INFO | Train Epoch: 11 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.067/s, 149.067/s/gpu LR: 0.000136 Logit Scale: 25.984 Contrastive_loss: 0.17067 (0.20426) Loss: 0.17067 (0.20426) 2025-03-19,11:37:59 | INFO | Train Epoch: 11 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 150.016/s, 150.016/s/gpu LR: 0.000136 Logit Scale: 25.975 Contrastive_loss: 0.59719 (0.20644) Loss: 0.59719 (0.20644) 2025-03-19,11:38:20 | INFO | Train Epoch: 11 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.615/s, 149.615/s/gpu LR: 0.000136 Logit Scale: 25.964 Contrastive_loss: 0.30679 (0.20699) Loss: 0.30679 (0.20699) 2025-03-19,11:38:42 | INFO | Train Epoch: 11 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.570/s, 148.570/s/gpu LR: 0.000136 Logit Scale: 25.946 Contrastive_loss: 0.23958 (0.20717) Loss: 0.23958 (0.20717) 2025-03-19,11:39:03 | INFO | Train Epoch: 11 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.261/s, 148.261/s/gpu LR: 0.000136 Logit Scale: 25.944 Contrastive_loss: 0.18527 (0.20705) Loss: 0.18527 (0.20705) 2025-03-19,11:39:25 | INFO | Train Epoch: 11 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.862/s, 149.862/s/gpu LR: 0.000136 Logit Scale: 25.946 Contrastive_loss: 0.17616 (0.20688) Loss: 0.17616 (0.20688) 2025-03-19,11:39:46 | INFO | Train Epoch: 11 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 147.958/s, 147.958/s/gpu LR: 0.000136 Logit Scale: 25.960 Contrastive_loss: 0.18736 (0.20678) Loss: 0.18736 (0.20678) 2025-03-19,11:40:08 | INFO | Train Epoch: 11 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.217, 151.407/s, 151.407/s/gpu LR: 0.000136 Logit Scale: 25.933 Contrastive_loss: 0.042213 (0.20589) Loss: 0.042213 (0.20589) 2025-03-19,11:40:29 | INFO | Train Epoch: 11 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.598/s, 149.598/s/gpu LR: 0.000136 Logit Scale: 25.918 Contrastive_loss: 0.18510 (0.20578) Loss: 0.18510 (0.20578) 2025-03-19,11:40:51 | INFO | Train Epoch: 11 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 151.218/s, 151.218/s/gpu LR: 0.000136 Logit Scale: 25.904 Contrastive_loss: 0.10753 (0.20526) Loss: 0.10753 (0.20526) 2025-03-19,11:41:12 | INFO | Train Epoch: 11 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 149.242/s, 149.242/s/gpu LR: 0.000136 Logit Scale: 25.922 Contrastive_loss: 0.36749 (0.20612) Loss: 0.36749 (0.20612) 2025-03-19,11:41:34 | INFO | Train Epoch: 11 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 151.594/s, 151.594/s/gpu LR: 0.000136 Logit Scale: 25.942 Contrastive_loss: 0.33607 (0.20680) Loss: 0.33607 (0.20680) 2025-03-19,11:41:56 | INFO | Train Epoch: 11 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.219, 147.815/s, 147.815/s/gpu LR: 0.000135 Logit Scale: 26.001 Contrastive_loss: 0.18913 (0.20671) Loss: 0.18913 (0.20671) 2025-03-19,11:42:18 | INFO | Train Epoch: 11 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.218, 147.523/s, 147.523/s/gpu LR: 0.000135 Logit Scale: 25.995 Contrastive_loss: 0.33079 (0.20736) Loss: 0.33079 (0.20736) 2025-03-19,11:42:39 | INFO | Train Epoch: 11 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.218, 145.458/s, 145.458/s/gpu LR: 0.000135 Logit Scale: 25.951 Contrastive_loss: 0.40883 (0.20840) Loss: 0.40883 (0.20840) 2025-03-19,11:43:01 | INFO | Train Epoch: 11 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 145.232/s, 145.232/s/gpu LR: 0.000135 Logit Scale: 25.955 Contrastive_loss: 0.14734 (0.20809) Loss: 0.14734 (0.20809) 2025-03-19,11:43:23 | INFO | Train Epoch: 11 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 147.056/s, 147.056/s/gpu LR: 0.000135 Logit Scale: 25.934 Contrastive_loss: 0.29392 (0.20853) Loss: 0.29392 (0.20853) 2025-03-19,11:43:45 | INFO | Train Epoch: 11 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 147.832/s, 147.832/s/gpu LR: 0.000135 Logit Scale: 25.954 Contrastive_loss: 0.32507 (0.20912) Loss: 0.32507 (0.20912) 2025-03-19,11:44:07 | INFO | Train Epoch: 11 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.217, 148.165/s, 148.165/s/gpu LR: 0.000135 Logit Scale: 25.953 Contrastive_loss: 0.070592 (0.20842) Loss: 0.070592 (0.20842) 2025-03-19,11:44:28 | INFO | Train Epoch: 11 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.218, 148.035/s, 148.035/s/gpu LR: 0.000135 Logit Scale: 25.959 Contrastive_loss: 0.25142 (0.20863) Loss: 0.25142 (0.20863) 2025-03-19,11:44:50 | INFO | Train Epoch: 11 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 147.335/s, 147.335/s/gpu LR: 0.000135 Logit Scale: 25.968 Contrastive_loss: 0.019993 (0.20769) Loss: 0.019993 (0.20769) 2025-03-19,11:45:12 | INFO | Train Epoch: 11 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.217, 146.456/s, 146.456/s/gpu LR: 0.000135 Logit Scale: 25.940 Contrastive_loss: 0.20149 (0.20766) Loss: 0.20149 (0.20766) 2025-03-19,11:45:33 | INFO | Train Epoch: 11 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 149.079/s, 149.079/s/gpu LR: 0.000135 Logit Scale: 25.981 Contrastive_loss: 0.092398 (0.20708) Loss: 0.092398 (0.20708) 2025-03-19,11:45:55 | INFO | Train Epoch: 11 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.213, 152.068/s, 152.068/s/gpu LR: 0.000135 Logit Scale: 25.960 Contrastive_loss: 0.24422 (0.20727) Loss: 0.24422 (0.20727) 2025-03-19,11:46:16 | INFO | Train Epoch: 11 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 149.203/s, 149.203/s/gpu LR: 0.000135 Logit Scale: 25.960 Contrastive_loss: 0.17096 (0.20709) Loss: 0.17096 (0.20709) 2025-03-19,11:46:38 | INFO | Train Epoch: 11 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.217, 143.590/s, 143.590/s/gpu LR: 0.000135 Logit Scale: 25.970 Contrastive_loss: 0.20452 (0.20707) Loss: 0.20452 (0.20707) 2025-03-19,11:46:59 | INFO | Train Epoch: 11 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 149.851/s, 149.851/s/gpu LR: 0.000135 Logit Scale: 25.926 Contrastive_loss: 0.10647 (0.20658) Loss: 0.10647 (0.20658) 2025-03-19,11:47:21 | INFO | Train Epoch: 11 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 150.107/s, 150.107/s/gpu LR: 0.000135 Logit Scale: 25.960 Contrastive_loss: 0.11592 (0.20614) Loss: 0.11592 (0.20614) 2025-03-19,11:47:42 | INFO | Train Epoch: 11 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 148.145/s, 148.145/s/gpu LR: 0.000135 Logit Scale: 25.968 Contrastive_loss: 0.17926 (0.20601) Loss: 0.17926 (0.20601) 2025-03-19,11:48:04 | INFO | Train Epoch: 11 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.213, 151.086/s, 151.086/s/gpu LR: 0.000135 Logit Scale: 25.929 Contrastive_loss: 0.074418 (0.20538) Loss: 0.074418 (0.20538) 2025-03-19,11:48:25 | INFO | Train Epoch: 11 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 149.443/s, 149.443/s/gpu LR: 0.000135 Logit Scale: 26.007 Contrastive_loss: 0.11228 (0.20494) Loss: 0.11228 (0.20494) 2025-03-19,11:48:47 | INFO | Train Epoch: 11 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 148.351/s, 148.351/s/gpu LR: 0.000135 Logit Scale: 26.014 Contrastive_loss: 0.10189 (0.20445) Loss: 0.10189 (0.20445) 2025-03-19,11:49:08 | INFO | Train Epoch: 11 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 148.595/s, 148.595/s/gpu LR: 0.000135 Logit Scale: 26.011 Contrastive_loss: 0.48317 (0.20577) Loss: 0.48317 (0.20577) 2025-03-19,11:49:30 | INFO | Train Epoch: 11 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.219, 144.235/s, 144.235/s/gpu LR: 0.000135 Logit Scale: 26.009 Contrastive_loss: 0.15061 (0.20551) Loss: 0.15061 (0.20551) 2025-03-19,11:49:52 | INFO | Train Epoch: 11 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 150.583/s, 150.583/s/gpu LR: 0.000135 Logit Scale: 26.006 Contrastive_loss: 0.20302 (0.20549) Loss: 0.20302 (0.20549) 2025-03-19,11:50:14 | INFO | Train Epoch: 11 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 146.391/s, 146.391/s/gpu LR: 0.000135 Logit Scale: 26.029 Contrastive_loss: 0.35014 (0.20617) Loss: 0.35014 (0.20617) 2025-03-19,11:50:35 | INFO | Train Epoch: 11 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 151.779/s, 151.779/s/gpu LR: 0.000134 Logit Scale: 26.016 Contrastive_loss: 0.27778 (0.20650) Loss: 0.27778 (0.20650) 2025-03-19,11:50:56 | INFO | Train Epoch: 11 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.213, 148.096/s, 148.096/s/gpu LR: 0.000134 Logit Scale: 26.037 Contrastive_loss: 0.55550 (0.20812) Loss: 0.55550 (0.20812) 2025-03-19,11:51:18 | INFO | Train Epoch: 11 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.343/s, 150.343/s/gpu LR: 0.000134 Logit Scale: 26.016 Contrastive_loss: 0.18975 (0.20803) Loss: 0.18975 (0.20803) 2025-03-19,11:51:39 | INFO | Train Epoch: 11 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 148.247/s, 148.247/s/gpu LR: 0.000134 Logit Scale: 26.056 Contrastive_loss: 0.20301 (0.20801) Loss: 0.20301 (0.20801) 2025-03-19,11:52:01 | INFO | Train Epoch: 11 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 146.767/s, 146.767/s/gpu LR: 0.000134 Logit Scale: 26.020 Contrastive_loss: 0.41323 (0.20895) Loss: 0.41323 (0.20895) 2025-03-19,11:52:22 | INFO | Train Epoch: 11 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 145.094/s, 145.094/s/gpu LR: 0.000134 Logit Scale: 25.977 Contrastive_loss: 0.31667 (0.20944) Loss: 0.31667 (0.20944) 2025-03-19,11:52:44 | INFO | Train Epoch: 11 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.218, 148.759/s, 148.759/s/gpu LR: 0.000134 Logit Scale: 25.996 Contrastive_loss: 0.17001 (0.20926) Loss: 0.17001 (0.20926) 2025-03-19,11:53:06 | INFO | Train Epoch: 11 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.218, 145.754/s, 145.754/s/gpu LR: 0.000134 Logit Scale: 25.980 Contrastive_loss: 0.014571 (0.20838) Loss: 0.014571 (0.20838) 2025-03-19,11:53:28 | INFO | Train Epoch: 11 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.219, 150.812/s, 150.812/s/gpu LR: 0.000134 Logit Scale: 25.969 Contrastive_loss: 0.13826 (0.20807) Loss: 0.13826 (0.20807) 2025-03-19,11:53:49 | INFO | Train Epoch: 11 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 152.296/s, 152.296/s/gpu LR: 0.000134 Logit Scale: 25.969 Contrastive_loss: 0.14450 (0.20778) Loss: 0.14450 (0.20778) 2025-03-19,11:54:11 | INFO | Train Epoch: 11 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 151.675/s, 151.675/s/gpu LR: 0.000134 Logit Scale: 25.965 Contrastive_loss: 0.097656 (0.20730) Loss: 0.097656 (0.20730) 2025-03-19,11:54:32 | INFO | Train Epoch: 11 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.487/s, 148.487/s/gpu LR: 0.000134 Logit Scale: 25.954 Contrastive_loss: 0.10239 (0.20683) Loss: 0.10239 (0.20683) 2025-03-19,11:54:54 | INFO | Train Epoch: 11 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 152.256/s, 152.256/s/gpu LR: 0.000134 Logit Scale: 25.948 Contrastive_loss: 0.61488 (0.20863) Loss: 0.61488 (0.20863) 2025-03-19,11:55:15 | INFO | Train Epoch: 11 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 152.474/s, 152.474/s/gpu LR: 0.000134 Logit Scale: 25.978 Contrastive_loss: 0.070219 (0.20802) Loss: 0.070219 (0.20802) 2025-03-19,11:55:37 | INFO | Train Epoch: 11 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 147.013/s, 147.013/s/gpu LR: 0.000134 Logit Scale: 25.982 Contrastive_loss: 0.25819 (0.20824) Loss: 0.25819 (0.20824) 2025-03-19,11:55:58 | INFO | Train Epoch: 11 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 148.521/s, 148.521/s/gpu LR: 0.000134 Logit Scale: 25.988 Contrastive_loss: 0.15386 (0.20800) Loss: 0.15386 (0.20800) 2025-03-19,11:56:20 | INFO | Train Epoch: 11 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 147.961/s, 147.961/s/gpu LR: 0.000134 Logit Scale: 25.949 Contrastive_loss: 0.30836 (0.20844) Loss: 0.30836 (0.20844) 2025-03-19,11:56:41 | INFO | Train Epoch: 11 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.507/s, 149.507/s/gpu LR: 0.000134 Logit Scale: 25.943 Contrastive_loss: 0.0053922 (0.20756) Loss: 0.0053922 (0.20756) 2025-03-19,11:57:03 | INFO | Train Epoch: 11 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 148.573/s, 148.573/s/gpu LR: 0.000134 Logit Scale: 25.949 Contrastive_loss: 0.14911 (0.20731) Loss: 0.14911 (0.20731) 2025-03-19,11:57:24 | INFO | Train Epoch: 11 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 147.907/s, 147.907/s/gpu LR: 0.000134 Logit Scale: 25.986 Contrastive_loss: 0.28085 (0.20763) Loss: 0.28085 (0.20763) 2025-03-19,11:57:46 | INFO | Train Epoch: 11 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 148.339/s, 148.339/s/gpu LR: 0.000134 Logit Scale: 25.991 Contrastive_loss: 0.29929 (0.20802) Loss: 0.29929 (0.20802) 2025-03-19,11:58:07 | INFO | Train Epoch: 11 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 149.485/s, 149.485/s/gpu LR: 0.000134 Logit Scale: 26.000 Contrastive_loss: 0.20110 (0.20799) Loss: 0.20110 (0.20799) 2025-03-19,11:58:29 | INFO | Train Epoch: 11 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 148.246/s, 148.246/s/gpu LR: 0.000134 Logit Scale: 26.022 Contrastive_loss: 0.12644 (0.20764) Loss: 0.12644 (0.20764) 2025-03-19,11:58:50 | INFO | Train Epoch: 11 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 147.153/s, 147.153/s/gpu LR: 0.000134 Logit Scale: 26.028 Contrastive_loss: 0.21433 (0.20767) Loss: 0.21433 (0.20767) 2025-03-19,11:59:12 | INFO | Train Epoch: 11 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.219, 146.375/s, 146.375/s/gpu LR: 0.000133 Logit Scale: 26.022 Contrastive_loss: 0.089714 (0.20718) Loss: 0.089714 (0.20718) 2025-03-19,11:59:34 | INFO | Train Epoch: 11 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.219, 142.559/s, 142.559/s/gpu LR: 0.000133 Logit Scale: 26.012 Contrastive_loss: 0.40519 (0.20800) Loss: 0.40519 (0.20800) 2025-03-19,11:59:42 | INFO | Train Epoch: 11 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.221, 146.101/s, 146.101/s/gpu LR: 0.000133 Logit Scale: 26.009 Contrastive_loss: 0.23962 (0.20813) Loss: 0.23962 (0.20813) 2025-03-19,11:59:42 | INFO | Eval Epoch: 12 [32 / 7443] Clip Loss: 2.874313 2025-03-19,11:59:48 | INFO | Eval Epoch: 12 [3232 / 7443] Clip Loss: 0.863406 2025-03-19,11:59:54 | INFO | Eval Epoch: 12 [6432 / 7443] Clip Loss: 0.668711 2025-03-19,11:59:57 | INFO | Eval Epoch: 12 image_to_text_mean_rank: 90.0181 image_to_text_median_rank: 7.0000 image_to_text_R@1: 0.1267 image_to_text_R@5: 0.4311 image_to_text_R@10: 0.6096 text_to_image_mean_rank: 58.3744 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1310 text_to_image_R@5: 0.4362 text_to_image_R@10: 0.6121 clip_val_loss: 0.6251 epoch: 12.0000 num_samples: 7443.0000 2025-03-19,12:00:30 | INFO | Start epoch 12 2025-03-19,12:00:31 | INFO | Train Epoch: 12 [ 32/766009 (0%)] Data (t): 0.172 Batch (t): 0.375, 85.3845/s, 85.3845/s/gpu LR: 0.000133 Logit Scale: 26.008 Contrastive_loss: 0.16973 (0.16973) Loss: 0.16973 (0.16973) 2025-03-19,12:00:52 | INFO | Train Epoch: 12 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 150.144/s, 150.144/s/gpu LR: 0.000133 Logit Scale: 26.044 Contrastive_loss: 0.0097016 (0.089716) Loss: 0.0097016 (0.089716) 2025-03-19,12:01:14 | INFO | Train Epoch: 12 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 148.546/s, 148.546/s/gpu LR: 0.000133 Logit Scale: 26.044 Contrastive_loss: 0.20203 (0.12715) Loss: 0.20203 (0.12715) 2025-03-19,12:01:35 | INFO | Train Epoch: 12 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.915/s, 149.915/s/gpu LR: 0.000133 Logit Scale: 26.100 Contrastive_loss: 0.031254 (0.10318) Loss: 0.031254 (0.10318) 2025-03-19,12:01:57 | INFO | Train Epoch: 12 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 148.409/s, 148.409/s/gpu LR: 0.000133 Logit Scale: 26.129 Contrastive_loss: 0.076580 (0.097858) Loss: 0.076580 (0.097858) 2025-03-19,12:02:18 | INFO | Train Epoch: 12 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 148.568/s, 148.568/s/gpu LR: 0.000133 Logit Scale: 26.142 Contrastive_loss: 0.087045 (0.096056) Loss: 0.087045 (0.096056) 2025-03-19,12:02:40 | INFO | Train Epoch: 12 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 149.588/s, 149.588/s/gpu LR: 0.000133 Logit Scale: 26.161 Contrastive_loss: 0.037001 (0.087620) Loss: 0.037001 (0.087620) 2025-03-19,12:03:01 | INFO | Train Epoch: 12 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 150.020/s, 150.020/s/gpu LR: 0.000133 Logit Scale: 26.178 Contrastive_loss: 0.19980 (0.10164) Loss: 0.19980 (0.10164) 2025-03-19,12:03:23 | INFO | Train Epoch: 12 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.504/s, 149.504/s/gpu LR: 0.000133 Logit Scale: 26.201 Contrastive_loss: 0.11559 (0.10319) Loss: 0.11559 (0.10319) 2025-03-19,12:03:44 | INFO | Train Epoch: 12 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.216, 149.920/s, 149.920/s/gpu LR: 0.000133 Logit Scale: 26.232 Contrastive_loss: 0.26111 (0.11898) Loss: 0.26111 (0.11898) 2025-03-19,12:04:06 | INFO | Train Epoch: 12 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.213, 149.915/s, 149.915/s/gpu LR: 0.000133 Logit Scale: 26.240 Contrastive_loss: 0.23212 (0.12927) Loss: 0.23212 (0.12927) 2025-03-19,12:04:27 | INFO | Train Epoch: 12 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 149.938/s, 149.938/s/gpu LR: 0.000133 Logit Scale: 26.258 Contrastive_loss: 0.17284 (0.13290) Loss: 0.17284 (0.13290) 2025-03-19,12:04:48 | INFO | Train Epoch: 12 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 151.699/s, 151.699/s/gpu LR: 0.000133 Logit Scale: 26.233 Contrastive_loss: 0.18520 (0.13692) Loss: 0.18520 (0.13692) 2025-03-19,12:05:10 | INFO | Train Epoch: 12 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 150.389/s, 150.389/s/gpu LR: 0.000133 Logit Scale: 26.211 Contrastive_loss: 0.18423 (0.14030) Loss: 0.18423 (0.14030) 2025-03-19,12:05:31 | INFO | Train Epoch: 12 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 151.622/s, 151.622/s/gpu LR: 0.000133 Logit Scale: 26.224 Contrastive_loss: 0.18222 (0.14310) Loss: 0.18222 (0.14310) 2025-03-19,12:05:53 | INFO | Train Epoch: 12 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 149.689/s, 149.689/s/gpu LR: 0.000133 Logit Scale: 26.256 Contrastive_loss: 0.054627 (0.13757) Loss: 0.054627 (0.13757) 2025-03-19,12:06:14 | INFO | Train Epoch: 12 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 149.832/s, 149.832/s/gpu LR: 0.000133 Logit Scale: 26.248 Contrastive_loss: 0.093333 (0.13496) Loss: 0.093333 (0.13496) 2025-03-19,12:06:36 | INFO | Train Epoch: 12 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 148.120/s, 148.120/s/gpu LR: 0.000133 Logit Scale: 26.255 Contrastive_loss: 0.095363 (0.13276) Loss: 0.095363 (0.13276) 2025-03-19,12:06:58 | INFO | Train Epoch: 12 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 148.423/s, 148.423/s/gpu LR: 0.000133 Logit Scale: 26.260 Contrastive_loss: 0.12959 (0.13260) Loss: 0.12959 (0.13260) 2025-03-19,12:07:19 | INFO | Train Epoch: 12 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 146.403/s, 146.403/s/gpu LR: 0.000133 Logit Scale: 26.250 Contrastive_loss: 0.17642 (0.13479) Loss: 0.17642 (0.13479) 2025-03-19,12:07:41 | INFO | Train Epoch: 12 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 148.994/s, 148.994/s/gpu LR: 0.000133 Logit Scale: 26.206 Contrastive_loss: 0.24457 (0.14002) Loss: 0.24457 (0.14002) 2025-03-19,12:08:02 | INFO | Train Epoch: 12 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 148.590/s, 148.590/s/gpu LR: 0.000133 Logit Scale: 26.243 Contrastive_loss: 0.079693 (0.13727) Loss: 0.079693 (0.13727) 2025-03-19,12:08:24 | INFO | Train Epoch: 12 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 149.193/s, 149.193/s/gpu LR: 0.000133 Logit Scale: 26.246 Contrastive_loss: 0.27619 (0.14331) Loss: 0.27619 (0.14331) 2025-03-19,12:08:46 | INFO | Train Epoch: 12 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 149.075/s, 149.075/s/gpu LR: 0.000132 Logit Scale: 26.219 Contrastive_loss: 0.049128 (0.13939) Loss: 0.049128 (0.13939) 2025-03-19,12:09:07 | INFO | Train Epoch: 12 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.704/s, 149.704/s/gpu LR: 0.000132 Logit Scale: 26.293 Contrastive_loss: 0.15936 (0.14019) Loss: 0.15936 (0.14019) 2025-03-19,12:09:29 | INFO | Train Epoch: 12 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.267/s, 149.267/s/gpu LR: 0.000132 Logit Scale: 26.304 Contrastive_loss: 0.33915 (0.14784) Loss: 0.33915 (0.14784) 2025-03-19,12:09:50 | INFO | Train Epoch: 12 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 150.373/s, 150.373/s/gpu LR: 0.000132 Logit Scale: 26.326 Contrastive_loss: 0.24731 (0.15152) Loss: 0.24731 (0.15152) 2025-03-19,12:10:11 | INFO | Train Epoch: 12 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 150.091/s, 150.091/s/gpu LR: 0.000132 Logit Scale: 26.285 Contrastive_loss: 0.37769 (0.15960) Loss: 0.37769 (0.15960) 2025-03-19,12:10:33 | INFO | Train Epoch: 12 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 149.170/s, 149.170/s/gpu LR: 0.000132 Logit Scale: 26.287 Contrastive_loss: 0.24002 (0.16238) Loss: 0.24002 (0.16238) 2025-03-19,12:10:54 | INFO | Train Epoch: 12 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 148.253/s, 148.253/s/gpu LR: 0.000132 Logit Scale: 26.305 Contrastive_loss: 0.53003 (0.17463) Loss: 0.53003 (0.17463) 2025-03-19,12:11:16 | INFO | Train Epoch: 12 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.981/s, 149.981/s/gpu LR: 0.000132 Logit Scale: 26.319 Contrastive_loss: 0.029525 (0.16995) Loss: 0.029525 (0.16995) 2025-03-19,12:11:37 | INFO | Train Epoch: 12 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.387/s, 149.387/s/gpu LR: 0.000132 Logit Scale: 26.303 Contrastive_loss: 0.11026 (0.16808) Loss: 0.11026 (0.16808) 2025-03-19,12:11:59 | INFO | Train Epoch: 12 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 148.387/s, 148.387/s/gpu LR: 0.000132 Logit Scale: 26.282 Contrastive_loss: 0.46029 (0.17694) Loss: 0.46029 (0.17694) 2025-03-19,12:12:21 | INFO | Train Epoch: 12 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.995/s, 149.995/s/gpu LR: 0.000132 Logit Scale: 26.260 Contrastive_loss: 0.27642 (0.17986) Loss: 0.27642 (0.17986) 2025-03-19,12:12:42 | INFO | Train Epoch: 12 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.146/s, 149.146/s/gpu LR: 0.000132 Logit Scale: 26.315 Contrastive_loss: 0.26298 (0.18224) Loss: 0.26298 (0.18224) 2025-03-19,12:13:04 | INFO | Train Epoch: 12 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.736/s, 149.736/s/gpu LR: 0.000132 Logit Scale: 26.296 Contrastive_loss: 0.30954 (0.18578) Loss: 0.30954 (0.18578) 2025-03-19,12:13:25 | INFO | Train Epoch: 12 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.711/s, 148.711/s/gpu LR: 0.000132 Logit Scale: 26.280 Contrastive_loss: 0.048248 (0.18206) Loss: 0.048248 (0.18206) 2025-03-19,12:13:47 | INFO | Train Epoch: 12 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.271/s, 149.271/s/gpu LR: 0.000132 Logit Scale: 26.293 Contrastive_loss: 0.15279 (0.18129) Loss: 0.15279 (0.18129) 2025-03-19,12:14:08 | INFO | Train Epoch: 12 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 145.739/s, 145.739/s/gpu LR: 0.000132 Logit Scale: 26.311 Contrastive_loss: 0.43304 (0.18774) Loss: 0.43304 (0.18774) 2025-03-19,12:14:29 | INFO | Train Epoch: 12 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 151.854/s, 151.854/s/gpu LR: 0.000132 Logit Scale: 26.329 Contrastive_loss: 0.25024 (0.18931) Loss: 0.25024 (0.18931) 2025-03-19,12:14:51 | INFO | Train Epoch: 12 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.213, 151.315/s, 151.315/s/gpu LR: 0.000132 Logit Scale: 26.309 Contrastive_loss: 0.036282 (0.18557) Loss: 0.036282 (0.18557) 2025-03-19,12:15:12 | INFO | Train Epoch: 12 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 144.848/s, 144.848/s/gpu LR: 0.000132 Logit Scale: 26.302 Contrastive_loss: 0.056760 (0.18251) Loss: 0.056760 (0.18251) 2025-03-19,12:15:34 | INFO | Train Epoch: 12 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 147.051/s, 147.051/s/gpu LR: 0.000132 Logit Scale: 26.285 Contrastive_loss: 0.036005 (0.17910) Loss: 0.036005 (0.17910) 2025-03-19,12:15:55 | INFO | Train Epoch: 12 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 149.389/s, 149.389/s/gpu LR: 0.000132 Logit Scale: 26.292 Contrastive_loss: 0.11232 (0.17758) Loss: 0.11232 (0.17758) 2025-03-19,12:16:17 | INFO | Train Epoch: 12 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 149.709/s, 149.709/s/gpu LR: 0.000132 Logit Scale: 26.295 Contrastive_loss: 0.26458 (0.17952) Loss: 0.26458 (0.17952) 2025-03-19,12:16:39 | INFO | Train Epoch: 12 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.219, 143.672/s, 143.672/s/gpu LR: 0.000132 Logit Scale: 26.290 Contrastive_loss: 0.12009 (0.17822) Loss: 0.12009 (0.17822) 2025-03-19,12:17:01 | INFO | Train Epoch: 12 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.222, 145.459/s, 145.459/s/gpu LR: 0.000131 Logit Scale: 26.331 Contrastive_loss: 0.11300 (0.17684) Loss: 0.11300 (0.17684) 2025-03-19,12:17:23 | INFO | Train Epoch: 12 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.219, 147.506/s, 147.506/s/gpu LR: 0.000131 Logit Scale: 26.344 Contrastive_loss: 0.21587 (0.17765) Loss: 0.21587 (0.17765) 2025-03-19,12:17:44 | INFO | Train Epoch: 12 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 148.523/s, 148.523/s/gpu LR: 0.000131 Logit Scale: 26.297 Contrastive_loss: 0.18929 (0.17789) Loss: 0.18929 (0.17789) 2025-03-19,12:18:06 | INFO | Train Epoch: 12 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.576/s, 149.576/s/gpu LR: 0.000131 Logit Scale: 26.305 Contrastive_loss: 0.30933 (0.18052) Loss: 0.30933 (0.18052) 2025-03-19,12:18:27 | INFO | Train Epoch: 12 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.385/s, 149.385/s/gpu LR: 0.000131 Logit Scale: 26.333 Contrastive_loss: 0.21687 (0.18123) Loss: 0.21687 (0.18123) 2025-03-19,12:18:49 | INFO | Train Epoch: 12 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.012/s, 149.012/s/gpu LR: 0.000131 Logit Scale: 26.321 Contrastive_loss: 0.22621 (0.18209) Loss: 0.22621 (0.18209) 2025-03-19,12:19:10 | INFO | Train Epoch: 12 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 149.957/s, 149.957/s/gpu LR: 0.000131 Logit Scale: 26.339 Contrastive_loss: 0.15619 (0.18160) Loss: 0.15619 (0.18160) 2025-03-19,12:19:32 | INFO | Train Epoch: 12 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 148.431/s, 148.431/s/gpu LR: 0.000131 Logit Scale: 26.293 Contrastive_loss: 0.25518 (0.18297) Loss: 0.25518 (0.18297) 2025-03-19,12:19:53 | INFO | Train Epoch: 12 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.826/s, 149.826/s/gpu LR: 0.000131 Logit Scale: 26.289 Contrastive_loss: 0.19887 (0.18326) Loss: 0.19887 (0.18326) 2025-03-19,12:20:15 | INFO | Train Epoch: 12 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 152.505/s, 152.505/s/gpu LR: 0.000131 Logit Scale: 26.272 Contrastive_loss: 0.10269 (0.18182) Loss: 0.10269 (0.18182) 2025-03-19,12:20:37 | INFO | Train Epoch: 12 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 146.489/s, 146.489/s/gpu LR: 0.000131 Logit Scale: 26.302 Contrastive_loss: 0.18480 (0.18187) Loss: 0.18480 (0.18187) 2025-03-19,12:20:58 | INFO | Train Epoch: 12 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.218, 146.922/s, 146.922/s/gpu LR: 0.000131 Logit Scale: 26.297 Contrastive_loss: 0.15596 (0.18142) Loss: 0.15596 (0.18142) 2025-03-19,12:21:20 | INFO | Train Epoch: 12 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.221, 145.436/s, 145.436/s/gpu LR: 0.000131 Logit Scale: 26.316 Contrastive_loss: 0.073416 (0.17959) Loss: 0.073416 (0.17959) 2025-03-19,12:21:42 | INFO | Train Epoch: 12 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 144.811/s, 144.811/s/gpu LR: 0.000131 Logit Scale: 26.350 Contrastive_loss: 0.24751 (0.18072) Loss: 0.24751 (0.18072) 2025-03-19,12:22:04 | INFO | Train Epoch: 12 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.219, 147.226/s, 147.226/s/gpu LR: 0.000131 Logit Scale: 26.294 Contrastive_loss: 0.24394 (0.18176) Loss: 0.24394 (0.18176) 2025-03-19,12:22:26 | INFO | Train Epoch: 12 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 148.904/s, 148.904/s/gpu LR: 0.000131 Logit Scale: 26.302 Contrastive_loss: 0.22138 (0.18240) Loss: 0.22138 (0.18240) 2025-03-19,12:22:47 | INFO | Train Epoch: 12 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 151.751/s, 151.751/s/gpu LR: 0.000131 Logit Scale: 26.300 Contrastive_loss: 0.060120 (0.18046) Loss: 0.060120 (0.18046) 2025-03-19,12:23:09 | INFO | Train Epoch: 12 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 152.854/s, 152.854/s/gpu LR: 0.000131 Logit Scale: 26.334 Contrastive_loss: 0.14066 (0.17984) Loss: 0.14066 (0.17984) 2025-03-19,12:23:30 | INFO | Train Epoch: 12 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.217, 147.704/s, 147.704/s/gpu LR: 0.000131 Logit Scale: 26.331 Contrastive_loss: 0.47972 (0.18445) Loss: 0.47972 (0.18445) 2025-03-19,12:23:52 | INFO | Train Epoch: 12 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.218, 148.465/s, 148.465/s/gpu LR: 0.000131 Logit Scale: 26.302 Contrastive_loss: 0.15938 (0.18407) Loss: 0.15938 (0.18407) 2025-03-19,12:24:14 | INFO | Train Epoch: 12 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 150.149/s, 150.149/s/gpu LR: 0.000131 Logit Scale: 26.355 Contrastive_loss: 0.067837 (0.18234) Loss: 0.067837 (0.18234) 2025-03-19,12:24:35 | INFO | Train Epoch: 12 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.810/s, 149.810/s/gpu LR: 0.000131 Logit Scale: 26.354 Contrastive_loss: 0.21970 (0.18289) Loss: 0.21970 (0.18289) 2025-03-19,12:24:57 | INFO | Train Epoch: 12 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.589/s, 149.589/s/gpu LR: 0.000131 Logit Scale: 26.385 Contrastive_loss: 0.12341 (0.18202) Loss: 0.12341 (0.18202) 2025-03-19,12:25:18 | INFO | Train Epoch: 12 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.098/s, 149.098/s/gpu LR: 0.000131 Logit Scale: 26.367 Contrastive_loss: 0.046209 (0.18008) Loss: 0.046209 (0.18008) 2025-03-19,12:25:39 | INFO | Train Epoch: 12 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.922/s, 149.922/s/gpu LR: 0.000130 Logit Scale: 26.395 Contrastive_loss: 0.18424 (0.18014) Loss: 0.18424 (0.18014) 2025-03-19,12:26:01 | INFO | Train Epoch: 12 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 150.302/s, 150.302/s/gpu LR: 0.000130 Logit Scale: 26.388 Contrastive_loss: 0.18805 (0.18025) Loss: 0.18805 (0.18025) 2025-03-19,12:26:22 | INFO | Train Epoch: 12 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 149.712/s, 149.712/s/gpu LR: 0.000130 Logit Scale: 26.384 Contrastive_loss: 0.33752 (0.18241) Loss: 0.33752 (0.18241) 2025-03-19,12:26:44 | INFO | Train Epoch: 12 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 150.591/s, 150.591/s/gpu LR: 0.000130 Logit Scale: 26.389 Contrastive_loss: 0.36300 (0.18485) Loss: 0.36300 (0.18485) 2025-03-19,12:27:05 | INFO | Train Epoch: 12 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.982/s, 149.982/s/gpu LR: 0.000130 Logit Scale: 26.407 Contrastive_loss: 0.15850 (0.18449) Loss: 0.15850 (0.18449) 2025-03-19,12:27:27 | INFO | Train Epoch: 12 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.511/s, 148.511/s/gpu LR: 0.000130 Logit Scale: 26.431 Contrastive_loss: 0.015058 (0.18227) Loss: 0.015058 (0.18227) 2025-03-19,12:27:48 | INFO | Train Epoch: 12 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 146.998/s, 146.998/s/gpu LR: 0.000130 Logit Scale: 26.420 Contrastive_loss: 0.11121 (0.18134) Loss: 0.11121 (0.18134) 2025-03-19,12:28:10 | INFO | Train Epoch: 12 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 145.024/s, 145.024/s/gpu LR: 0.000130 Logit Scale: 26.373 Contrastive_loss: 0.078759 (0.18003) Loss: 0.078759 (0.18003) 2025-03-19,12:28:31 | INFO | Train Epoch: 12 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 149.336/s, 149.336/s/gpu LR: 0.000130 Logit Scale: 26.392 Contrastive_loss: 0.27095 (0.18118) Loss: 0.27095 (0.18118) 2025-03-19,12:28:52 | INFO | Train Epoch: 12 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.212, 151.168/s, 151.168/s/gpu LR: 0.000130 Logit Scale: 26.388 Contrastive_loss: 0.030913 (0.17930) Loss: 0.030913 (0.17930) 2025-03-19,12:29:14 | INFO | Train Epoch: 12 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.212, 150.560/s, 150.560/s/gpu LR: 0.000130 Logit Scale: 26.405 Contrastive_loss: 0.024642 (0.17739) Loss: 0.024642 (0.17739) 2025-03-19,12:29:35 | INFO | Train Epoch: 12 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.213, 151.695/s, 151.695/s/gpu LR: 0.000130 Logit Scale: 26.370 Contrastive_loss: 0.16206 (0.17720) Loss: 0.16206 (0.17720) 2025-03-19,12:29:56 | INFO | Train Epoch: 12 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.213, 151.287/s, 151.287/s/gpu LR: 0.000130 Logit Scale: 26.407 Contrastive_loss: 0.15247 (0.17691) Loss: 0.15247 (0.17691) 2025-03-19,12:30:18 | INFO | Train Epoch: 12 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.213, 150.488/s, 150.488/s/gpu LR: 0.000130 Logit Scale: 26.374 Contrastive_loss: 0.055200 (0.17546) Loss: 0.055200 (0.17546) 2025-03-19,12:30:39 | INFO | Train Epoch: 12 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 150.271/s, 150.271/s/gpu LR: 0.000130 Logit Scale: 26.335 Contrastive_loss: 0.085519 (0.17440) Loss: 0.085519 (0.17440) 2025-03-19,12:31:00 | INFO | Train Epoch: 12 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.213, 150.445/s, 150.445/s/gpu LR: 0.000130 Logit Scale: 26.364 Contrastive_loss: 0.37763 (0.17676) Loss: 0.37763 (0.17676) 2025-03-19,12:31:22 | INFO | Train Epoch: 12 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 150.215/s, 150.215/s/gpu LR: 0.000130 Logit Scale: 26.338 Contrastive_loss: 0.14001 (0.17634) Loss: 0.14001 (0.17634) 2025-03-19,12:31:43 | INFO | Train Epoch: 12 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.164/s, 149.164/s/gpu LR: 0.000130 Logit Scale: 26.348 Contrastive_loss: 0.22394 (0.17688) Loss: 0.22394 (0.17688) 2025-03-19,12:32:05 | INFO | Train Epoch: 12 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 148.442/s, 148.442/s/gpu LR: 0.000130 Logit Scale: 26.320 Contrastive_loss: 0.059150 (0.17556) Loss: 0.059150 (0.17556) 2025-03-19,12:32:26 | INFO | Train Epoch: 12 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 147.480/s, 147.480/s/gpu LR: 0.000130 Logit Scale: 26.359 Contrastive_loss: 0.43859 (0.17848) Loss: 0.43859 (0.17848) 2025-03-19,12:32:48 | INFO | Train Epoch: 12 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 150.323/s, 150.323/s/gpu LR: 0.000130 Logit Scale: 26.307 Contrastive_loss: 0.18396 (0.17854) Loss: 0.18396 (0.17854) 2025-03-19,12:33:09 | INFO | Train Epoch: 12 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.705/s, 148.705/s/gpu LR: 0.000130 Logit Scale: 26.329 Contrastive_loss: 0.16177 (0.17836) Loss: 0.16177 (0.17836) 2025-03-19,12:33:31 | INFO | Train Epoch: 12 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.631/s, 149.631/s/gpu LR: 0.000130 Logit Scale: 26.308 Contrastive_loss: 0.26428 (0.17928) Loss: 0.26428 (0.17928) 2025-03-19,12:33:52 | INFO | Train Epoch: 12 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 150.421/s, 150.421/s/gpu LR: 0.000130 Logit Scale: 26.310 Contrastive_loss: 0.14636 (0.17893) Loss: 0.14636 (0.17893) 2025-03-19,12:34:14 | INFO | Train Epoch: 12 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 148.850/s, 148.850/s/gpu LR: 0.000129 Logit Scale: 26.310 Contrastive_loss: 0.075957 (0.17785) Loss: 0.075957 (0.17785) 2025-03-19,12:34:35 | INFO | Train Epoch: 12 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.213, 151.227/s, 151.227/s/gpu LR: 0.000129 Logit Scale: 26.296 Contrastive_loss: 0.17336 (0.17780) Loss: 0.17336 (0.17780) 2025-03-19,12:34:57 | INFO | Train Epoch: 12 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 148.258/s, 148.258/s/gpu LR: 0.000129 Logit Scale: 26.278 Contrastive_loss: 0.36499 (0.17973) Loss: 0.36499 (0.17973) 2025-03-19,12:35:18 | INFO | Train Epoch: 12 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 150.907/s, 150.907/s/gpu LR: 0.000129 Logit Scale: 26.308 Contrastive_loss: 0.12180 (0.17914) Loss: 0.12180 (0.17914) 2025-03-19,12:35:40 | INFO | Train Epoch: 12 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 147.903/s, 147.903/s/gpu LR: 0.000129 Logit Scale: 26.311 Contrastive_loss: 0.12378 (0.17858) Loss: 0.12378 (0.17858) 2025-03-19,12:36:02 | INFO | Train Epoch: 12 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 149.409/s, 149.409/s/gpu LR: 0.000129 Logit Scale: 26.301 Contrastive_loss: 0.12127 (0.17801) Loss: 0.12127 (0.17801) 2025-03-19,12:36:23 | INFO | Train Epoch: 12 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 150.125/s, 150.125/s/gpu LR: 0.000129 Logit Scale: 26.277 Contrastive_loss: 0.17356 (0.17796) Loss: 0.17356 (0.17796) 2025-03-19,12:36:44 | INFO | Train Epoch: 12 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.215, 149.789/s, 149.789/s/gpu LR: 0.000129 Logit Scale: 26.300 Contrastive_loss: 0.34074 (0.17956) Loss: 0.34074 (0.17956) 2025-03-19,12:37:06 | INFO | Train Epoch: 12 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 151.111/s, 151.111/s/gpu LR: 0.000129 Logit Scale: 26.273 Contrastive_loss: 0.25693 (0.18031) Loss: 0.25693 (0.18031) 2025-03-19,12:37:27 | INFO | Train Epoch: 12 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.212, 150.703/s, 150.703/s/gpu LR: 0.000129 Logit Scale: 26.284 Contrastive_loss: 0.12272 (0.17976) Loss: 0.12272 (0.17976) 2025-03-19,12:37:48 | INFO | Train Epoch: 12 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.212, 151.472/s, 151.472/s/gpu LR: 0.000129 Logit Scale: 26.325 Contrastive_loss: 0.075022 (0.17876) Loss: 0.075022 (0.17876) 2025-03-19,12:38:10 | INFO | Train Epoch: 12 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 149.503/s, 149.503/s/gpu LR: 0.000129 Logit Scale: 26.336 Contrastive_loss: 0.26403 (0.17956) Loss: 0.26403 (0.17956) 2025-03-19,12:38:31 | INFO | Train Epoch: 12 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 150.564/s, 150.564/s/gpu LR: 0.000129 Logit Scale: 26.312 Contrastive_loss: 0.20219 (0.17978) Loss: 0.20219 (0.17978) 2025-03-19,12:38:52 | INFO | Train Epoch: 12 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.213, 150.929/s, 150.929/s/gpu LR: 0.000129 Logit Scale: 26.289 Contrastive_loss: 0.23427 (0.18028) Loss: 0.23427 (0.18028) 2025-03-19,12:39:14 | INFO | Train Epoch: 12 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 149.372/s, 149.372/s/gpu LR: 0.000129 Logit Scale: 26.270 Contrastive_loss: 0.27839 (0.18118) Loss: 0.27839 (0.18118) 2025-03-19,12:39:35 | INFO | Train Epoch: 12 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 150.445/s, 150.445/s/gpu LR: 0.000129 Logit Scale: 26.241 Contrastive_loss: 0.11832 (0.18061) Loss: 0.11832 (0.18061) 2025-03-19,12:39:57 | INFO | Train Epoch: 12 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.165/s, 149.165/s/gpu LR: 0.000129 Logit Scale: 26.312 Contrastive_loss: 0.11621 (0.18003) Loss: 0.11621 (0.18003) 2025-03-19,12:40:18 | INFO | Train Epoch: 12 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 147.804/s, 147.804/s/gpu LR: 0.000129 Logit Scale: 26.271 Contrastive_loss: 0.21555 (0.18035) Loss: 0.21555 (0.18035) 2025-03-19,12:40:40 | INFO | Train Epoch: 12 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 149.954/s, 149.954/s/gpu LR: 0.000129 Logit Scale: 26.272 Contrastive_loss: 0.068478 (0.17936) Loss: 0.068478 (0.17936) 2025-03-19,12:41:01 | INFO | Train Epoch: 12 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 146.760/s, 146.760/s/gpu LR: 0.000129 Logit Scale: 26.269 Contrastive_loss: 0.013763 (0.17790) Loss: 0.013763 (0.17790) 2025-03-19,12:41:23 | INFO | Train Epoch: 12 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.812/s, 148.812/s/gpu LR: 0.000129 Logit Scale: 26.284 Contrastive_loss: 0.060522 (0.17688) Loss: 0.060522 (0.17688) 2025-03-19,12:41:44 | INFO | Train Epoch: 12 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 150.022/s, 150.022/s/gpu LR: 0.000129 Logit Scale: 26.266 Contrastive_loss: 0.47833 (0.17948) Loss: 0.47833 (0.17948) 2025-03-19,12:42:06 | INFO | Train Epoch: 12 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 150.665/s, 150.665/s/gpu LR: 0.000129 Logit Scale: 26.282 Contrastive_loss: 0.17475 (0.17944) Loss: 0.17475 (0.17944) 2025-03-19,12:42:27 | INFO | Train Epoch: 12 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 149.227/s, 149.227/s/gpu LR: 0.000128 Logit Scale: 26.280 Contrastive_loss: 0.20928 (0.17969) Loss: 0.20928 (0.17969) 2025-03-19,12:42:49 | INFO | Train Epoch: 12 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 149.185/s, 149.185/s/gpu LR: 0.000128 Logit Scale: 26.291 Contrastive_loss: 0.0075916 (0.17825) Loss: 0.0075916 (0.17825) 2025-03-19,12:43:11 | INFO | Train Epoch: 12 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.220, 149.804/s, 149.804/s/gpu LR: 0.000128 Logit Scale: 26.311 Contrastive_loss: 0.048728 (0.17717) Loss: 0.048728 (0.17717) 2025-03-19,12:43:32 | INFO | Train Epoch: 12 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 148.624/s, 148.624/s/gpu LR: 0.000128 Logit Scale: 26.300 Contrastive_loss: 0.36435 (0.17871) Loss: 0.36435 (0.17871) 2025-03-19,12:43:53 | INFO | Train Epoch: 12 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 151.948/s, 151.948/s/gpu LR: 0.000128 Logit Scale: 26.293 Contrastive_loss: 0.24040 (0.17922) Loss: 0.24040 (0.17922) 2025-03-19,12:44:15 | INFO | Train Epoch: 12 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 148.776/s, 148.776/s/gpu LR: 0.000128 Logit Scale: 26.323 Contrastive_loss: 0.058065 (0.17824) Loss: 0.058065 (0.17824) 2025-03-19,12:44:37 | INFO | Train Epoch: 12 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 150.275/s, 150.275/s/gpu LR: 0.000128 Logit Scale: 26.283 Contrastive_loss: 0.22493 (0.17861) Loss: 0.22493 (0.17861) 2025-03-19,12:44:58 | INFO | Train Epoch: 12 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.837/s, 148.837/s/gpu LR: 0.000128 Logit Scale: 26.315 Contrastive_loss: 0.24246 (0.17912) Loss: 0.24246 (0.17912) 2025-03-19,12:45:19 | INFO | Train Epoch: 12 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.527/s, 148.527/s/gpu LR: 0.000128 Logit Scale: 26.354 Contrastive_loss: 0.059834 (0.17818) Loss: 0.059834 (0.17818) 2025-03-19,12:45:41 | INFO | Train Epoch: 12 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 150.107/s, 150.107/s/gpu LR: 0.000128 Logit Scale: 26.347 Contrastive_loss: 0.011851 (0.17687) Loss: 0.011851 (0.17687) 2025-03-19,12:46:02 | INFO | Train Epoch: 12 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 150.409/s, 150.409/s/gpu LR: 0.000128 Logit Scale: 26.351 Contrastive_loss: 0.20674 (0.17710) Loss: 0.20674 (0.17710) 2025-03-19,12:46:24 | INFO | Train Epoch: 12 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 150.409/s, 150.409/s/gpu LR: 0.000128 Logit Scale: 26.289 Contrastive_loss: 0.17210 (0.17706) Loss: 0.17210 (0.17706) 2025-03-19,12:46:45 | INFO | Train Epoch: 12 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 149.568/s, 149.568/s/gpu LR: 0.000128 Logit Scale: 26.305 Contrastive_loss: 0.26679 (0.17775) Loss: 0.26679 (0.17775) 2025-03-19,12:47:07 | INFO | Train Epoch: 12 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 148.898/s, 148.898/s/gpu LR: 0.000128 Logit Scale: 26.303 Contrastive_loss: 0.088626 (0.17707) Loss: 0.088626 (0.17707) 2025-03-19,12:47:28 | INFO | Train Epoch: 12 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 148.426/s, 148.426/s/gpu LR: 0.000128 Logit Scale: 26.331 Contrastive_loss: 0.15010 (0.17687) Loss: 0.15010 (0.17687) 2025-03-19,12:47:50 | INFO | Train Epoch: 12 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 148.760/s, 148.760/s/gpu LR: 0.000128 Logit Scale: 26.321 Contrastive_loss: 0.15357 (0.17669) Loss: 0.15357 (0.17669) 2025-03-19,12:48:12 | INFO | Train Epoch: 12 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 148.650/s, 148.650/s/gpu LR: 0.000128 Logit Scale: 26.335 Contrastive_loss: 0.030277 (0.17560) Loss: 0.030277 (0.17560) 2025-03-19,12:48:34 | INFO | Train Epoch: 12 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 148.294/s, 148.294/s/gpu LR: 0.000128 Logit Scale: 26.308 Contrastive_loss: 0.44717 (0.17761) Loss: 0.44717 (0.17761) 2025-03-19,12:48:55 | INFO | Train Epoch: 12 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 147.581/s, 147.581/s/gpu LR: 0.000128 Logit Scale: 26.322 Contrastive_loss: 0.20121 (0.17778) Loss: 0.20121 (0.17778) 2025-03-19,12:49:17 | INFO | Train Epoch: 12 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 149.664/s, 149.664/s/gpu LR: 0.000128 Logit Scale: 26.272 Contrastive_loss: 0.075403 (0.17704) Loss: 0.075403 (0.17704) 2025-03-19,12:49:38 | INFO | Train Epoch: 12 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 148.009/s, 148.009/s/gpu LR: 0.000128 Logit Scale: 26.265 Contrastive_loss: 0.17692 (0.17704) Loss: 0.17692 (0.17704) 2025-03-19,12:50:00 | INFO | Train Epoch: 12 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.219, 147.158/s, 147.158/s/gpu LR: 0.000128 Logit Scale: 26.276 Contrastive_loss: 0.22535 (0.17738) Loss: 0.22535 (0.17738) 2025-03-19,12:50:22 | INFO | Train Epoch: 12 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.218, 147.873/s, 147.873/s/gpu LR: 0.000128 Logit Scale: 26.275 Contrastive_loss: 0.10993 (0.17690) Loss: 0.10993 (0.17690) 2025-03-19,12:50:44 | INFO | Train Epoch: 12 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.218, 148.012/s, 148.012/s/gpu LR: 0.000128 Logit Scale: 26.316 Contrastive_loss: 0.10692 (0.17640) Loss: 0.10692 (0.17640) 2025-03-19,12:51:05 | INFO | Train Epoch: 12 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 149.984/s, 149.984/s/gpu LR: 0.000127 Logit Scale: 26.276 Contrastive_loss: 0.41685 (0.17810) Loss: 0.41685 (0.17810) 2025-03-19,12:51:27 | INFO | Train Epoch: 12 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 151.425/s, 151.425/s/gpu LR: 0.000127 Logit Scale: 26.268 Contrastive_loss: 0.37112 (0.17945) Loss: 0.37112 (0.17945) 2025-03-19,12:51:48 | INFO | Train Epoch: 12 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 147.869/s, 147.869/s/gpu LR: 0.000127 Logit Scale: 26.290 Contrastive_loss: 0.38201 (0.18085) Loss: 0.38201 (0.18085) 2025-03-19,12:52:10 | INFO | Train Epoch: 12 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 149.368/s, 149.368/s/gpu LR: 0.000127 Logit Scale: 26.259 Contrastive_loss: 0.069163 (0.18008) Loss: 0.069163 (0.18008) 2025-03-19,12:52:32 | INFO | Train Epoch: 12 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 149.861/s, 149.861/s/gpu LR: 0.000127 Logit Scale: 26.243 Contrastive_loss: 0.35473 (0.18128) Loss: 0.35473 (0.18128) 2025-03-19,12:52:53 | INFO | Train Epoch: 12 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 151.173/s, 151.173/s/gpu LR: 0.000127 Logit Scale: 26.264 Contrastive_loss: 0.24215 (0.18169) Loss: 0.24215 (0.18169) 2025-03-19,12:53:14 | INFO | Train Epoch: 12 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 151.063/s, 151.063/s/gpu LR: 0.000127 Logit Scale: 26.200 Contrastive_loss: 0.12694 (0.18132) Loss: 0.12694 (0.18132) 2025-03-19,12:53:36 | INFO | Train Epoch: 12 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 148.566/s, 148.566/s/gpu LR: 0.000127 Logit Scale: 26.247 Contrastive_loss: 0.32353 (0.18228) Loss: 0.32353 (0.18228) 2025-03-19,12:53:57 | INFO | Train Epoch: 12 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 150.325/s, 150.325/s/gpu LR: 0.000127 Logit Scale: 26.221 Contrastive_loss: 0.27455 (0.18289) Loss: 0.27455 (0.18289) 2025-03-19,12:54:18 | INFO | Train Epoch: 12 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 151.308/s, 151.308/s/gpu LR: 0.000127 Logit Scale: 26.258 Contrastive_loss: 0.20087 (0.18301) Loss: 0.20087 (0.18301) 2025-03-19,12:54:40 | INFO | Train Epoch: 12 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.311/s, 149.311/s/gpu LR: 0.000127 Logit Scale: 26.253 Contrastive_loss: 0.27027 (0.18359) Loss: 0.27027 (0.18359) 2025-03-19,12:55:01 | INFO | Train Epoch: 12 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 152.227/s, 152.227/s/gpu LR: 0.000127 Logit Scale: 26.262 Contrastive_loss: 0.13865 (0.18329) Loss: 0.13865 (0.18329) 2025-03-19,12:55:22 | INFO | Train Epoch: 12 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 150.018/s, 150.018/s/gpu LR: 0.000127 Logit Scale: 26.263 Contrastive_loss: 0.065893 (0.18253) Loss: 0.065893 (0.18253) 2025-03-19,12:55:44 | INFO | Train Epoch: 12 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 144.973/s, 144.973/s/gpu LR: 0.000127 Logit Scale: 26.262 Contrastive_loss: 0.19537 (0.18261) Loss: 0.19537 (0.18261) 2025-03-19,12:56:06 | INFO | Train Epoch: 12 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.218, 147.520/s, 147.520/s/gpu LR: 0.000127 Logit Scale: 26.260 Contrastive_loss: 0.19596 (0.18270) Loss: 0.19596 (0.18270) 2025-03-19,12:56:27 | INFO | Train Epoch: 12 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 147.623/s, 147.623/s/gpu LR: 0.000127 Logit Scale: 26.208 Contrastive_loss: 0.041657 (0.18180) Loss: 0.041657 (0.18180) 2025-03-19,12:56:49 | INFO | Train Epoch: 12 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 149.663/s, 149.663/s/gpu LR: 0.000127 Logit Scale: 26.225 Contrastive_loss: 0.16486 (0.18169) Loss: 0.16486 (0.18169) 2025-03-19,12:57:11 | INFO | Train Epoch: 12 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.188/s, 149.188/s/gpu LR: 0.000127 Logit Scale: 26.232 Contrastive_loss: 0.11862 (0.18130) Loss: 0.11862 (0.18130) 2025-03-19,12:57:32 | INFO | Train Epoch: 12 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.640/s, 149.640/s/gpu LR: 0.000127 Logit Scale: 26.216 Contrastive_loss: 0.039452 (0.18041) Loss: 0.039452 (0.18041) 2025-03-19,12:57:53 | INFO | Train Epoch: 12 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 149.469/s, 149.469/s/gpu LR: 0.000127 Logit Scale: 26.267 Contrastive_loss: 0.23772 (0.18077) Loss: 0.23772 (0.18077) 2025-03-19,12:58:15 | INFO | Train Epoch: 12 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.219, 145.275/s, 145.275/s/gpu LR: 0.000127 Logit Scale: 26.270 Contrastive_loss: 0.12167 (0.18040) Loss: 0.12167 (0.18040) 2025-03-19,12:58:37 | INFO | Train Epoch: 12 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 145.751/s, 145.751/s/gpu LR: 0.000127 Logit Scale: 26.291 Contrastive_loss: 0.42643 (0.18191) Loss: 0.42643 (0.18191) 2025-03-19,12:58:59 | INFO | Train Epoch: 12 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 145.762/s, 145.762/s/gpu LR: 0.000127 Logit Scale: 26.267 Contrastive_loss: 0.36280 (0.18301) Loss: 0.36280 (0.18301) 2025-03-19,12:59:21 | INFO | Train Epoch: 12 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 145.910/s, 145.910/s/gpu LR: 0.000126 Logit Scale: 26.266 Contrastive_loss: 0.079452 (0.18239) Loss: 0.079452 (0.18239) 2025-03-19,12:59:42 | INFO | Train Epoch: 12 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 147.687/s, 147.687/s/gpu LR: 0.000126 Logit Scale: 26.258 Contrastive_loss: 0.18777 (0.18242) Loss: 0.18777 (0.18242) 2025-03-19,13:00:04 | INFO | Train Epoch: 12 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 148.224/s, 148.224/s/gpu LR: 0.000126 Logit Scale: 26.270 Contrastive_loss: 0.27542 (0.18298) Loss: 0.27542 (0.18298) 2025-03-19,13:00:25 | INFO | Train Epoch: 12 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 146.987/s, 146.987/s/gpu LR: 0.000126 Logit Scale: 26.280 Contrastive_loss: 0.0035944 (0.18191) Loss: 0.0035944 (0.18191) 2025-03-19,13:00:47 | INFO | Train Epoch: 12 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 148.236/s, 148.236/s/gpu LR: 0.000126 Logit Scale: 26.276 Contrastive_loss: 0.068347 (0.18124) Loss: 0.068347 (0.18124) 2025-03-19,13:01:09 | INFO | Train Epoch: 12 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 147.631/s, 147.631/s/gpu LR: 0.000126 Logit Scale: 26.291 Contrastive_loss: 0.12097 (0.18088) Loss: 0.12097 (0.18088) 2025-03-19,13:01:31 | INFO | Train Epoch: 12 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.218, 147.665/s, 147.665/s/gpu LR: 0.000126 Logit Scale: 26.276 Contrastive_loss: 0.50569 (0.18278) Loss: 0.50569 (0.18278) 2025-03-19,13:01:53 | INFO | Train Epoch: 12 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 147.010/s, 147.010/s/gpu LR: 0.000126 Logit Scale: 26.247 Contrastive_loss: 0.65365 (0.18552) Loss: 0.65365 (0.18552) 2025-03-19,13:02:14 | INFO | Train Epoch: 12 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 149.336/s, 149.336/s/gpu LR: 0.000126 Logit Scale: 26.280 Contrastive_loss: 0.21170 (0.18567) Loss: 0.21170 (0.18567) 2025-03-19,13:02:36 | INFO | Train Epoch: 12 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 147.420/s, 147.420/s/gpu LR: 0.000126 Logit Scale: 26.294 Contrastive_loss: 0.17193 (0.18559) Loss: 0.17193 (0.18559) 2025-03-19,13:02:58 | INFO | Train Epoch: 12 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.133/s, 149.133/s/gpu LR: 0.000126 Logit Scale: 26.289 Contrastive_loss: 0.22625 (0.18582) Loss: 0.22625 (0.18582) 2025-03-19,13:03:19 | INFO | Train Epoch: 12 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.082/s, 149.082/s/gpu LR: 0.000126 Logit Scale: 26.307 Contrastive_loss: 0.31180 (0.18654) Loss: 0.31180 (0.18654) 2025-03-19,13:03:41 | INFO | Train Epoch: 12 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 147.886/s, 147.886/s/gpu LR: 0.000126 Logit Scale: 26.297 Contrastive_loss: 0.0032405 (0.18550) Loss: 0.0032405 (0.18550) 2025-03-19,13:04:02 | INFO | Train Epoch: 12 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 148.350/s, 148.350/s/gpu LR: 0.000126 Logit Scale: 26.270 Contrastive_loss: 0.31562 (0.18623) Loss: 0.31562 (0.18623) 2025-03-19,13:04:24 | INFO | Train Epoch: 12 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 148.529/s, 148.529/s/gpu LR: 0.000126 Logit Scale: 26.289 Contrastive_loss: 0.22417 (0.18645) Loss: 0.22417 (0.18645) 2025-03-19,13:04:45 | INFO | Train Epoch: 12 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 149.236/s, 149.236/s/gpu LR: 0.000126 Logit Scale: 26.341 Contrastive_loss: 0.14542 (0.18622) Loss: 0.14542 (0.18622) 2025-03-19,13:05:07 | INFO | Train Epoch: 12 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 149.611/s, 149.611/s/gpu LR: 0.000126 Logit Scale: 26.369 Contrastive_loss: 0.029119 (0.18535) Loss: 0.029119 (0.18535) 2025-03-19,13:05:28 | INFO | Train Epoch: 12 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 149.292/s, 149.292/s/gpu LR: 0.000126 Logit Scale: 26.340 Contrastive_loss: 0.35086 (0.18626) Loss: 0.35086 (0.18626) 2025-03-19,13:05:50 | INFO | Train Epoch: 12 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.605/s, 149.605/s/gpu LR: 0.000126 Logit Scale: 26.324 Contrastive_loss: 0.29655 (0.18686) Loss: 0.29655 (0.18686) 2025-03-19,13:06:12 | INFO | Train Epoch: 12 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.903/s, 148.903/s/gpu LR: 0.000126 Logit Scale: 26.298 Contrastive_loss: 0.18819 (0.18687) Loss: 0.18819 (0.18687) 2025-03-19,13:06:33 | INFO | Train Epoch: 12 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.222/s, 149.222/s/gpu LR: 0.000126 Logit Scale: 26.334 Contrastive_loss: 0.051342 (0.18614) Loss: 0.051342 (0.18614) 2025-03-19,13:06:55 | INFO | Train Epoch: 12 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 148.902/s, 148.902/s/gpu LR: 0.000126 Logit Scale: 26.343 Contrastive_loss: 0.23848 (0.18642) Loss: 0.23848 (0.18642) 2025-03-19,13:07:16 | INFO | Train Epoch: 12 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 149.695/s, 149.695/s/gpu LR: 0.000126 Logit Scale: 26.298 Contrastive_loss: 0.078782 (0.18584) Loss: 0.078782 (0.18584) 2025-03-19,13:07:38 | INFO | Train Epoch: 12 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 149.613/s, 149.613/s/gpu LR: 0.000125 Logit Scale: 26.325 Contrastive_loss: 0.056982 (0.18516) Loss: 0.056982 (0.18516) 2025-03-19,13:07:59 | INFO | Train Epoch: 12 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 150.184/s, 150.184/s/gpu LR: 0.000125 Logit Scale: 26.319 Contrastive_loss: 0.36775 (0.18612) Loss: 0.36775 (0.18612) 2025-03-19,13:08:21 | INFO | Train Epoch: 12 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.496/s, 149.496/s/gpu LR: 0.000125 Logit Scale: 26.294 Contrastive_loss: 0.19489 (0.18617) Loss: 0.19489 (0.18617) 2025-03-19,13:08:42 | INFO | Train Epoch: 12 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.350/s, 148.350/s/gpu LR: 0.000125 Logit Scale: 26.303 Contrastive_loss: 0.30725 (0.18680) Loss: 0.30725 (0.18680) 2025-03-19,13:09:04 | INFO | Train Epoch: 12 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 147.336/s, 147.336/s/gpu LR: 0.000125 Logit Scale: 26.283 Contrastive_loss: 0.096924 (0.18634) Loss: 0.096924 (0.18634) 2025-03-19,13:09:26 | INFO | Train Epoch: 12 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 146.946/s, 146.946/s/gpu LR: 0.000125 Logit Scale: 26.307 Contrastive_loss: 0.24453 (0.18664) Loss: 0.24453 (0.18664) 2025-03-19,13:09:47 | INFO | Train Epoch: 12 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.081/s, 149.081/s/gpu LR: 0.000125 Logit Scale: 26.329 Contrastive_loss: 0.40377 (0.18776) Loss: 0.40377 (0.18776) 2025-03-19,13:10:09 | INFO | Train Epoch: 12 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.205/s, 149.205/s/gpu LR: 0.000125 Logit Scale: 26.273 Contrastive_loss: 0.076671 (0.18719) Loss: 0.076671 (0.18719) 2025-03-19,13:10:30 | INFO | Train Epoch: 12 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 146.809/s, 146.809/s/gpu LR: 0.000125 Logit Scale: 26.290 Contrastive_loss: 0.11274 (0.18681) Loss: 0.11274 (0.18681) 2025-03-19,13:10:52 | INFO | Train Epoch: 12 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.217, 147.879/s, 147.879/s/gpu LR: 0.000125 Logit Scale: 26.319 Contrastive_loss: 0.23916 (0.18707) Loss: 0.23916 (0.18707) 2025-03-19,13:11:14 | INFO | Train Epoch: 12 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.217, 149.225/s, 149.225/s/gpu LR: 0.000125 Logit Scale: 26.253 Contrastive_loss: 0.070164 (0.18648) Loss: 0.070164 (0.18648) 2025-03-19,13:11:35 | INFO | Train Epoch: 12 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.212, 151.877/s, 151.877/s/gpu LR: 0.000125 Logit Scale: 26.266 Contrastive_loss: 0.20949 (0.18660) Loss: 0.20949 (0.18660) 2025-03-19,13:11:56 | INFO | Train Epoch: 12 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.213, 151.712/s, 151.712/s/gpu LR: 0.000125 Logit Scale: 26.297 Contrastive_loss: 0.37608 (0.18755) Loss: 0.37608 (0.18755) 2025-03-19,13:12:18 | INFO | Train Epoch: 12 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.213, 150.096/s, 150.096/s/gpu LR: 0.000125 Logit Scale: 26.317 Contrastive_loss: 0.19369 (0.18758) Loss: 0.19369 (0.18758) 2025-03-19,13:12:39 | INFO | Train Epoch: 12 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.212, 149.667/s, 149.667/s/gpu LR: 0.000125 Logit Scale: 26.305 Contrastive_loss: 0.27296 (0.18800) Loss: 0.27296 (0.18800) 2025-03-19,13:13:00 | INFO | Train Epoch: 12 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.212, 151.438/s, 151.438/s/gpu LR: 0.000125 Logit Scale: 26.309 Contrastive_loss: 0.41465 (0.18912) Loss: 0.41465 (0.18912) 2025-03-19,13:13:21 | INFO | Train Epoch: 12 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.213, 150.652/s, 150.652/s/gpu LR: 0.000125 Logit Scale: 26.302 Contrastive_loss: 0.31083 (0.18971) Loss: 0.31083 (0.18971) 2025-03-19,13:13:43 | INFO | Train Epoch: 12 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 149.614/s, 149.614/s/gpu LR: 0.000125 Logit Scale: 26.311 Contrastive_loss: 0.045573 (0.18901) Loss: 0.045573 (0.18901) 2025-03-19,13:14:04 | INFO | Train Epoch: 12 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 150.297/s, 150.297/s/gpu LR: 0.000125 Logit Scale: 26.304 Contrastive_loss: 0.15938 (0.18887) Loss: 0.15938 (0.18887) 2025-03-19,13:14:25 | INFO | Train Epoch: 12 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 152.672/s, 152.672/s/gpu LR: 0.000125 Logit Scale: 26.329 Contrastive_loss: 0.052002 (0.18820) Loss: 0.052002 (0.18820) 2025-03-19,13:14:47 | INFO | Train Epoch: 12 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.213, 148.880/s, 148.880/s/gpu LR: 0.000125 Logit Scale: 26.335 Contrastive_loss: 0.20980 (0.18831) Loss: 0.20980 (0.18831) 2025-03-19,13:15:08 | INFO | Train Epoch: 12 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 147.052/s, 147.052/s/gpu LR: 0.000125 Logit Scale: 26.377 Contrastive_loss: 0.29924 (0.18884) Loss: 0.29924 (0.18884) 2025-03-19,13:15:30 | INFO | Train Epoch: 12 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 151.513/s, 151.513/s/gpu LR: 0.000125 Logit Scale: 26.364 Contrastive_loss: 0.056548 (0.18821) Loss: 0.056548 (0.18821) 2025-03-19,13:15:51 | INFO | Train Epoch: 12 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.213, 150.374/s, 150.374/s/gpu LR: 0.000125 Logit Scale: 26.341 Contrastive_loss: 0.25050 (0.18850) Loss: 0.25050 (0.18850) 2025-03-19,13:16:13 | INFO | Train Epoch: 12 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.220, 146.385/s, 146.385/s/gpu LR: 0.000124 Logit Scale: 26.319 Contrastive_loss: 0.26679 (0.18887) Loss: 0.26679 (0.18887) 2025-03-19,13:16:35 | INFO | Train Epoch: 12 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 148.184/s, 148.184/s/gpu LR: 0.000124 Logit Scale: 26.342 Contrastive_loss: 0.16544 (0.18876) Loss: 0.16544 (0.18876) 2025-03-19,13:16:57 | INFO | Train Epoch: 12 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 145.883/s, 145.883/s/gpu LR: 0.000124 Logit Scale: 26.319 Contrastive_loss: 0.33545 (0.18945) Loss: 0.33545 (0.18945) 2025-03-19,13:17:19 | INFO | Train Epoch: 12 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.221, 145.926/s, 145.926/s/gpu LR: 0.000124 Logit Scale: 26.344 Contrastive_loss: 0.094495 (0.18901) Loss: 0.094495 (0.18901) 2025-03-19,13:17:41 | INFO | Train Epoch: 12 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.217, 149.737/s, 149.737/s/gpu LR: 0.000124 Logit Scale: 26.316 Contrastive_loss: 0.084878 (0.18852) Loss: 0.084878 (0.18852) 2025-03-19,13:18:02 | INFO | Train Epoch: 12 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 149.764/s, 149.764/s/gpu LR: 0.000124 Logit Scale: 26.311 Contrastive_loss: 0.37383 (0.18938) Loss: 0.37383 (0.18938) 2025-03-19,13:18:23 | INFO | Train Epoch: 12 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 150.019/s, 150.019/s/gpu LR: 0.000124 Logit Scale: 26.346 Contrastive_loss: 0.19402 (0.18940) Loss: 0.19402 (0.18940) 2025-03-19,13:18:45 | INFO | Train Epoch: 12 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 150.250/s, 150.250/s/gpu LR: 0.000124 Logit Scale: 26.350 Contrastive_loss: 0.20131 (0.18945) Loss: 0.20131 (0.18945) 2025-03-19,13:19:06 | INFO | Train Epoch: 12 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.466/s, 148.466/s/gpu LR: 0.000124 Logit Scale: 26.351 Contrastive_loss: 0.21834 (0.18959) Loss: 0.21834 (0.18959) 2025-03-19,13:19:29 | INFO | Train Epoch: 12 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.221, 147.478/s, 147.478/s/gpu LR: 0.000124 Logit Scale: 26.380 Contrastive_loss: 0.33691 (0.19025) Loss: 0.33691 (0.19025) 2025-03-19,13:19:50 | INFO | Train Epoch: 12 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 149.717/s, 149.717/s/gpu LR: 0.000124 Logit Scale: 26.331 Contrastive_loss: 0.057496 (0.18965) Loss: 0.057496 (0.18965) 2025-03-19,13:20:11 | INFO | Train Epoch: 12 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.076/s, 150.076/s/gpu LR: 0.000124 Logit Scale: 26.363 Contrastive_loss: 0.41268 (0.19065) Loss: 0.41268 (0.19065) 2025-03-19,13:20:33 | INFO | Train Epoch: 12 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 147.271/s, 147.271/s/gpu LR: 0.000124 Logit Scale: 26.374 Contrastive_loss: 0.25486 (0.19094) Loss: 0.25486 (0.19094) 2025-03-19,13:20:55 | INFO | Train Epoch: 12 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 144.643/s, 144.643/s/gpu LR: 0.000124 Logit Scale: 26.335 Contrastive_loss: 0.36719 (0.19172) Loss: 0.36719 (0.19172) 2025-03-19,13:21:16 | INFO | Train Epoch: 12 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 149.236/s, 149.236/s/gpu LR: 0.000124 Logit Scale: 26.343 Contrastive_loss: 0.23124 (0.19190) Loss: 0.23124 (0.19190) 2025-03-19,13:21:38 | INFO | Train Epoch: 12 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 144.804/s, 144.804/s/gpu LR: 0.000124 Logit Scale: 26.338 Contrastive_loss: 0.066018 (0.19134) Loss: 0.066018 (0.19134) 2025-03-19,13:22:00 | INFO | Train Epoch: 12 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 148.983/s, 148.983/s/gpu LR: 0.000124 Logit Scale: 26.355 Contrastive_loss: 0.29427 (0.19180) Loss: 0.29427 (0.19180) 2025-03-19,13:22:21 | INFO | Train Epoch: 12 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 149.882/s, 149.882/s/gpu LR: 0.000124 Logit Scale: 26.404 Contrastive_loss: 0.28065 (0.19218) Loss: 0.28065 (0.19218) 2025-03-19,13:22:43 | INFO | Train Epoch: 12 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 150.303/s, 150.303/s/gpu LR: 0.000124 Logit Scale: 26.370 Contrastive_loss: 0.52216 (0.19362) Loss: 0.52216 (0.19362) 2025-03-19,13:23:04 | INFO | Train Epoch: 12 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 149.948/s, 149.948/s/gpu LR: 0.000124 Logit Scale: 26.382 Contrastive_loss: 0.089645 (0.19317) Loss: 0.089645 (0.19317) 2025-03-19,13:23:25 | INFO | Train Epoch: 12 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.875/s, 149.875/s/gpu LR: 0.000124 Logit Scale: 26.390 Contrastive_loss: 0.28071 (0.19355) Loss: 0.28071 (0.19355) 2025-03-19,13:23:47 | INFO | Train Epoch: 12 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 147.810/s, 147.810/s/gpu LR: 0.000124 Logit Scale: 26.391 Contrastive_loss: 0.19927 (0.19357) Loss: 0.19927 (0.19357) 2025-03-19,13:24:08 | INFO | Train Epoch: 12 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 150.343/s, 150.343/s/gpu LR: 0.000124 Logit Scale: 26.351 Contrastive_loss: 0.069315 (0.19304) Loss: 0.069315 (0.19304) 2025-03-19,13:24:30 | INFO | Train Epoch: 12 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 150.663/s, 150.663/s/gpu LR: 0.000123 Logit Scale: 26.360 Contrastive_loss: 0.10355 (0.19266) Loss: 0.10355 (0.19266) 2025-03-19,13:24:51 | INFO | Train Epoch: 12 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 148.856/s, 148.856/s/gpu LR: 0.000123 Logit Scale: 26.353 Contrastive_loss: 0.10046 (0.19227) Loss: 0.10046 (0.19227) 2025-03-19,13:25:13 | INFO | Train Epoch: 12 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 148.442/s, 148.442/s/gpu LR: 0.000123 Logit Scale: 26.373 Contrastive_loss: 0.39944 (0.19314) Loss: 0.39944 (0.19314) 2025-03-19,13:25:34 | INFO | Train Epoch: 12 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.932/s, 149.932/s/gpu LR: 0.000123 Logit Scale: 26.382 Contrastive_loss: 0.073129 (0.19264) Loss: 0.073129 (0.19264) 2025-03-19,13:25:56 | INFO | Train Epoch: 12 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 145.976/s, 145.976/s/gpu LR: 0.000123 Logit Scale: 26.380 Contrastive_loss: 0.25978 (0.19292) Loss: 0.25978 (0.19292) 2025-03-19,13:26:17 | INFO | Train Epoch: 12 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.217, 146.578/s, 146.578/s/gpu LR: 0.000123 Logit Scale: 26.391 Contrastive_loss: 0.040554 (0.19228) Loss: 0.040554 (0.19228) 2025-03-19,13:26:25 | INFO | Train Epoch: 12 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.218, 150.257/s, 150.257/s/gpu LR: 0.000123 Logit Scale: 26.377 Contrastive_loss: 0.10349 (0.19192) Loss: 0.10349 (0.19192) 2025-03-19,13:26:25 | INFO | Eval Epoch: 13 [32 / 7443] Clip Loss: 3.192471 2025-03-19,13:26:31 | INFO | Eval Epoch: 13 [3232 / 7443] Clip Loss: 0.867067 2025-03-19,13:26:37 | INFO | Eval Epoch: 13 [6432 / 7443] Clip Loss: 0.670879 2025-03-19,13:26:40 | INFO | Eval Epoch: 13 image_to_text_mean_rank: 100.0316 image_to_text_median_rank: 7.0000 image_to_text_R@1: 0.1337 image_to_text_R@5: 0.4350 image_to_text_R@10: 0.6117 text_to_image_mean_rank: 60.3457 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1378 text_to_image_R@5: 0.4389 text_to_image_R@10: 0.6117 clip_val_loss: 0.6328 epoch: 13.0000 num_samples: 7443.0000 2025-03-19,13:27:12 | INFO | Start epoch 13 2025-03-19,13:27:13 | INFO | Train Epoch: 13 [ 32/766009 (0%)] Data (t): 0.172 Batch (t): 0.378, 84.7583/s, 84.7583/s/gpu LR: 0.000123 Logit Scale: 26.375 Contrastive_loss: 0.16442 (0.16442) Loss: 0.16442 (0.16442) 2025-03-19,13:27:34 | INFO | Train Epoch: 13 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.217, 148.145/s, 148.145/s/gpu LR: 0.000123 Logit Scale: 26.418 Contrastive_loss: 0.050319 (0.10737) Loss: 0.050319 (0.10737) 2025-03-19,13:27:56 | INFO | Train Epoch: 13 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 148.014/s, 148.014/s/gpu LR: 0.000123 Logit Scale: 26.446 Contrastive_loss: 0.21862 (0.14445) Loss: 0.21862 (0.14445) 2025-03-19,13:28:18 | INFO | Train Epoch: 13 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.218, 147.194/s, 147.194/s/gpu LR: 0.000123 Logit Scale: 26.449 Contrastive_loss: 0.29237 (0.18143) Loss: 0.29237 (0.18143) 2025-03-19,13:28:40 | INFO | Train Epoch: 13 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.217, 151.247/s, 151.247/s/gpu LR: 0.000123 Logit Scale: 26.487 Contrastive_loss: 0.41082 (0.22731) Loss: 0.41082 (0.22731) 2025-03-19,13:29:01 | INFO | Train Epoch: 13 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.378/s, 149.378/s/gpu LR: 0.000123 Logit Scale: 26.484 Contrastive_loss: 0.19440 (0.22182) Loss: 0.19440 (0.22182) 2025-03-19,13:29:22 | INFO | Train Epoch: 13 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 151.854/s, 151.854/s/gpu LR: 0.000123 Logit Scale: 26.483 Contrastive_loss: 0.15653 (0.21250) Loss: 0.15653 (0.21250) 2025-03-19,13:29:44 | INFO | Train Epoch: 13 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 149.799/s, 149.799/s/gpu LR: 0.000123 Logit Scale: 26.515 Contrastive_loss: 0.079252 (0.19584) Loss: 0.079252 (0.19584) 2025-03-19,13:30:05 | INFO | Train Epoch: 13 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 146.417/s, 146.417/s/gpu LR: 0.000123 Logit Scale: 26.527 Contrastive_loss: 0.071595 (0.18203) Loss: 0.071595 (0.18203) 2025-03-19,13:30:27 | INFO | Train Epoch: 13 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.218, 146.864/s, 146.864/s/gpu LR: 0.000123 Logit Scale: 26.538 Contrastive_loss: 0.32323 (0.19615) Loss: 0.32323 (0.19615) 2025-03-19,13:30:48 | INFO | Train Epoch: 13 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.218, 147.288/s, 147.288/s/gpu LR: 0.000123 Logit Scale: 26.519 Contrastive_loss: 0.084078 (0.18597) Loss: 0.084078 (0.18597) 2025-03-19,13:31:10 | INFO | Train Epoch: 13 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.508/s, 149.508/s/gpu LR: 0.000123 Logit Scale: 26.545 Contrastive_loss: 0.24264 (0.19069) Loss: 0.24264 (0.19069) 2025-03-19,13:31:31 | INFO | Train Epoch: 13 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 148.688/s, 148.688/s/gpu LR: 0.000123 Logit Scale: 26.538 Contrastive_loss: 0.076108 (0.18188) Loss: 0.076108 (0.18188) 2025-03-19,13:31:53 | INFO | Train Epoch: 13 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.319/s, 149.319/s/gpu LR: 0.000123 Logit Scale: 26.554 Contrastive_loss: 0.043703 (0.17201) Loss: 0.043703 (0.17201) 2025-03-19,13:32:14 | INFO | Train Epoch: 13 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 150.625/s, 150.625/s/gpu LR: 0.000123 Logit Scale: 26.566 Contrastive_loss: 0.13275 (0.16939) Loss: 0.13275 (0.16939) 2025-03-19,13:32:36 | INFO | Train Epoch: 13 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 146.089/s, 146.089/s/gpu LR: 0.000123 Logit Scale: 26.539 Contrastive_loss: 0.12540 (0.16664) Loss: 0.12540 (0.16664) 2025-03-19,13:32:57 | INFO | Train Epoch: 13 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 148.684/s, 148.684/s/gpu LR: 0.000123 Logit Scale: 26.530 Contrastive_loss: 0.14377 (0.16529) Loss: 0.14377 (0.16529) 2025-03-19,13:33:19 | INFO | Train Epoch: 13 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 150.035/s, 150.035/s/gpu LR: 0.000123 Logit Scale: 26.572 Contrastive_loss: 0.037192 (0.15818) Loss: 0.037192 (0.15818) 2025-03-19,13:33:41 | INFO | Train Epoch: 13 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 148.308/s, 148.308/s/gpu LR: 0.000122 Logit Scale: 26.572 Contrastive_loss: 0.30480 (0.16589) Loss: 0.30480 (0.16589) 2025-03-19,13:34:02 | INFO | Train Epoch: 13 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.695/s, 149.695/s/gpu LR: 0.000122 Logit Scale: 26.570 Contrastive_loss: 0.18842 (0.16702) Loss: 0.18842 (0.16702) 2025-03-19,13:34:24 | INFO | Train Epoch: 13 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.236/s, 149.236/s/gpu LR: 0.000122 Logit Scale: 26.570 Contrastive_loss: 0.029543 (0.16047) Loss: 0.029543 (0.16047) 2025-03-19,13:34:45 | INFO | Train Epoch: 13 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 148.667/s, 148.667/s/gpu LR: 0.000122 Logit Scale: 26.609 Contrastive_loss: 0.30289 (0.16695) Loss: 0.30289 (0.16695) 2025-03-19,13:35:07 | INFO | Train Epoch: 13 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 149.270/s, 149.270/s/gpu LR: 0.000122 Logit Scale: 26.559 Contrastive_loss: 0.17389 (0.16725) Loss: 0.17389 (0.16725) 2025-03-19,13:35:28 | INFO | Train Epoch: 13 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.413/s, 149.413/s/gpu LR: 0.000122 Logit Scale: 26.569 Contrastive_loss: 0.14178 (0.16619) Loss: 0.14178 (0.16619) 2025-03-19,13:35:50 | INFO | Train Epoch: 13 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 149.684/s, 149.684/s/gpu LR: 0.000122 Logit Scale: 26.571 Contrastive_loss: 0.31434 (0.17211) Loss: 0.31434 (0.17211) 2025-03-19,13:36:11 | INFO | Train Epoch: 13 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 148.589/s, 148.589/s/gpu LR: 0.000122 Logit Scale: 26.543 Contrastive_loss: 0.051190 (0.16746) Loss: 0.051190 (0.16746) 2025-03-19,13:36:33 | INFO | Train Epoch: 13 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 147.428/s, 147.428/s/gpu LR: 0.000122 Logit Scale: 26.538 Contrastive_loss: 0.34572 (0.17406) Loss: 0.34572 (0.17406) 2025-03-19,13:36:55 | INFO | Train Epoch: 13 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.220, 145.495/s, 145.495/s/gpu LR: 0.000122 Logit Scale: 26.550 Contrastive_loss: 0.075074 (0.17053) Loss: 0.075074 (0.17053) 2025-03-19,13:37:17 | INFO | Train Epoch: 13 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 149.215/s, 149.215/s/gpu LR: 0.000122 Logit Scale: 26.540 Contrastive_loss: 0.085219 (0.16759) Loss: 0.085219 (0.16759) 2025-03-19,13:37:38 | INFO | Train Epoch: 13 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 147.631/s, 147.631/s/gpu LR: 0.000122 Logit Scale: 26.529 Contrastive_loss: 0.12716 (0.16624) Loss: 0.12716 (0.16624) 2025-03-19,13:37:59 | INFO | Train Epoch: 13 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 150.167/s, 150.167/s/gpu LR: 0.000122 Logit Scale: 26.546 Contrastive_loss: 0.23394 (0.16842) Loss: 0.23394 (0.16842) 2025-03-19,13:38:21 | INFO | Train Epoch: 13 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 148.905/s, 148.905/s/gpu LR: 0.000122 Logit Scale: 26.527 Contrastive_loss: 0.078943 (0.16563) Loss: 0.078943 (0.16563) 2025-03-19,13:38:42 | INFO | Train Epoch: 13 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.699/s, 148.699/s/gpu LR: 0.000122 Logit Scale: 26.526 Contrastive_loss: 0.13327 (0.16465) Loss: 0.13327 (0.16465) 2025-03-19,13:39:04 | INFO | Train Epoch: 13 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 148.260/s, 148.260/s/gpu LR: 0.000122 Logit Scale: 26.521 Contrastive_loss: 0.10575 (0.16291) Loss: 0.10575 (0.16291) 2025-03-19,13:39:25 | INFO | Train Epoch: 13 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.555/s, 148.555/s/gpu LR: 0.000122 Logit Scale: 26.542 Contrastive_loss: 0.11500 (0.16155) Loss: 0.11500 (0.16155) 2025-03-19,13:39:47 | INFO | Train Epoch: 13 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 153.481/s, 153.481/s/gpu LR: 0.000122 Logit Scale: 26.538 Contrastive_loss: 0.047324 (0.15837) Loss: 0.047324 (0.15837) 2025-03-19,13:40:08 | INFO | Train Epoch: 13 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 149.207/s, 149.207/s/gpu LR: 0.000122 Logit Scale: 26.541 Contrastive_loss: 0.049790 (0.15544) Loss: 0.049790 (0.15544) 2025-03-19,13:40:30 | INFO | Train Epoch: 13 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 147.704/s, 147.704/s/gpu LR: 0.000122 Logit Scale: 26.535 Contrastive_loss: 0.076351 (0.15336) Loss: 0.076351 (0.15336) 2025-03-19,13:40:51 | INFO | Train Epoch: 13 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 149.620/s, 149.620/s/gpu LR: 0.000122 Logit Scale: 26.509 Contrastive_loss: 0.13544 (0.15290) Loss: 0.13544 (0.15290) 2025-03-19,13:41:13 | INFO | Train Epoch: 13 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 149.411/s, 149.411/s/gpu LR: 0.000122 Logit Scale: 26.561 Contrastive_loss: 0.067468 (0.15076) Loss: 0.067468 (0.15076) 2025-03-19,13:41:34 | INFO | Train Epoch: 13 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 149.577/s, 149.577/s/gpu LR: 0.000122 Logit Scale: 26.545 Contrastive_loss: 0.16038 (0.15100) Loss: 0.16038 (0.15100) 2025-03-19,13:41:55 | INFO | Train Epoch: 13 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 148.793/s, 148.793/s/gpu LR: 0.000121 Logit Scale: 26.559 Contrastive_loss: 0.26279 (0.15366) Loss: 0.26279 (0.15366) 2025-03-19,13:42:17 | INFO | Train Epoch: 13 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 149.179/s, 149.179/s/gpu LR: 0.000121 Logit Scale: 26.567 Contrastive_loss: 0.15571 (0.15371) Loss: 0.15571 (0.15371) 2025-03-19,13:42:38 | INFO | Train Epoch: 13 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.063/s, 149.063/s/gpu LR: 0.000121 Logit Scale: 26.557 Contrastive_loss: 0.21677 (0.15514) Loss: 0.21677 (0.15514) 2025-03-19,13:43:00 | INFO | Train Epoch: 13 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 144.371/s, 144.371/s/gpu LR: 0.000121 Logit Scale: 26.540 Contrastive_loss: 0.15696 (0.15518) Loss: 0.15696 (0.15518) 2025-03-19,13:43:22 | INFO | Train Epoch: 13 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.219, 147.238/s, 147.238/s/gpu LR: 0.000121 Logit Scale: 26.544 Contrastive_loss: 0.17193 (0.15554) Loss: 0.17193 (0.15554) 2025-03-19,13:43:44 | INFO | Train Epoch: 13 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.220, 147.090/s, 147.090/s/gpu LR: 0.000121 Logit Scale: 26.540 Contrastive_loss: 0.041375 (0.15311) Loss: 0.041375 (0.15311) 2025-03-19,13:44:06 | INFO | Train Epoch: 13 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.255/s, 149.255/s/gpu LR: 0.000121 Logit Scale: 26.568 Contrastive_loss: 0.10982 (0.15221) Loss: 0.10982 (0.15221) 2025-03-19,13:44:27 | INFO | Train Epoch: 13 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.085/s, 149.085/s/gpu LR: 0.000121 Logit Scale: 26.533 Contrastive_loss: 0.36172 (0.15649) Loss: 0.36172 (0.15649) 2025-03-19,13:44:49 | INFO | Train Epoch: 13 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.936/s, 149.936/s/gpu LR: 0.000121 Logit Scale: 26.537 Contrastive_loss: 0.12925 (0.15594) Loss: 0.12925 (0.15594) 2025-03-19,13:45:10 | INFO | Train Epoch: 13 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 148.166/s, 148.166/s/gpu LR: 0.000121 Logit Scale: 26.524 Contrastive_loss: 0.097574 (0.15480) Loss: 0.097574 (0.15480) 2025-03-19,13:45:32 | INFO | Train Epoch: 13 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 149.719/s, 149.719/s/gpu LR: 0.000121 Logit Scale: 26.499 Contrastive_loss: 0.075355 (0.15327) Loss: 0.075355 (0.15327) 2025-03-19,13:45:53 | INFO | Train Epoch: 13 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 148.759/s, 148.759/s/gpu LR: 0.000121 Logit Scale: 26.539 Contrastive_loss: 0.15737 (0.15335) Loss: 0.15737 (0.15335) 2025-03-19,13:46:15 | INFO | Train Epoch: 13 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.217, 148.995/s, 148.995/s/gpu LR: 0.000121 Logit Scale: 26.540 Contrastive_loss: 0.062664 (0.15167) Loss: 0.062664 (0.15167) 2025-03-19,13:46:36 | INFO | Train Epoch: 13 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.469/s, 149.469/s/gpu LR: 0.000121 Logit Scale: 26.522 Contrastive_loss: 0.22639 (0.15303) Loss: 0.22639 (0.15303) 2025-03-19,13:46:58 | INFO | Train Epoch: 13 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.423/s, 149.423/s/gpu LR: 0.000121 Logit Scale: 26.532 Contrastive_loss: 0.12645 (0.15255) Loss: 0.12645 (0.15255) 2025-03-19,13:47:19 | INFO | Train Epoch: 13 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 147.860/s, 147.860/s/gpu LR: 0.000121 Logit Scale: 26.534 Contrastive_loss: 0.22708 (0.15386) Loss: 0.22708 (0.15386) 2025-03-19,13:47:41 | INFO | Train Epoch: 13 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 149.228/s, 149.228/s/gpu LR: 0.000121 Logit Scale: 26.529 Contrastive_loss: 0.14125 (0.15364) Loss: 0.14125 (0.15364) 2025-03-19,13:48:02 | INFO | Train Epoch: 13 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 148.815/s, 148.815/s/gpu LR: 0.000121 Logit Scale: 26.533 Contrastive_loss: 0.082198 (0.15243) Loss: 0.082198 (0.15243) 2025-03-19,13:48:24 | INFO | Train Epoch: 13 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 149.467/s, 149.467/s/gpu LR: 0.000121 Logit Scale: 26.560 Contrastive_loss: 0.0070443 (0.15001) Loss: 0.0070443 (0.15001) 2025-03-19,13:48:45 | INFO | Train Epoch: 13 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 147.661/s, 147.661/s/gpu LR: 0.000121 Logit Scale: 26.580 Contrastive_loss: 0.22586 (0.15125) Loss: 0.22586 (0.15125) 2025-03-19,13:49:07 | INFO | Train Epoch: 13 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 150.044/s, 150.044/s/gpu LR: 0.000121 Logit Scale: 26.595 Contrastive_loss: 0.56496 (0.15793) Loss: 0.56496 (0.15793) 2025-03-19,13:49:28 | INFO | Train Epoch: 13 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 148.739/s, 148.739/s/gpu LR: 0.000121 Logit Scale: 26.573 Contrastive_loss: 0.018627 (0.15571) Loss: 0.018627 (0.15571) 2025-03-19,13:49:50 | INFO | Train Epoch: 13 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 149.820/s, 149.820/s/gpu LR: 0.000121 Logit Scale: 26.584 Contrastive_loss: 0.27332 (0.15755) Loss: 0.27332 (0.15755) 2025-03-19,13:50:11 | INFO | Train Epoch: 13 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.146/s, 149.146/s/gpu LR: 0.000120 Logit Scale: 26.563 Contrastive_loss: 0.048658 (0.15588) Loss: 0.048658 (0.15588) 2025-03-19,13:50:33 | INFO | Train Epoch: 13 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.718/s, 149.718/s/gpu LR: 0.000120 Logit Scale: 26.582 Contrastive_loss: 0.40978 (0.15972) Loss: 0.40978 (0.15972) 2025-03-19,13:50:54 | INFO | Train Epoch: 13 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.933/s, 148.933/s/gpu LR: 0.000120 Logit Scale: 26.591 Contrastive_loss: 0.053314 (0.15814) Loss: 0.053314 (0.15814) 2025-03-19,13:51:16 | INFO | Train Epoch: 13 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.798/s, 149.798/s/gpu LR: 0.000120 Logit Scale: 26.577 Contrastive_loss: 0.20293 (0.15879) Loss: 0.20293 (0.15879) 2025-03-19,13:51:37 | INFO | Train Epoch: 13 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.440/s, 149.440/s/gpu LR: 0.000120 Logit Scale: 26.567 Contrastive_loss: 0.17437 (0.15902) Loss: 0.17437 (0.15902) 2025-03-19,13:51:59 | INFO | Train Epoch: 13 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.542/s, 148.542/s/gpu LR: 0.000120 Logit Scale: 26.556 Contrastive_loss: 0.091649 (0.15806) Loss: 0.091649 (0.15806) 2025-03-19,13:52:20 | INFO | Train Epoch: 13 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.217, 148.017/s, 148.017/s/gpu LR: 0.000120 Logit Scale: 26.547 Contrastive_loss: 0.0054127 (0.15591) Loss: 0.0054127 (0.15591) 2025-03-19,13:52:42 | INFO | Train Epoch: 13 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 148.399/s, 148.399/s/gpu LR: 0.000120 Logit Scale: 26.527 Contrastive_loss: 0.25532 (0.15729) Loss: 0.25532 (0.15729) 2025-03-19,13:53:04 | INFO | Train Epoch: 13 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 147.698/s, 147.698/s/gpu LR: 0.000120 Logit Scale: 26.530 Contrastive_loss: 0.21402 (0.15807) Loss: 0.21402 (0.15807) 2025-03-19,13:53:25 | INFO | Train Epoch: 13 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 150.495/s, 150.495/s/gpu LR: 0.000120 Logit Scale: 26.509 Contrastive_loss: 0.22427 (0.15896) Loss: 0.22427 (0.15896) 2025-03-19,13:53:47 | INFO | Train Epoch: 13 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 149.627/s, 149.627/s/gpu LR: 0.000120 Logit Scale: 26.556 Contrastive_loss: 0.044961 (0.15744) Loss: 0.044961 (0.15744) 2025-03-19,13:54:08 | INFO | Train Epoch: 13 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 152.749/s, 152.749/s/gpu LR: 0.000120 Logit Scale: 26.570 Contrastive_loss: 0.13049 (0.15709) Loss: 0.13049 (0.15709) 2025-03-19,13:54:30 | INFO | Train Epoch: 13 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 149.312/s, 149.312/s/gpu LR: 0.000120 Logit Scale: 26.563 Contrastive_loss: 0.17252 (0.15729) Loss: 0.17252 (0.15729) 2025-03-19,13:54:51 | INFO | Train Epoch: 13 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 148.002/s, 148.002/s/gpu LR: 0.000120 Logit Scale: 26.552 Contrastive_loss: 0.13833 (0.15704) Loss: 0.13833 (0.15704) 2025-03-19,13:55:13 | INFO | Train Epoch: 13 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.401/s, 147.401/s/gpu LR: 0.000120 Logit Scale: 26.562 Contrastive_loss: 0.12045 (0.15658) Loss: 0.12045 (0.15658) 2025-03-19,13:55:34 | INFO | Train Epoch: 13 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 147.792/s, 147.792/s/gpu LR: 0.000120 Logit Scale: 26.619 Contrastive_loss: 0.10331 (0.15591) Loss: 0.10331 (0.15591) 2025-03-19,13:55:56 | INFO | Train Epoch: 13 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 147.101/s, 147.101/s/gpu LR: 0.000120 Logit Scale: 26.623 Contrastive_loss: 0.58792 (0.16125) Loss: 0.58792 (0.16125) 2025-03-19,13:56:18 | INFO | Train Epoch: 13 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.219, 140.931/s, 140.931/s/gpu LR: 0.000120 Logit Scale: 26.575 Contrastive_loss: 0.24249 (0.16224) Loss: 0.24249 (0.16224) 2025-03-19,13:56:40 | INFO | Train Epoch: 13 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 147.908/s, 147.908/s/gpu LR: 0.000120 Logit Scale: 26.607 Contrastive_loss: 0.11687 (0.16169) Loss: 0.11687 (0.16169) 2025-03-19,13:57:01 | INFO | Train Epoch: 13 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.839/s, 148.839/s/gpu LR: 0.000120 Logit Scale: 26.595 Contrastive_loss: 0.12824 (0.16129) Loss: 0.12824 (0.16129) 2025-03-19,13:57:23 | INFO | Train Epoch: 13 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 147.227/s, 147.227/s/gpu LR: 0.000120 Logit Scale: 26.579 Contrastive_loss: 0.12771 (0.16090) Loss: 0.12771 (0.16090) 2025-03-19,13:57:44 | INFO | Train Epoch: 13 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 150.022/s, 150.022/s/gpu LR: 0.000120 Logit Scale: 26.548 Contrastive_loss: 0.15659 (0.16085) Loss: 0.15659 (0.16085) 2025-03-19,13:58:06 | INFO | Train Epoch: 13 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.959/s, 148.959/s/gpu LR: 0.000120 Logit Scale: 26.556 Contrastive_loss: 0.19727 (0.16127) Loss: 0.19727 (0.16127) 2025-03-19,13:58:27 | INFO | Train Epoch: 13 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.456/s, 148.456/s/gpu LR: 0.000119 Logit Scale: 26.527 Contrastive_loss: 0.056582 (0.16008) Loss: 0.056582 (0.16008) 2025-03-19,13:58:49 | INFO | Train Epoch: 13 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.217, 150.110/s, 150.110/s/gpu LR: 0.000119 Logit Scale: 26.574 Contrastive_loss: 0.16475 (0.16013) Loss: 0.16475 (0.16013) 2025-03-19,13:59:11 | INFO | Train Epoch: 13 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 149.794/s, 149.794/s/gpu LR: 0.000119 Logit Scale: 26.521 Contrastive_loss: 0.012926 (0.15849) Loss: 0.012926 (0.15849) 2025-03-19,13:59:32 | INFO | Train Epoch: 13 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.452/s, 149.452/s/gpu LR: 0.000119 Logit Scale: 26.517 Contrastive_loss: 0.12928 (0.15817) Loss: 0.12928 (0.15817) 2025-03-19,13:59:53 | INFO | Train Epoch: 13 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.405/s, 149.405/s/gpu LR: 0.000119 Logit Scale: 26.556 Contrastive_loss: 0.21394 (0.15878) Loss: 0.21394 (0.15878) 2025-03-19,14:00:15 | INFO | Train Epoch: 13 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.143/s, 149.143/s/gpu LR: 0.000119 Logit Scale: 26.564 Contrastive_loss: 0.25924 (0.15986) Loss: 0.25924 (0.15986) 2025-03-19,14:00:36 | INFO | Train Epoch: 13 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 147.886/s, 147.886/s/gpu LR: 0.000119 Logit Scale: 26.539 Contrastive_loss: 0.15741 (0.15983) Loss: 0.15741 (0.15983) 2025-03-19,14:00:58 | INFO | Train Epoch: 13 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.341/s, 149.341/s/gpu LR: 0.000119 Logit Scale: 26.512 Contrastive_loss: 0.10127 (0.15922) Loss: 0.10127 (0.15922) 2025-03-19,14:01:19 | INFO | Train Epoch: 13 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 149.746/s, 149.746/s/gpu LR: 0.000119 Logit Scale: 26.508 Contrastive_loss: 0.038398 (0.15796) Loss: 0.038398 (0.15796) 2025-03-19,14:01:41 | INFO | Train Epoch: 13 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 148.917/s, 148.917/s/gpu LR: 0.000119 Logit Scale: 26.519 Contrastive_loss: 0.14146 (0.15779) Loss: 0.14146 (0.15779) 2025-03-19,14:02:03 | INFO | Train Epoch: 13 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 150.537/s, 150.537/s/gpu LR: 0.000119 Logit Scale: 26.540 Contrastive_loss: 0.29921 (0.15923) Loss: 0.29921 (0.15923) 2025-03-19,14:02:24 | INFO | Train Epoch: 13 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.254/s, 149.254/s/gpu LR: 0.000119 Logit Scale: 26.543 Contrastive_loss: 0.029879 (0.15792) Loss: 0.029879 (0.15792) 2025-03-19,14:02:46 | INFO | Train Epoch: 13 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.769/s, 148.769/s/gpu LR: 0.000119 Logit Scale: 26.522 Contrastive_loss: 0.0080916 (0.15643) Loss: 0.0080916 (0.15643) 2025-03-19,14:03:07 | INFO | Train Epoch: 13 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.088/s, 149.088/s/gpu LR: 0.000119 Logit Scale: 26.525 Contrastive_loss: 0.25536 (0.15741) Loss: 0.25536 (0.15741) 2025-03-19,14:03:29 | INFO | Train Epoch: 13 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.217, 144.375/s, 144.375/s/gpu LR: 0.000119 Logit Scale: 26.544 Contrastive_loss: 0.079157 (0.15664) Loss: 0.079157 (0.15664) 2025-03-19,14:03:50 | INFO | Train Epoch: 13 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 149.604/s, 149.604/s/gpu LR: 0.000119 Logit Scale: 26.563 Contrastive_loss: 0.25748 (0.15762) Loss: 0.25748 (0.15762) 2025-03-19,14:04:12 | INFO | Train Epoch: 13 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.030/s, 149.030/s/gpu LR: 0.000119 Logit Scale: 26.576 Contrastive_loss: 0.10156 (0.15708) Loss: 0.10156 (0.15708) 2025-03-19,14:04:34 | INFO | Train Epoch: 13 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 145.437/s, 145.437/s/gpu LR: 0.000119 Logit Scale: 26.512 Contrastive_loss: 0.31983 (0.15863) Loss: 0.31983 (0.15863) 2025-03-19,14:04:55 | INFO | Train Epoch: 13 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.217, 149.921/s, 149.921/s/gpu LR: 0.000119 Logit Scale: 26.504 Contrastive_loss: 0.39530 (0.16086) Loss: 0.39530 (0.16086) 2025-03-19,14:05:17 | INFO | Train Epoch: 13 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 149.570/s, 149.570/s/gpu LR: 0.000119 Logit Scale: 26.473 Contrastive_loss: 0.15576 (0.16081) Loss: 0.15576 (0.16081) 2025-03-19,14:05:38 | INFO | Train Epoch: 13 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.214/s, 149.214/s/gpu LR: 0.000119 Logit Scale: 26.465 Contrastive_loss: 0.22689 (0.16143) Loss: 0.22689 (0.16143) 2025-03-19,14:06:00 | INFO | Train Epoch: 13 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 148.984/s, 148.984/s/gpu LR: 0.000119 Logit Scale: 26.464 Contrastive_loss: 0.094684 (0.16081) Loss: 0.094684 (0.16081) 2025-03-19,14:06:21 | INFO | Train Epoch: 13 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 148.756/s, 148.756/s/gpu LR: 0.000119 Logit Scale: 26.450 Contrastive_loss: 0.12151 (0.16046) Loss: 0.12151 (0.16046) 2025-03-19,14:06:43 | INFO | Train Epoch: 13 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 148.766/s, 148.766/s/gpu LR: 0.000118 Logit Scale: 26.485 Contrastive_loss: 0.18640 (0.16069) Loss: 0.18640 (0.16069) 2025-03-19,14:07:04 | INFO | Train Epoch: 13 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.496/s, 149.496/s/gpu LR: 0.000118 Logit Scale: 26.519 Contrastive_loss: 0.13702 (0.16048) Loss: 0.13702 (0.16048) 2025-03-19,14:07:26 | INFO | Train Epoch: 13 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 147.452/s, 147.452/s/gpu LR: 0.000118 Logit Scale: 26.501 Contrastive_loss: 0.097387 (0.15992) Loss: 0.097387 (0.15992) 2025-03-19,14:07:48 | INFO | Train Epoch: 13 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 148.577/s, 148.577/s/gpu LR: 0.000118 Logit Scale: 26.510 Contrastive_loss: 0.38233 (0.16187) Loss: 0.38233 (0.16187) 2025-03-19,14:08:09 | INFO | Train Epoch: 13 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 143.040/s, 143.040/s/gpu LR: 0.000118 Logit Scale: 26.485 Contrastive_loss: 0.038476 (0.16080) Loss: 0.038476 (0.16080) 2025-03-19,14:08:32 | INFO | Train Epoch: 13 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.221, 146.057/s, 146.057/s/gpu LR: 0.000118 Logit Scale: 26.497 Contrastive_loss: 0.22365 (0.16134) Loss: 0.22365 (0.16134) 2025-03-19,14:08:53 | INFO | Train Epoch: 13 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.219, 148.068/s, 148.068/s/gpu LR: 0.000118 Logit Scale: 26.495 Contrastive_loss: 0.23017 (0.16193) Loss: 0.23017 (0.16193) 2025-03-19,14:09:15 | INFO | Train Epoch: 13 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.219, 146.470/s, 146.470/s/gpu LR: 0.000118 Logit Scale: 26.460 Contrastive_loss: 0.26080 (0.16277) Loss: 0.26080 (0.16277) 2025-03-19,14:09:37 | INFO | Train Epoch: 13 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 149.139/s, 149.139/s/gpu LR: 0.000118 Logit Scale: 26.484 Contrastive_loss: 0.12823 (0.16248) Loss: 0.12823 (0.16248) 2025-03-19,14:09:58 | INFO | Train Epoch: 13 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 151.003/s, 151.003/s/gpu LR: 0.000118 Logit Scale: 26.497 Contrastive_loss: 0.34417 (0.16399) Loss: 0.34417 (0.16399) 2025-03-19,14:10:20 | INFO | Train Epoch: 13 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.213, 151.732/s, 151.732/s/gpu LR: 0.000118 Logit Scale: 26.511 Contrastive_loss: 0.092162 (0.16340) Loss: 0.092162 (0.16340) 2025-03-19,14:10:41 | INFO | Train Epoch: 13 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 151.215/s, 151.215/s/gpu LR: 0.000118 Logit Scale: 26.500 Contrastive_loss: 0.17038 (0.16345) Loss: 0.17038 (0.16345) 2025-03-19,14:11:03 | INFO | Train Epoch: 13 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 147.942/s, 147.942/s/gpu LR: 0.000118 Logit Scale: 26.534 Contrastive_loss: 0.10363 (0.16297) Loss: 0.10363 (0.16297) 2025-03-19,14:11:25 | INFO | Train Epoch: 13 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.218, 147.628/s, 147.628/s/gpu LR: 0.000118 Logit Scale: 26.536 Contrastive_loss: 0.49577 (0.16565) Loss: 0.49577 (0.16565) 2025-03-19,14:11:46 | INFO | Train Epoch: 13 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.217, 149.432/s, 149.432/s/gpu LR: 0.000118 Logit Scale: 26.553 Contrastive_loss: 0.10345 (0.16515) Loss: 0.10345 (0.16515) 2025-03-19,14:12:08 | INFO | Train Epoch: 13 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 146.949/s, 146.949/s/gpu LR: 0.000118 Logit Scale: 26.553 Contrastive_loss: 0.30775 (0.16629) Loss: 0.30775 (0.16629) 2025-03-19,14:12:29 | INFO | Train Epoch: 13 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 151.300/s, 151.300/s/gpu LR: 0.000118 Logit Scale: 26.540 Contrastive_loss: 0.13865 (0.16607) Loss: 0.13865 (0.16607) 2025-03-19,14:12:51 | INFO | Train Epoch: 13 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 149.092/s, 149.092/s/gpu LR: 0.000118 Logit Scale: 26.533 Contrastive_loss: 0.15000 (0.16594) Loss: 0.15000 (0.16594) 2025-03-19,14:13:12 | INFO | Train Epoch: 13 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 151.219/s, 151.219/s/gpu LR: 0.000118 Logit Scale: 26.497 Contrastive_loss: 0.12375 (0.16561) Loss: 0.12375 (0.16561) 2025-03-19,14:13:34 | INFO | Train Epoch: 13 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 150.900/s, 150.900/s/gpu LR: 0.000118 Logit Scale: 26.515 Contrastive_loss: 0.088693 (0.16502) Loss: 0.088693 (0.16502) 2025-03-19,14:13:55 | INFO | Train Epoch: 13 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.212, 147.216/s, 147.216/s/gpu LR: 0.000118 Logit Scale: 26.471 Contrastive_loss: 0.072897 (0.16432) Loss: 0.072897 (0.16432) 2025-03-19,14:14:16 | INFO | Train Epoch: 13 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 147.909/s, 147.909/s/gpu LR: 0.000118 Logit Scale: 26.455 Contrastive_loss: 0.17870 (0.16443) Loss: 0.17870 (0.16443) 2025-03-19,14:14:38 | INFO | Train Epoch: 13 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 148.281/s, 148.281/s/gpu LR: 0.000118 Logit Scale: 26.456 Contrastive_loss: 0.26139 (0.16516) Loss: 0.26139 (0.16516) 2025-03-19,14:15:00 | INFO | Train Epoch: 13 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 149.260/s, 149.260/s/gpu LR: 0.000117 Logit Scale: 26.457 Contrastive_loss: 0.11345 (0.16477) Loss: 0.11345 (0.16477) 2025-03-19,14:15:21 | INFO | Train Epoch: 13 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 153.195/s, 153.195/s/gpu LR: 0.000117 Logit Scale: 26.465 Contrastive_loss: 0.17699 (0.16486) Loss: 0.17699 (0.16486) 2025-03-19,14:15:42 | INFO | Train Epoch: 13 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 148.990/s, 148.990/s/gpu LR: 0.000117 Logit Scale: 26.442 Contrastive_loss: 0.029258 (0.16387) Loss: 0.029258 (0.16387) 2025-03-19,14:16:04 | INFO | Train Epoch: 13 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 150.177/s, 150.177/s/gpu LR: 0.000117 Logit Scale: 26.420 Contrastive_loss: 0.37519 (0.16541) Loss: 0.37519 (0.16541) 2025-03-19,14:16:25 | INFO | Train Epoch: 13 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 148.676/s, 148.676/s/gpu LR: 0.000117 Logit Scale: 26.441 Contrastive_loss: 0.18959 (0.16558) Loss: 0.18959 (0.16558) 2025-03-19,14:16:47 | INFO | Train Epoch: 13 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 148.885/s, 148.885/s/gpu LR: 0.000117 Logit Scale: 26.450 Contrastive_loss: 0.10957 (0.16518) Loss: 0.10957 (0.16518) 2025-03-19,14:17:08 | INFO | Train Epoch: 13 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 149.668/s, 149.668/s/gpu LR: 0.000117 Logit Scale: 26.447 Contrastive_loss: 0.13028 (0.16493) Loss: 0.13028 (0.16493) 2025-03-19,14:17:30 | INFO | Train Epoch: 13 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 149.847/s, 149.847/s/gpu LR: 0.000117 Logit Scale: 26.470 Contrastive_loss: 0.15125 (0.16483) Loss: 0.15125 (0.16483) 2025-03-19,14:17:51 | INFO | Train Epoch: 13 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.268/s, 149.268/s/gpu LR: 0.000117 Logit Scale: 26.443 Contrastive_loss: 0.21644 (0.16520) Loss: 0.21644 (0.16520) 2025-03-19,14:18:13 | INFO | Train Epoch: 13 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.605/s, 149.605/s/gpu LR: 0.000117 Logit Scale: 26.512 Contrastive_loss: 0.15736 (0.16514) Loss: 0.15736 (0.16514) 2025-03-19,14:18:34 | INFO | Train Epoch: 13 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 147.163/s, 147.163/s/gpu LR: 0.000117 Logit Scale: 26.545 Contrastive_loss: 0.13499 (0.16493) Loss: 0.13499 (0.16493) 2025-03-19,14:18:56 | INFO | Train Epoch: 13 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 148.863/s, 148.863/s/gpu LR: 0.000117 Logit Scale: 26.563 Contrastive_loss: 0.098517 (0.16448) Loss: 0.098517 (0.16448) 2025-03-19,14:19:17 | INFO | Train Epoch: 13 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 147.979/s, 147.979/s/gpu LR: 0.000117 Logit Scale: 26.550 Contrastive_loss: 0.13159 (0.16425) Loss: 0.13159 (0.16425) 2025-03-19,14:19:39 | INFO | Train Epoch: 13 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.052/s, 148.052/s/gpu LR: 0.000117 Logit Scale: 26.550 Contrastive_loss: 0.16860 (0.16428) Loss: 0.16860 (0.16428) 2025-03-19,14:20:00 | INFO | Train Epoch: 13 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.503/s, 148.503/s/gpu LR: 0.000117 Logit Scale: 26.534 Contrastive_loss: 0.27455 (0.16502) Loss: 0.27455 (0.16502) 2025-03-19,14:20:22 | INFO | Train Epoch: 13 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 147.715/s, 147.715/s/gpu LR: 0.000117 Logit Scale: 26.534 Contrastive_loss: 0.33490 (0.16616) Loss: 0.33490 (0.16616) 2025-03-19,14:20:44 | INFO | Train Epoch: 13 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.219, 148.547/s, 148.547/s/gpu LR: 0.000117 Logit Scale: 26.491 Contrastive_loss: 0.28845 (0.16698) Loss: 0.28845 (0.16698) 2025-03-19,14:21:06 | INFO | Train Epoch: 13 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 148.558/s, 148.558/s/gpu LR: 0.000117 Logit Scale: 26.498 Contrastive_loss: 0.034659 (0.16610) Loss: 0.034659 (0.16610) 2025-03-19,14:21:27 | INFO | Train Epoch: 13 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 148.052/s, 148.052/s/gpu LR: 0.000117 Logit Scale: 26.503 Contrastive_loss: 0.12740 (0.16585) Loss: 0.12740 (0.16585) 2025-03-19,14:21:48 | INFO | Train Epoch: 13 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.197/s, 149.197/s/gpu LR: 0.000117 Logit Scale: 26.457 Contrastive_loss: 0.25992 (0.16646) Loss: 0.25992 (0.16646) 2025-03-19,14:22:10 | INFO | Train Epoch: 13 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 148.410/s, 148.410/s/gpu LR: 0.000117 Logit Scale: 26.461 Contrastive_loss: 0.082878 (0.16592) Loss: 0.082878 (0.16592) 2025-03-19,14:22:31 | INFO | Train Epoch: 13 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 148.173/s, 148.173/s/gpu LR: 0.000117 Logit Scale: 26.467 Contrastive_loss: 0.19276 (0.16609) Loss: 0.19276 (0.16609) 2025-03-19,14:22:53 | INFO | Train Epoch: 13 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 146.547/s, 146.547/s/gpu LR: 0.000117 Logit Scale: 26.456 Contrastive_loss: 0.11954 (0.16580) Loss: 0.11954 (0.16580) 2025-03-19,14:23:15 | INFO | Train Epoch: 13 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 149.056/s, 149.056/s/gpu LR: 0.000116 Logit Scale: 26.491 Contrastive_loss: 0.079134 (0.16524) Loss: 0.079134 (0.16524) 2025-03-19,14:23:36 | INFO | Train Epoch: 13 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 147.978/s, 147.978/s/gpu LR: 0.000116 Logit Scale: 26.480 Contrastive_loss: 0.13186 (0.16503) Loss: 0.13186 (0.16503) 2025-03-19,14:23:58 | INFO | Train Epoch: 13 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 147.907/s, 147.907/s/gpu LR: 0.000116 Logit Scale: 26.479 Contrastive_loss: 0.15078 (0.16494) Loss: 0.15078 (0.16494) 2025-03-19,14:24:20 | INFO | Train Epoch: 13 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 147.163/s, 147.163/s/gpu LR: 0.000116 Logit Scale: 26.508 Contrastive_loss: 0.19618 (0.16514) Loss: 0.19618 (0.16514) 2025-03-19,14:24:42 | INFO | Train Epoch: 13 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.219, 147.177/s, 147.177/s/gpu LR: 0.000116 Logit Scale: 26.523 Contrastive_loss: 0.28966 (0.16591) Loss: 0.28966 (0.16591) 2025-03-19,14:25:04 | INFO | Train Epoch: 13 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.221, 147.716/s, 147.716/s/gpu LR: 0.000116 Logit Scale: 26.522 Contrastive_loss: 0.093418 (0.16546) Loss: 0.093418 (0.16546) 2025-03-19,14:25:25 | INFO | Train Epoch: 13 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 142.794/s, 142.794/s/gpu LR: 0.000116 Logit Scale: 26.539 Contrastive_loss: 0.19362 (0.16564) Loss: 0.19362 (0.16564) 2025-03-19,14:25:47 | INFO | Train Epoch: 13 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 148.811/s, 148.811/s/gpu LR: 0.000116 Logit Scale: 26.500 Contrastive_loss: 0.16219 (0.16562) Loss: 0.16219 (0.16562) 2025-03-19,14:26:08 | INFO | Train Epoch: 13 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 149.437/s, 149.437/s/gpu LR: 0.000116 Logit Scale: 26.477 Contrastive_loss: 0.17075 (0.16565) Loss: 0.17075 (0.16565) 2025-03-19,14:26:30 | INFO | Train Epoch: 13 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 143.387/s, 143.387/s/gpu LR: 0.000116 Logit Scale: 26.473 Contrastive_loss: 0.24085 (0.16610) Loss: 0.24085 (0.16610) 2025-03-19,14:26:52 | INFO | Train Epoch: 13 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 146.577/s, 146.577/s/gpu LR: 0.000116 Logit Scale: 26.472 Contrastive_loss: 0.021426 (0.16523) Loss: 0.021426 (0.16523) 2025-03-19,14:27:13 | INFO | Train Epoch: 13 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 148.005/s, 148.005/s/gpu LR: 0.000116 Logit Scale: 26.493 Contrastive_loss: 0.22817 (0.16561) Loss: 0.22817 (0.16561) 2025-03-19,14:27:35 | INFO | Train Epoch: 13 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 148.085/s, 148.085/s/gpu LR: 0.000116 Logit Scale: 26.485 Contrastive_loss: 0.12855 (0.16539) Loss: 0.12855 (0.16539) 2025-03-19,14:27:57 | INFO | Train Epoch: 13 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 148.037/s, 148.037/s/gpu LR: 0.000116 Logit Scale: 26.481 Contrastive_loss: 0.29903 (0.16617) Loss: 0.29903 (0.16617) 2025-03-19,14:28:18 | INFO | Train Epoch: 13 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 148.217/s, 148.217/s/gpu LR: 0.000116 Logit Scale: 26.536 Contrastive_loss: 0.14967 (0.16608) Loss: 0.14967 (0.16608) 2025-03-19,14:28:40 | INFO | Train Epoch: 13 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 140.908/s, 140.908/s/gpu LR: 0.000116 Logit Scale: 26.508 Contrastive_loss: 0.25992 (0.16662) Loss: 0.25992 (0.16662) 2025-03-19,14:29:02 | INFO | Train Epoch: 13 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 148.456/s, 148.456/s/gpu LR: 0.000116 Logit Scale: 26.540 Contrastive_loss: 0.14008 (0.16647) Loss: 0.14008 (0.16647) 2025-03-19,14:29:23 | INFO | Train Epoch: 13 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 148.721/s, 148.721/s/gpu LR: 0.000116 Logit Scale: 26.546 Contrastive_loss: 0.23508 (0.16686) Loss: 0.23508 (0.16686) 2025-03-19,14:29:45 | INFO | Train Epoch: 13 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 148.221/s, 148.221/s/gpu LR: 0.000116 Logit Scale: 26.544 Contrastive_loss: 0.093906 (0.16645) Loss: 0.093906 (0.16645) 2025-03-19,14:30:07 | INFO | Train Epoch: 13 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.220, 146.800/s, 146.800/s/gpu LR: 0.000116 Logit Scale: 26.506 Contrastive_loss: 0.15997 (0.16641) Loss: 0.15997 (0.16641) 2025-03-19,14:30:29 | INFO | Train Epoch: 13 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 146.669/s, 146.669/s/gpu LR: 0.000116 Logit Scale: 26.497 Contrastive_loss: 0.25092 (0.16689) Loss: 0.25092 (0.16689) 2025-03-19,14:30:50 | INFO | Train Epoch: 13 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 146.464/s, 146.464/s/gpu LR: 0.000116 Logit Scale: 26.539 Contrastive_loss: 0.19982 (0.16707) Loss: 0.19982 (0.16707) 2025-03-19,14:31:12 | INFO | Train Epoch: 13 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 147.979/s, 147.979/s/gpu LR: 0.000115 Logit Scale: 26.510 Contrastive_loss: 0.14587 (0.16696) Loss: 0.14587 (0.16696) 2025-03-19,14:31:34 | INFO | Train Epoch: 13 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.217, 147.129/s, 147.129/s/gpu LR: 0.000115 Logit Scale: 26.549 Contrastive_loss: 0.058923 (0.16636) Loss: 0.058923 (0.16636) 2025-03-19,14:31:56 | INFO | Train Epoch: 13 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.219, 147.737/s, 147.737/s/gpu LR: 0.000115 Logit Scale: 26.576 Contrastive_loss: 0.26325 (0.16689) Loss: 0.26325 (0.16689) 2025-03-19,14:32:17 | INFO | Train Epoch: 13 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.433/s, 148.433/s/gpu LR: 0.000115 Logit Scale: 26.602 Contrastive_loss: 0.21683 (0.16716) Loss: 0.21683 (0.16716) 2025-03-19,14:32:39 | INFO | Train Epoch: 13 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.206/s, 148.206/s/gpu LR: 0.000115 Logit Scale: 26.610 Contrastive_loss: 0.21616 (0.16743) Loss: 0.21616 (0.16743) 2025-03-19,14:33:01 | INFO | Train Epoch: 13 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.978/s, 148.978/s/gpu LR: 0.000115 Logit Scale: 26.608 Contrastive_loss: 0.040899 (0.16674) Loss: 0.040899 (0.16674) 2025-03-19,14:33:22 | INFO | Train Epoch: 13 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.217, 147.017/s, 147.017/s/gpu LR: 0.000115 Logit Scale: 26.596 Contrastive_loss: 0.15408 (0.16668) Loss: 0.15408 (0.16668) 2025-03-19,14:33:44 | INFO | Train Epoch: 13 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 149.851/s, 149.851/s/gpu LR: 0.000115 Logit Scale: 26.616 Contrastive_loss: 0.085483 (0.16624) Loss: 0.085483 (0.16624) 2025-03-19,14:34:05 | INFO | Train Epoch: 13 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 148.634/s, 148.634/s/gpu LR: 0.000115 Logit Scale: 26.593 Contrastive_loss: 0.062787 (0.16569) Loss: 0.062787 (0.16569) 2025-03-19,14:34:27 | INFO | Train Epoch: 13 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 149.314/s, 149.314/s/gpu LR: 0.000115 Logit Scale: 26.571 Contrastive_loss: 0.16671 (0.16569) Loss: 0.16671 (0.16569) 2025-03-19,14:34:48 | INFO | Train Epoch: 13 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 150.239/s, 150.239/s/gpu LR: 0.000115 Logit Scale: 26.577 Contrastive_loss: 0.11445 (0.16542) Loss: 0.11445 (0.16542) 2025-03-19,14:35:09 | INFO | Train Epoch: 13 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 149.188/s, 149.188/s/gpu LR: 0.000115 Logit Scale: 26.591 Contrastive_loss: 0.32487 (0.16626) Loss: 0.32487 (0.16626) 2025-03-19,14:35:31 | INFO | Train Epoch: 13 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 149.840/s, 149.840/s/gpu LR: 0.000115 Logit Scale: 26.562 Contrastive_loss: 0.30314 (0.16698) Loss: 0.30314 (0.16698) 2025-03-19,14:35:52 | INFO | Train Epoch: 13 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 148.567/s, 148.567/s/gpu LR: 0.000115 Logit Scale: 26.566 Contrastive_loss: 0.18562 (0.16707) Loss: 0.18562 (0.16707) 2025-03-19,14:36:14 | INFO | Train Epoch: 13 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 150.548/s, 150.548/s/gpu LR: 0.000115 Logit Scale: 26.560 Contrastive_loss: 0.17849 (0.16713) Loss: 0.17849 (0.16713) 2025-03-19,14:36:35 | INFO | Train Epoch: 13 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 151.451/s, 151.451/s/gpu LR: 0.000115 Logit Scale: 26.539 Contrastive_loss: 0.15884 (0.16709) Loss: 0.15884 (0.16709) 2025-03-19,14:36:57 | INFO | Train Epoch: 13 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.213, 150.267/s, 150.267/s/gpu LR: 0.000115 Logit Scale: 26.542 Contrastive_loss: 0.19071 (0.16721) Loss: 0.19071 (0.16721) 2025-03-19,14:37:18 | INFO | Train Epoch: 13 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 150.173/s, 150.173/s/gpu LR: 0.000115 Logit Scale: 26.551 Contrastive_loss: 0.082351 (0.16678) Loss: 0.082351 (0.16678) 2025-03-19,14:37:39 | INFO | Train Epoch: 13 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.213, 150.990/s, 150.990/s/gpu LR: 0.000115 Logit Scale: 26.591 Contrastive_loss: 0.090692 (0.16639) Loss: 0.090692 (0.16639) 2025-03-19,14:38:01 | INFO | Train Epoch: 13 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 146.336/s, 146.336/s/gpu LR: 0.000115 Logit Scale: 26.600 Contrastive_loss: 0.18439 (0.16648) Loss: 0.18439 (0.16648) 2025-03-19,14:38:23 | INFO | Train Epoch: 13 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 147.696/s, 147.696/s/gpu LR: 0.000115 Logit Scale: 26.586 Contrastive_loss: 0.21418 (0.16672) Loss: 0.21418 (0.16672) 2025-03-19,14:38:44 | INFO | Train Epoch: 13 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.219, 149.019/s, 149.019/s/gpu LR: 0.000115 Logit Scale: 26.614 Contrastive_loss: 0.060215 (0.16619) Loss: 0.060215 (0.16619) 2025-03-19,14:39:06 | INFO | Train Epoch: 13 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 147.606/s, 147.606/s/gpu LR: 0.000115 Logit Scale: 26.630 Contrastive_loss: 0.25988 (0.16666) Loss: 0.25988 (0.16666) 2025-03-19,14:39:28 | INFO | Train Epoch: 13 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 150.408/s, 150.408/s/gpu LR: 0.000114 Logit Scale: 26.601 Contrastive_loss: 0.22452 (0.16694) Loss: 0.22452 (0.16694) 2025-03-19,14:39:50 | INFO | Train Epoch: 13 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 148.409/s, 148.409/s/gpu LR: 0.000114 Logit Scale: 26.590 Contrastive_loss: 0.11572 (0.16669) Loss: 0.11572 (0.16669) 2025-03-19,14:40:11 | INFO | Train Epoch: 13 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 149.937/s, 149.937/s/gpu LR: 0.000114 Logit Scale: 26.617 Contrastive_loss: 0.13948 (0.16656) Loss: 0.13948 (0.16656) 2025-03-19,14:40:32 | INFO | Train Epoch: 13 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.213, 150.760/s, 150.760/s/gpu LR: 0.000114 Logit Scale: 26.592 Contrastive_loss: 0.22355 (0.16684) Loss: 0.22355 (0.16684) 2025-03-19,14:40:54 | INFO | Train Epoch: 13 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.527/s, 148.527/s/gpu LR: 0.000114 Logit Scale: 26.612 Contrastive_loss: 0.065168 (0.16634) Loss: 0.065168 (0.16634) 2025-03-19,14:41:15 | INFO | Train Epoch: 13 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.385/s, 148.385/s/gpu LR: 0.000114 Logit Scale: 26.636 Contrastive_loss: 0.11307 (0.16608) Loss: 0.11307 (0.16608) 2025-03-19,14:41:37 | INFO | Train Epoch: 13 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.296/s, 149.296/s/gpu LR: 0.000114 Logit Scale: 26.623 Contrastive_loss: 0.029515 (0.16543) Loss: 0.029515 (0.16543) 2025-03-19,14:41:58 | INFO | Train Epoch: 13 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 149.762/s, 149.762/s/gpu LR: 0.000114 Logit Scale: 26.605 Contrastive_loss: 0.17907 (0.16549) Loss: 0.17907 (0.16549) 2025-03-19,14:42:20 | INFO | Train Epoch: 13 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 149.634/s, 149.634/s/gpu LR: 0.000114 Logit Scale: 26.607 Contrastive_loss: 0.23595 (0.16583) Loss: 0.23595 (0.16583) 2025-03-19,14:42:41 | INFO | Train Epoch: 13 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 147.990/s, 147.990/s/gpu LR: 0.000114 Logit Scale: 26.624 Contrastive_loss: 0.094405 (0.16549) Loss: 0.094405 (0.16549) 2025-03-19,14:43:03 | INFO | Train Epoch: 13 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 149.393/s, 149.393/s/gpu LR: 0.000114 Logit Scale: 26.582 Contrastive_loss: 0.10941 (0.16523) Loss: 0.10941 (0.16523) 2025-03-19,14:43:24 | INFO | Train Epoch: 13 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.311/s, 149.311/s/gpu LR: 0.000114 Logit Scale: 26.620 Contrastive_loss: 0.13046 (0.16506) Loss: 0.13046 (0.16506) 2025-03-19,14:43:46 | INFO | Train Epoch: 13 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 148.241/s, 148.241/s/gpu LR: 0.000114 Logit Scale: 26.615 Contrastive_loss: 0.22909 (0.16536) Loss: 0.22909 (0.16536) 2025-03-19,14:44:07 | INFO | Train Epoch: 13 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 149.670/s, 149.670/s/gpu LR: 0.000114 Logit Scale: 26.598 Contrastive_loss: 0.088934 (0.16501) Loss: 0.088934 (0.16501) 2025-03-19,14:44:29 | INFO | Train Epoch: 13 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 150.235/s, 150.235/s/gpu LR: 0.000114 Logit Scale: 26.612 Contrastive_loss: 0.095035 (0.16468) Loss: 0.095035 (0.16468) 2025-03-19,14:44:50 | INFO | Train Epoch: 13 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.217, 145.967/s, 145.967/s/gpu LR: 0.000114 Logit Scale: 26.628 Contrastive_loss: 0.11178 (0.16444) Loss: 0.11178 (0.16444) 2025-03-19,14:45:12 | INFO | Train Epoch: 13 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 152.207/s, 152.207/s/gpu LR: 0.000114 Logit Scale: 26.639 Contrastive_loss: 0.078296 (0.16404) Loss: 0.078296 (0.16404) 2025-03-19,14:45:33 | INFO | Train Epoch: 13 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.564/s, 149.564/s/gpu LR: 0.000114 Logit Scale: 26.665 Contrastive_loss: 0.067393 (0.16360) Loss: 0.067393 (0.16360) 2025-03-19,14:45:55 | INFO | Train Epoch: 13 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 150.240/s, 150.240/s/gpu LR: 0.000114 Logit Scale: 26.679 Contrastive_loss: 0.077299 (0.16321) Loss: 0.077299 (0.16321) 2025-03-19,14:46:16 | INFO | Train Epoch: 13 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 150.352/s, 150.352/s/gpu LR: 0.000114 Logit Scale: 26.637 Contrastive_loss: 0.21622 (0.16345) Loss: 0.21622 (0.16345) 2025-03-19,14:46:38 | INFO | Train Epoch: 13 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.212, 152.533/s, 152.533/s/gpu LR: 0.000114 Logit Scale: 26.628 Contrastive_loss: 0.15884 (0.16343) Loss: 0.15884 (0.16343) 2025-03-19,14:46:59 | INFO | Train Epoch: 13 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.211, 151.347/s, 151.347/s/gpu LR: 0.000114 Logit Scale: 26.642 Contrastive_loss: 0.14080 (0.16333) Loss: 0.14080 (0.16333) 2025-03-19,14:47:20 | INFO | Train Epoch: 13 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.212, 150.826/s, 150.826/s/gpu LR: 0.000114 Logit Scale: 26.660 Contrastive_loss: 0.36444 (0.16422) Loss: 0.36444 (0.16422) 2025-03-19,14:47:41 | INFO | Train Epoch: 13 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 151.531/s, 151.531/s/gpu LR: 0.000113 Logit Scale: 26.641 Contrastive_loss: 0.12223 (0.16404) Loss: 0.12223 (0.16404) 2025-03-19,14:48:02 | INFO | Train Epoch: 13 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.212, 151.292/s, 151.292/s/gpu LR: 0.000113 Logit Scale: 26.635 Contrastive_loss: 0.098889 (0.16375) Loss: 0.098889 (0.16375) 2025-03-19,14:48:23 | INFO | Train Epoch: 13 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.211, 150.316/s, 150.316/s/gpu LR: 0.000113 Logit Scale: 26.626 Contrastive_loss: 0.18021 (0.16382) Loss: 0.18021 (0.16382) 2025-03-19,14:48:45 | INFO | Train Epoch: 13 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.213, 151.659/s, 151.659/s/gpu LR: 0.000113 Logit Scale: 26.635 Contrastive_loss: 0.22933 (0.16411) Loss: 0.22933 (0.16411) 2025-03-19,14:49:06 | INFO | Train Epoch: 13 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.213, 150.367/s, 150.367/s/gpu LR: 0.000113 Logit Scale: 26.580 Contrastive_loss: 0.37437 (0.16503) Loss: 0.37437 (0.16503) 2025-03-19,14:49:27 | INFO | Train Epoch: 13 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 150.329/s, 150.329/s/gpu LR: 0.000113 Logit Scale: 26.563 Contrastive_loss: 0.62929 (0.16705) Loss: 0.62929 (0.16705) 2025-03-19,14:49:49 | INFO | Train Epoch: 13 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 149.758/s, 149.758/s/gpu LR: 0.000113 Logit Scale: 26.588 Contrastive_loss: 0.041548 (0.16650) Loss: 0.041548 (0.16650) 2025-03-19,14:50:10 | INFO | Train Epoch: 13 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.707/s, 149.707/s/gpu LR: 0.000113 Logit Scale: 26.574 Contrastive_loss: 0.19085 (0.16661) Loss: 0.19085 (0.16661) 2025-03-19,14:50:32 | INFO | Train Epoch: 13 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.754/s, 149.754/s/gpu LR: 0.000113 Logit Scale: 26.520 Contrastive_loss: 0.10961 (0.16636) Loss: 0.10961 (0.16636) 2025-03-19,14:50:53 | INFO | Train Epoch: 13 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 151.936/s, 151.936/s/gpu LR: 0.000113 Logit Scale: 26.547 Contrastive_loss: 0.16069 (0.16634) Loss: 0.16069 (0.16634) 2025-03-19,14:51:14 | INFO | Train Epoch: 13 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.211, 151.705/s, 151.705/s/gpu LR: 0.000113 Logit Scale: 26.562 Contrastive_loss: 0.16641 (0.16634) Loss: 0.16641 (0.16634) 2025-03-19,14:51:35 | INFO | Train Epoch: 13 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.211, 151.692/s, 151.692/s/gpu LR: 0.000113 Logit Scale: 26.577 Contrastive_loss: 0.18748 (0.16643) Loss: 0.18748 (0.16643) 2025-03-19,14:51:57 | INFO | Train Epoch: 13 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 148.139/s, 148.139/s/gpu LR: 0.000113 Logit Scale: 26.569 Contrastive_loss: 0.39919 (0.16741) Loss: 0.39919 (0.16741) 2025-03-19,14:52:18 | INFO | Train Epoch: 13 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.212, 150.698/s, 150.698/s/gpu LR: 0.000113 Logit Scale: 26.564 Contrastive_loss: 0.19117 (0.16751) Loss: 0.19117 (0.16751) 2025-03-19,14:52:39 | INFO | Train Epoch: 13 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 149.816/s, 149.816/s/gpu LR: 0.000113 Logit Scale: 26.552 Contrastive_loss: 0.027476 (0.16693) Loss: 0.027476 (0.16693) 2025-03-19,14:53:00 | INFO | Train Epoch: 13 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.214, 149.466/s, 149.466/s/gpu LR: 0.000113 Logit Scale: 26.522 Contrastive_loss: 0.16767 (0.16693) Loss: 0.16767 (0.16693) 2025-03-19,14:53:08 | INFO | Train Epoch: 13 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.215, 149.645/s, 149.645/s/gpu LR: 0.000113 Logit Scale: 26.520 Contrastive_loss: 0.24565 (0.16725) Loss: 0.24565 (0.16725) 2025-03-19,14:53:09 | INFO | Eval Epoch: 14 [32 / 7443] Clip Loss: 3.243996 2025-03-19,14:53:14 | INFO | Eval Epoch: 14 [3232 / 7443] Clip Loss: 0.827644 2025-03-19,14:53:20 | INFO | Eval Epoch: 14 [6432 / 7443] Clip Loss: 0.639604 2025-03-19,14:53:23 | INFO | Eval Epoch: 14 image_to_text_mean_rank: 81.7238 image_to_text_median_rank: 7.0000 image_to_text_R@1: 0.1383 image_to_text_R@5: 0.4396 image_to_text_R@10: 0.6203 text_to_image_mean_rank: 52.9764 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1309 text_to_image_R@5: 0.4430 text_to_image_R@10: 0.6113 clip_val_loss: 0.6011 epoch: 14.0000 num_samples: 7443.0000 2025-03-19,14:53:56 | INFO | Start epoch 14 2025-03-19,14:53:56 | INFO | Train Epoch: 14 [ 32/766009 (0%)] Data (t): 0.172 Batch (t): 0.378, 84.5609/s, 84.5609/s/gpu LR: 0.000113 Logit Scale: 26.520 Contrastive_loss: 0.077303 (0.077303) Loss: 0.077303 (0.077303) 2025-03-19,14:54:18 | INFO | Train Epoch: 14 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.216, 148.692/s, 148.692/s/gpu LR: 0.000113 Logit Scale: 26.562 Contrastive_loss: 0.079429 (0.078366) Loss: 0.079429 (0.078366) 2025-03-19,14:54:39 | INFO | Train Epoch: 14 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 150.656/s, 150.656/s/gpu LR: 0.000113 Logit Scale: 26.592 Contrastive_loss: 0.097879 (0.084870) Loss: 0.097879 (0.084870) 2025-03-19,14:55:00 | INFO | Train Epoch: 14 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.213, 149.892/s, 149.892/s/gpu LR: 0.000113 Logit Scale: 26.620 Contrastive_loss: 0.079130 (0.083435) Loss: 0.079130 (0.083435) 2025-03-19,14:55:22 | INFO | Train Epoch: 14 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.218, 150.472/s, 150.472/s/gpu LR: 0.000113 Logit Scale: 26.623 Contrastive_loss: 0.12816 (0.092380) Loss: 0.12816 (0.092380) 2025-03-19,14:55:44 | INFO | Train Epoch: 14 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 149.524/s, 149.524/s/gpu LR: 0.000113 Logit Scale: 26.650 Contrastive_loss: 0.14257 (0.10075) Loss: 0.14257 (0.10075) 2025-03-19,14:56:05 | INFO | Train Epoch: 14 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 150.720/s, 150.720/s/gpu LR: 0.000113 Logit Scale: 26.658 Contrastive_loss: 0.24461 (0.12130) Loss: 0.24461 (0.12130) 2025-03-19,14:56:27 | INFO | Train Epoch: 14 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 148.450/s, 148.450/s/gpu LR: 0.000112 Logit Scale: 26.654 Contrastive_loss: 0.33761 (0.14834) Loss: 0.33761 (0.14834) 2025-03-19,14:56:48 | INFO | Train Epoch: 14 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 152.102/s, 152.102/s/gpu LR: 0.000112 Logit Scale: 26.663 Contrastive_loss: 0.21061 (0.15526) Loss: 0.21061 (0.15526) 2025-03-19,14:57:09 | INFO | Train Epoch: 14 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.213, 145.480/s, 145.480/s/gpu LR: 0.000112 Logit Scale: 26.681 Contrastive_loss: 0.14204 (0.15393) Loss: 0.14204 (0.15393) 2025-03-19,14:57:31 | INFO | Train Epoch: 14 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.216, 149.794/s, 149.794/s/gpu LR: 0.000112 Logit Scale: 26.684 Contrastive_loss: 0.15486 (0.15402) Loss: 0.15486 (0.15402) 2025-03-19,14:57:53 | INFO | Train Epoch: 14 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.292/s, 147.292/s/gpu LR: 0.000112 Logit Scale: 26.677 Contrastive_loss: 0.16006 (0.15452) Loss: 0.16006 (0.15452) 2025-03-19,14:58:14 | INFO | Train Epoch: 14 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.693/s, 147.693/s/gpu LR: 0.000112 Logit Scale: 26.687 Contrastive_loss: 0.064897 (0.14763) Loss: 0.064897 (0.14763) 2025-03-19,14:58:36 | INFO | Train Epoch: 14 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 149.780/s, 149.780/s/gpu LR: 0.000112 Logit Scale: 26.681 Contrastive_loss: 0.096968 (0.14401) Loss: 0.096968 (0.14401) 2025-03-19,14:58:57 | INFO | Train Epoch: 14 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 149.228/s, 149.228/s/gpu LR: 0.000112 Logit Scale: 26.705 Contrastive_loss: 0.14882 (0.14433) Loss: 0.14882 (0.14433) 2025-03-19,14:59:18 | INFO | Train Epoch: 14 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.213, 151.330/s, 151.330/s/gpu LR: 0.000112 Logit Scale: 26.717 Contrastive_loss: 0.13232 (0.14358) Loss: 0.13232 (0.14358) 2025-03-19,14:59:40 | INFO | Train Epoch: 14 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.212, 151.375/s, 151.375/s/gpu LR: 0.000112 Logit Scale: 26.744 Contrastive_loss: 0.25664 (0.15023) Loss: 0.25664 (0.15023) 2025-03-19,15:00:01 | INFO | Train Epoch: 14 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.213, 150.438/s, 150.438/s/gpu LR: 0.000112 Logit Scale: 26.768 Contrastive_loss: 0.17222 (0.15145) Loss: 0.17222 (0.15145) 2025-03-19,15:00:22 | INFO | Train Epoch: 14 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 149.545/s, 149.545/s/gpu LR: 0.000112 Logit Scale: 26.780 Contrastive_loss: 0.15155 (0.15146) Loss: 0.15155 (0.15146) 2025-03-19,15:00:44 | INFO | Train Epoch: 14 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.831/s, 149.831/s/gpu LR: 0.000112 Logit Scale: 26.797 Contrastive_loss: 0.12477 (0.15012) Loss: 0.12477 (0.15012) 2025-03-19,15:01:05 | INFO | Train Epoch: 14 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.692/s, 148.692/s/gpu LR: 0.000112 Logit Scale: 26.826 Contrastive_loss: 0.12227 (0.14880) Loss: 0.12227 (0.14880) 2025-03-19,15:01:27 | INFO | Train Epoch: 14 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 150.140/s, 150.140/s/gpu LR: 0.000112 Logit Scale: 26.824 Contrastive_loss: 0.20799 (0.15149) Loss: 0.20799 (0.15149) 2025-03-19,15:01:48 | INFO | Train Epoch: 14 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 148.907/s, 148.907/s/gpu LR: 0.000112 Logit Scale: 26.825 Contrastive_loss: 0.14123 (0.15104) Loss: 0.14123 (0.15104) 2025-03-19,15:02:10 | INFO | Train Epoch: 14 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 154.901/s, 154.901/s/gpu LR: 0.000112 Logit Scale: 26.837 Contrastive_loss: 0.15402 (0.15117) Loss: 0.15402 (0.15117) 2025-03-19,15:02:31 | INFO | Train Epoch: 14 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.212, 149.561/s, 149.561/s/gpu LR: 0.000112 Logit Scale: 26.849 Contrastive_loss: 0.093363 (0.14885) Loss: 0.093363 (0.14885) 2025-03-19,15:02:52 | INFO | Train Epoch: 14 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.212, 151.401/s, 151.401/s/gpu LR: 0.000112 Logit Scale: 26.841 Contrastive_loss: 0.11064 (0.14738) Loss: 0.11064 (0.14738) 2025-03-19,15:03:13 | INFO | Train Epoch: 14 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.212, 151.843/s, 151.843/s/gpu LR: 0.000112 Logit Scale: 26.812 Contrastive_loss: 0.11444 (0.14616) Loss: 0.11444 (0.14616) 2025-03-19,15:03:35 | INFO | Train Epoch: 14 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.212, 149.652/s, 149.652/s/gpu LR: 0.000112 Logit Scale: 26.811 Contrastive_loss: 0.18274 (0.14747) Loss: 0.18274 (0.14747) 2025-03-19,15:03:56 | INFO | Train Epoch: 14 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 147.485/s, 147.485/s/gpu LR: 0.000112 Logit Scale: 26.800 Contrastive_loss: 0.22705 (0.15021) Loss: 0.22705 (0.15021) 2025-03-19,15:04:18 | INFO | Train Epoch: 14 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 150.109/s, 150.109/s/gpu LR: 0.000112 Logit Scale: 26.793 Contrastive_loss: 0.15920 (0.15051) Loss: 0.15920 (0.15051) 2025-03-19,15:04:39 | INFO | Train Epoch: 14 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 149.305/s, 149.305/s/gpu LR: 0.000111 Logit Scale: 26.785 Contrastive_loss: 0.068223 (0.14786) Loss: 0.068223 (0.14786) 2025-03-19,15:05:01 | INFO | Train Epoch: 14 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.401/s, 149.401/s/gpu LR: 0.000111 Logit Scale: 26.797 Contrastive_loss: 0.16154 (0.14829) Loss: 0.16154 (0.14829) 2025-03-19,15:05:22 | INFO | Train Epoch: 14 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.584/s, 148.584/s/gpu LR: 0.000111 Logit Scale: 26.795 Contrastive_loss: 0.15385 (0.14846) Loss: 0.15385 (0.14846) 2025-03-19,15:05:44 | INFO | Train Epoch: 14 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.281/s, 149.281/s/gpu LR: 0.000111 Logit Scale: 26.750 Contrastive_loss: 0.33918 (0.15406) Loss: 0.33918 (0.15406) 2025-03-19,15:06:05 | INFO | Train Epoch: 14 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 148.910/s, 148.910/s/gpu LR: 0.000111 Logit Scale: 26.774 Contrastive_loss: 0.15283 (0.15403) Loss: 0.15283 (0.15403) 2025-03-19,15:06:27 | INFO | Train Epoch: 14 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.279/s, 149.279/s/gpu LR: 0.000111 Logit Scale: 26.760 Contrastive_loss: 0.10226 (0.15259) Loss: 0.10226 (0.15259) 2025-03-19,15:06:48 | INFO | Train Epoch: 14 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.209/s, 149.209/s/gpu LR: 0.000111 Logit Scale: 26.780 Contrastive_loss: 0.18373 (0.15343) Loss: 0.18373 (0.15343) 2025-03-19,15:07:10 | INFO | Train Epoch: 14 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 147.825/s, 147.825/s/gpu LR: 0.000111 Logit Scale: 26.747 Contrastive_loss: 0.16487 (0.15373) Loss: 0.16487 (0.15373) 2025-03-19,15:07:31 | INFO | Train Epoch: 14 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 149.581/s, 149.581/s/gpu LR: 0.000111 Logit Scale: 26.758 Contrastive_loss: 0.26281 (0.15653) Loss: 0.26281 (0.15653) 2025-03-19,15:07:53 | INFO | Train Epoch: 14 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.217, 146.442/s, 146.442/s/gpu LR: 0.000111 Logit Scale: 26.723 Contrastive_loss: 0.048812 (0.15384) Loss: 0.048812 (0.15384) 2025-03-19,15:08:15 | INFO | Train Epoch: 14 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 148.310/s, 148.310/s/gpu LR: 0.000111 Logit Scale: 26.760 Contrastive_loss: 0.048076 (0.15126) Loss: 0.048076 (0.15126) 2025-03-19,15:08:37 | INFO | Train Epoch: 14 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.219, 141.125/s, 141.125/s/gpu LR: 0.000111 Logit Scale: 26.707 Contrastive_loss: 0.064749 (0.14920) Loss: 0.064749 (0.14920) 2025-03-19,15:08:59 | INFO | Train Epoch: 14 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.219, 147.282/s, 147.282/s/gpu LR: 0.000111 Logit Scale: 26.708 Contrastive_loss: 0.10974 (0.14828) Loss: 0.10974 (0.14828) 2025-03-19,15:09:20 | INFO | Train Epoch: 14 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.219, 148.751/s, 148.751/s/gpu LR: 0.000111 Logit Scale: 26.733 Contrastive_loss: 0.15951 (0.14854) Loss: 0.15951 (0.14854) 2025-03-19,15:09:42 | INFO | Train Epoch: 14 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 147.382/s, 147.382/s/gpu LR: 0.000111 Logit Scale: 26.729 Contrastive_loss: 0.33078 (0.15259) Loss: 0.33078 (0.15259) 2025-03-19,15:10:04 | INFO | Train Epoch: 14 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.219, 146.007/s, 146.007/s/gpu LR: 0.000111 Logit Scale: 26.718 Contrastive_loss: 0.32357 (0.15630) Loss: 0.32357 (0.15630) 2025-03-19,15:10:26 | INFO | Train Epoch: 14 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.220, 145.804/s, 145.804/s/gpu LR: 0.000111 Logit Scale: 26.690 Contrastive_loss: 0.36480 (0.16074) Loss: 0.36480 (0.16074) 2025-03-19,15:10:48 | INFO | Train Epoch: 14 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.221, 146.436/s, 146.436/s/gpu LR: 0.000111 Logit Scale: 26.664 Contrastive_loss: 0.18360 (0.16122) Loss: 0.18360 (0.16122) 2025-03-19,15:11:10 | INFO | Train Epoch: 14 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.221, 145.335/s, 145.335/s/gpu LR: 0.000111 Logit Scale: 26.660 Contrastive_loss: 0.10980 (0.16017) Loss: 0.10980 (0.16017) 2025-03-19,15:11:32 | INFO | Train Epoch: 14 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.218, 142.448/s, 142.448/s/gpu LR: 0.000111 Logit Scale: 26.629 Contrastive_loss: 0.074399 (0.15845) Loss: 0.074399 (0.15845) 2025-03-19,15:11:54 | INFO | Train Epoch: 14 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.218, 147.374/s, 147.374/s/gpu LR: 0.000111 Logit Scale: 26.645 Contrastive_loss: 0.028258 (0.15590) Loss: 0.028258 (0.15590) 2025-03-19,15:12:16 | INFO | Train Epoch: 14 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.218, 147.736/s, 147.736/s/gpu LR: 0.000111 Logit Scale: 26.685 Contrastive_loss: 0.091331 (0.15466) Loss: 0.091331 (0.15466) 2025-03-19,15:12:38 | INFO | Train Epoch: 14 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.220, 142.793/s, 142.793/s/gpu LR: 0.000111 Logit Scale: 26.690 Contrastive_loss: 0.27771 (0.15698) Loss: 0.27771 (0.15698) 2025-03-19,15:12:59 | INFO | Train Epoch: 14 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.218, 147.627/s, 147.627/s/gpu LR: 0.000110 Logit Scale: 26.698 Contrastive_loss: 0.22896 (0.15831) Loss: 0.22896 (0.15831) 2025-03-19,15:13:21 | INFO | Train Epoch: 14 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 147.452/s, 147.452/s/gpu LR: 0.000110 Logit Scale: 26.682 Contrastive_loss: 0.16731 (0.15847) Loss: 0.16731 (0.15847) 2025-03-19,15:13:43 | INFO | Train Epoch: 14 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 148.324/s, 148.324/s/gpu LR: 0.000110 Logit Scale: 26.693 Contrastive_loss: 0.045606 (0.15646) Loss: 0.045606 (0.15646) 2025-03-19,15:14:04 | INFO | Train Epoch: 14 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 151.397/s, 151.397/s/gpu LR: 0.000110 Logit Scale: 26.714 Contrastive_loss: 0.12127 (0.15584) Loss: 0.12127 (0.15584) 2025-03-19,15:14:26 | INFO | Train Epoch: 14 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.906/s, 149.906/s/gpu LR: 0.000110 Logit Scale: 26.722 Contrastive_loss: 0.22802 (0.15709) Loss: 0.22802 (0.15709) 2025-03-19,15:14:47 | INFO | Train Epoch: 14 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.225/s, 149.225/s/gpu LR: 0.000110 Logit Scale: 26.712 Contrastive_loss: 0.14567 (0.15689) Loss: 0.14567 (0.15689) 2025-03-19,15:15:09 | INFO | Train Epoch: 14 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 151.294/s, 151.294/s/gpu LR: 0.000110 Logit Scale: 26.748 Contrastive_loss: 0.051636 (0.15514) Loss: 0.051636 (0.15514) 2025-03-19,15:15:30 | INFO | Train Epoch: 14 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.213, 148.600/s, 148.600/s/gpu LR: 0.000110 Logit Scale: 26.723 Contrastive_loss: 0.033374 (0.15314) Loss: 0.033374 (0.15314) 2025-03-19,15:15:52 | INFO | Train Epoch: 14 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 149.575/s, 149.575/s/gpu LR: 0.000110 Logit Scale: 26.708 Contrastive_loss: 0.082537 (0.15200) Loss: 0.082537 (0.15200) 2025-03-19,15:16:13 | INFO | Train Epoch: 14 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 146.528/s, 146.528/s/gpu LR: 0.000110 Logit Scale: 26.710 Contrastive_loss: 0.25337 (0.15361) Loss: 0.25337 (0.15361) 2025-03-19,15:16:35 | INFO | Train Epoch: 14 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.222, 143.326/s, 143.326/s/gpu LR: 0.000110 Logit Scale: 26.686 Contrastive_loss: 0.092932 (0.15266) Loss: 0.092932 (0.15266) 2025-03-19,15:16:57 | INFO | Train Epoch: 14 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.221, 145.468/s, 145.468/s/gpu LR: 0.000110 Logit Scale: 26.720 Contrastive_loss: 0.40336 (0.15652) Loss: 0.40336 (0.15652) 2025-03-19,15:17:19 | INFO | Train Epoch: 14 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 150.352/s, 150.352/s/gpu LR: 0.000110 Logit Scale: 26.721 Contrastive_loss: 0.17455 (0.15679) Loss: 0.17455 (0.15679) 2025-03-19,15:17:40 | INFO | Train Epoch: 14 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 150.276/s, 150.276/s/gpu LR: 0.000110 Logit Scale: 26.718 Contrastive_loss: 0.15585 (0.15678) Loss: 0.15585 (0.15678) 2025-03-19,15:18:02 | INFO | Train Epoch: 14 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.999/s, 149.999/s/gpu LR: 0.000110 Logit Scale: 26.736 Contrastive_loss: 0.19363 (0.15732) Loss: 0.19363 (0.15732) 2025-03-19,15:18:23 | INFO | Train Epoch: 14 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 150.891/s, 150.891/s/gpu LR: 0.000110 Logit Scale: 26.759 Contrastive_loss: 0.22364 (0.15828) Loss: 0.22364 (0.15828) 2025-03-19,15:18:45 | INFO | Train Epoch: 14 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.161/s, 149.161/s/gpu LR: 0.000110 Logit Scale: 26.727 Contrastive_loss: 0.028495 (0.15643) Loss: 0.028495 (0.15643) 2025-03-19,15:19:06 | INFO | Train Epoch: 14 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.213, 151.187/s, 151.187/s/gpu LR: 0.000110 Logit Scale: 26.744 Contrastive_loss: 0.020801 (0.15452) Loss: 0.020801 (0.15452) 2025-03-19,15:19:27 | INFO | Train Epoch: 14 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.213, 149.439/s, 149.439/s/gpu LR: 0.000110 Logit Scale: 26.722 Contrastive_loss: 0.31155 (0.15670) Loss: 0.31155 (0.15670) 2025-03-19,15:19:49 | INFO | Train Epoch: 14 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.213, 150.328/s, 150.328/s/gpu LR: 0.000110 Logit Scale: 26.733 Contrastive_loss: 0.18984 (0.15715) Loss: 0.18984 (0.15715) 2025-03-19,15:20:10 | INFO | Train Epoch: 14 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 150.159/s, 150.159/s/gpu LR: 0.000110 Logit Scale: 26.737 Contrastive_loss: 0.031702 (0.15546) Loss: 0.031702 (0.15546) 2025-03-19,15:20:31 | INFO | Train Epoch: 14 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 146.607/s, 146.607/s/gpu LR: 0.000110 Logit Scale: 26.754 Contrastive_loss: 0.0047046 (0.15345) Loss: 0.0047046 (0.15345) 2025-03-19,15:20:53 | INFO | Train Epoch: 14 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.213, 149.804/s, 149.804/s/gpu LR: 0.000109 Logit Scale: 26.797 Contrastive_loss: 0.14741 (0.15337) Loss: 0.14741 (0.15337) 2025-03-19,15:21:14 | INFO | Train Epoch: 14 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 149.853/s, 149.853/s/gpu LR: 0.000109 Logit Scale: 26.775 Contrastive_loss: 0.31107 (0.15542) Loss: 0.31107 (0.15542) 2025-03-19,15:21:36 | INFO | Train Epoch: 14 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 150.221/s, 150.221/s/gpu LR: 0.000109 Logit Scale: 26.794 Contrastive_loss: 0.032676 (0.15384) Loss: 0.032676 (0.15384) 2025-03-19,15:21:57 | INFO | Train Epoch: 14 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 149.860/s, 149.860/s/gpu LR: 0.000109 Logit Scale: 26.785 Contrastive_loss: 0.22507 (0.15475) Loss: 0.22507 (0.15475) 2025-03-19,15:22:19 | INFO | Train Epoch: 14 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 149.506/s, 149.506/s/gpu LR: 0.000109 Logit Scale: 26.746 Contrastive_loss: 0.18028 (0.15506) Loss: 0.18028 (0.15506) 2025-03-19,15:22:40 | INFO | Train Epoch: 14 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.353/s, 147.353/s/gpu LR: 0.000109 Logit Scale: 26.751 Contrastive_loss: 0.087985 (0.15424) Loss: 0.087985 (0.15424) 2025-03-19,15:23:02 | INFO | Train Epoch: 14 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 145.982/s, 145.982/s/gpu LR: 0.000109 Logit Scale: 26.776 Contrastive_loss: 0.035384 (0.15279) Loss: 0.035384 (0.15279) 2025-03-19,15:23:23 | INFO | Train Epoch: 14 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.217, 148.837/s, 148.837/s/gpu LR: 0.000109 Logit Scale: 26.795 Contrastive_loss: 0.18897 (0.15322) Loss: 0.18897 (0.15322) 2025-03-19,15:23:45 | INFO | Train Epoch: 14 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.220, 146.952/s, 146.952/s/gpu LR: 0.000109 Logit Scale: 26.786 Contrastive_loss: 0.081372 (0.15237) Loss: 0.081372 (0.15237) 2025-03-19,15:24:07 | INFO | Train Epoch: 14 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 147.780/s, 147.780/s/gpu LR: 0.000109 Logit Scale: 26.791 Contrastive_loss: 0.096711 (0.15171) Loss: 0.096711 (0.15171) 2025-03-19,15:24:28 | INFO | Train Epoch: 14 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 150.578/s, 150.578/s/gpu LR: 0.000109 Logit Scale: 26.766 Contrastive_loss: 0.14319 (0.15161) Loss: 0.14319 (0.15161) 2025-03-19,15:24:50 | INFO | Train Epoch: 14 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.259/s, 149.259/s/gpu LR: 0.000109 Logit Scale: 26.762 Contrastive_loss: 0.27964 (0.15309) Loss: 0.27964 (0.15309) 2025-03-19,15:25:11 | INFO | Train Epoch: 14 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.256/s, 149.256/s/gpu LR: 0.000109 Logit Scale: 26.769 Contrastive_loss: 0.21252 (0.15376) Loss: 0.21252 (0.15376) 2025-03-19,15:25:33 | INFO | Train Epoch: 14 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 150.028/s, 150.028/s/gpu LR: 0.000109 Logit Scale: 26.712 Contrastive_loss: 0.24129 (0.15474) Loss: 0.24129 (0.15474) 2025-03-19,15:25:54 | INFO | Train Epoch: 14 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.213, 150.417/s, 150.417/s/gpu LR: 0.000109 Logit Scale: 26.688 Contrastive_loss: 0.24110 (0.15570) Loss: 0.24110 (0.15570) 2025-03-19,15:26:15 | INFO | Train Epoch: 14 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.213, 150.506/s, 150.506/s/gpu LR: 0.000109 Logit Scale: 26.633 Contrastive_loss: 0.15522 (0.15570) Loss: 0.15522 (0.15570) 2025-03-19,15:26:37 | INFO | Train Epoch: 14 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 146.271/s, 146.271/s/gpu LR: 0.000109 Logit Scale: 26.679 Contrastive_loss: 0.17789 (0.15594) Loss: 0.17789 (0.15594) 2025-03-19,15:26:59 | INFO | Train Epoch: 14 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 149.107/s, 149.107/s/gpu LR: 0.000109 Logit Scale: 26.717 Contrastive_loss: 0.095096 (0.15529) Loss: 0.095096 (0.15529) 2025-03-19,15:27:20 | INFO | Train Epoch: 14 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.157/s, 148.157/s/gpu LR: 0.000109 Logit Scale: 26.729 Contrastive_loss: 0.27075 (0.15651) Loss: 0.27075 (0.15651) 2025-03-19,15:27:42 | INFO | Train Epoch: 14 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.217, 149.272/s, 149.272/s/gpu LR: 0.000109 Logit Scale: 26.705 Contrastive_loss: 0.094677 (0.15586) Loss: 0.094677 (0.15586) 2025-03-19,15:28:03 | INFO | Train Epoch: 14 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 149.360/s, 149.360/s/gpu LR: 0.000109 Logit Scale: 26.721 Contrastive_loss: 0.20512 (0.15638) Loss: 0.20512 (0.15638) 2025-03-19,15:28:25 | INFO | Train Epoch: 14 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.305/s, 149.305/s/gpu LR: 0.000109 Logit Scale: 26.710 Contrastive_loss: 0.19499 (0.15677) Loss: 0.19499 (0.15677) 2025-03-19,15:28:46 | INFO | Train Epoch: 14 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.539/s, 149.539/s/gpu LR: 0.000109 Logit Scale: 26.751 Contrastive_loss: 0.25928 (0.15782) Loss: 0.25928 (0.15782) 2025-03-19,15:29:08 | INFO | Train Epoch: 14 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 146.480/s, 146.480/s/gpu LR: 0.000108 Logit Scale: 26.735 Contrastive_loss: 0.15471 (0.15779) Loss: 0.15471 (0.15779) 2025-03-19,15:29:30 | INFO | Train Epoch: 14 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.218, 147.709/s, 147.709/s/gpu LR: 0.000108 Logit Scale: 26.755 Contrastive_loss: 0.15360 (0.15775) Loss: 0.15360 (0.15775) 2025-03-19,15:29:51 | INFO | Train Epoch: 14 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.218, 146.606/s, 146.606/s/gpu LR: 0.000108 Logit Scale: 26.773 Contrastive_loss: 0.082777 (0.15700) Loss: 0.082777 (0.15700) 2025-03-19,15:30:13 | INFO | Train Epoch: 14 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.217, 148.049/s, 148.049/s/gpu LR: 0.000108 Logit Scale: 26.795 Contrastive_loss: 0.23473 (0.15777) Loss: 0.23473 (0.15777) 2025-03-19,15:30:34 | INFO | Train Epoch: 14 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 150.633/s, 150.633/s/gpu LR: 0.000108 Logit Scale: 26.782 Contrastive_loss: 0.16943 (0.15788) Loss: 0.16943 (0.15788) 2025-03-19,15:30:56 | INFO | Train Epoch: 14 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 148.645/s, 148.645/s/gpu LR: 0.000108 Logit Scale: 26.775 Contrastive_loss: 0.22743 (0.15855) Loss: 0.22743 (0.15855) 2025-03-19,15:31:18 | INFO | Train Epoch: 14 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 148.850/s, 148.850/s/gpu LR: 0.000108 Logit Scale: 26.766 Contrastive_loss: 0.27112 (0.15962) Loss: 0.27112 (0.15962) 2025-03-19,15:31:39 | INFO | Train Epoch: 14 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 149.231/s, 149.231/s/gpu LR: 0.000108 Logit Scale: 26.756 Contrastive_loss: 0.081220 (0.15888) Loss: 0.081220 (0.15888) 2025-03-19,15:32:01 | INFO | Train Epoch: 14 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 145.803/s, 145.803/s/gpu LR: 0.000108 Logit Scale: 26.759 Contrastive_loss: 0.19628 (0.15923) Loss: 0.19628 (0.15923) 2025-03-19,15:32:22 | INFO | Train Epoch: 14 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 149.404/s, 149.404/s/gpu LR: 0.000108 Logit Scale: 26.700 Contrastive_loss: 0.11918 (0.15886) Loss: 0.11918 (0.15886) 2025-03-19,15:32:44 | INFO | Train Epoch: 14 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.217, 144.147/s, 144.147/s/gpu LR: 0.000108 Logit Scale: 26.696 Contrastive_loss: 0.34936 (0.16061) Loss: 0.34936 (0.16061) 2025-03-19,15:33:06 | INFO | Train Epoch: 14 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 149.219/s, 149.219/s/gpu LR: 0.000108 Logit Scale: 26.686 Contrastive_loss: 0.17239 (0.16071) Loss: 0.17239 (0.16071) 2025-03-19,15:33:27 | INFO | Train Epoch: 14 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.374/s, 149.374/s/gpu LR: 0.000108 Logit Scale: 26.714 Contrastive_loss: 0.21987 (0.16125) Loss: 0.21987 (0.16125) 2025-03-19,15:33:48 | INFO | Train Epoch: 14 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.815/s, 149.815/s/gpu LR: 0.000108 Logit Scale: 26.724 Contrastive_loss: 0.22110 (0.16178) Loss: 0.22110 (0.16178) 2025-03-19,15:34:10 | INFO | Train Epoch: 14 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 149.819/s, 149.819/s/gpu LR: 0.000108 Logit Scale: 26.691 Contrastive_loss: 0.078089 (0.16104) Loss: 0.078089 (0.16104) 2025-03-19,15:34:31 | INFO | Train Epoch: 14 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 148.634/s, 148.634/s/gpu LR: 0.000108 Logit Scale: 26.747 Contrastive_loss: 0.035079 (0.15994) Loss: 0.035079 (0.15994) 2025-03-19,15:34:53 | INFO | Train Epoch: 14 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 150.082/s, 150.082/s/gpu LR: 0.000108 Logit Scale: 26.759 Contrastive_loss: 0.11926 (0.15958) Loss: 0.11926 (0.15958) 2025-03-19,15:35:14 | INFO | Train Epoch: 14 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 148.624/s, 148.624/s/gpu LR: 0.000108 Logit Scale: 26.756 Contrastive_loss: 0.14415 (0.15945) Loss: 0.14415 (0.15945) 2025-03-19,15:35:36 | INFO | Train Epoch: 14 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 147.694/s, 147.694/s/gpu LR: 0.000108 Logit Scale: 26.753 Contrastive_loss: 0.049969 (0.15851) Loss: 0.049969 (0.15851) 2025-03-19,15:35:58 | INFO | Train Epoch: 14 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.220, 146.604/s, 146.604/s/gpu LR: 0.000108 Logit Scale: 26.710 Contrastive_loss: 0.16988 (0.15861) Loss: 0.16988 (0.15861) 2025-03-19,15:36:20 | INFO | Train Epoch: 14 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.220, 152.157/s, 152.157/s/gpu LR: 0.000108 Logit Scale: 26.697 Contrastive_loss: 0.37942 (0.16047) Loss: 0.37942 (0.16047) 2025-03-19,15:36:41 | INFO | Train Epoch: 14 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.213, 148.584/s, 148.584/s/gpu LR: 0.000108 Logit Scale: 26.708 Contrastive_loss: 0.35275 (0.16207) Loss: 0.35275 (0.16207) 2025-03-19,15:37:03 | INFO | Train Epoch: 14 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 148.972/s, 148.972/s/gpu LR: 0.000108 Logit Scale: 26.700 Contrastive_loss: 0.31022 (0.16329) Loss: 0.31022 (0.16329) 2025-03-19,15:37:24 | INFO | Train Epoch: 14 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 150.445/s, 150.445/s/gpu LR: 0.000107 Logit Scale: 26.684 Contrastive_loss: 0.29634 (0.16438) Loss: 0.29634 (0.16438) 2025-03-19,15:37:46 | INFO | Train Epoch: 14 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 144.980/s, 144.980/s/gpu LR: 0.000107 Logit Scale: 26.669 Contrastive_loss: 0.068241 (0.16360) Loss: 0.068241 (0.16360) 2025-03-19,15:38:07 | INFO | Train Epoch: 14 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 148.291/s, 148.291/s/gpu LR: 0.000107 Logit Scale: 26.674 Contrastive_loss: 0.26095 (0.16439) Loss: 0.26095 (0.16439) 2025-03-19,15:38:28 | INFO | Train Epoch: 14 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 149.557/s, 149.557/s/gpu LR: 0.000107 Logit Scale: 26.670 Contrastive_loss: 0.22942 (0.16491) Loss: 0.22942 (0.16491) 2025-03-19,15:38:50 | INFO | Train Epoch: 14 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 151.074/s, 151.074/s/gpu LR: 0.000107 Logit Scale: 26.675 Contrastive_loss: 0.19668 (0.16516) Loss: 0.19668 (0.16516) 2025-03-19,15:39:11 | INFO | Train Epoch: 14 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.469/s, 148.469/s/gpu LR: 0.000107 Logit Scale: 26.679 Contrastive_loss: 0.071416 (0.16442) Loss: 0.071416 (0.16442) 2025-03-19,15:39:33 | INFO | Train Epoch: 14 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 149.913/s, 149.913/s/gpu LR: 0.000107 Logit Scale: 26.647 Contrastive_loss: 0.17811 (0.16453) Loss: 0.17811 (0.16453) 2025-03-19,15:39:54 | INFO | Train Epoch: 14 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 148.703/s, 148.703/s/gpu LR: 0.000107 Logit Scale: 26.627 Contrastive_loss: 0.12351 (0.16421) Loss: 0.12351 (0.16421) 2025-03-19,15:40:16 | INFO | Train Epoch: 14 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 151.438/s, 151.438/s/gpu LR: 0.000107 Logit Scale: 26.642 Contrastive_loss: 0.20620 (0.16453) Loss: 0.20620 (0.16453) 2025-03-19,15:40:37 | INFO | Train Epoch: 14 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 151.605/s, 151.605/s/gpu LR: 0.000107 Logit Scale: 26.653 Contrastive_loss: 0.029633 (0.16350) Loss: 0.029633 (0.16350) 2025-03-19,15:40:59 | INFO | Train Epoch: 14 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 148.211/s, 148.211/s/gpu LR: 0.000107 Logit Scale: 26.626 Contrastive_loss: 0.29313 (0.16448) Loss: 0.29313 (0.16448) 2025-03-19,15:41:20 | INFO | Train Epoch: 14 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 147.519/s, 147.519/s/gpu LR: 0.000107 Logit Scale: 26.668 Contrastive_loss: 0.17601 (0.16457) Loss: 0.17601 (0.16457) 2025-03-19,15:41:42 | INFO | Train Epoch: 14 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 150.726/s, 150.726/s/gpu LR: 0.000107 Logit Scale: 26.677 Contrastive_loss: 0.10826 (0.16415) Loss: 0.10826 (0.16415) 2025-03-19,15:42:03 | INFO | Train Epoch: 14 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 147.725/s, 147.725/s/gpu LR: 0.000107 Logit Scale: 26.639 Contrastive_loss: 0.071386 (0.16346) Loss: 0.071386 (0.16346) 2025-03-19,15:42:25 | INFO | Train Epoch: 14 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 148.144/s, 148.144/s/gpu LR: 0.000107 Logit Scale: 26.621 Contrastive_loss: 0.060013 (0.16270) Loss: 0.060013 (0.16270) 2025-03-19,15:42:46 | INFO | Train Epoch: 14 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.218, 144.826/s, 144.826/s/gpu LR: 0.000107 Logit Scale: 26.609 Contrastive_loss: 0.25004 (0.16334) Loss: 0.25004 (0.16334) 2025-03-19,15:43:08 | INFO | Train Epoch: 14 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.220, 147.616/s, 147.616/s/gpu LR: 0.000107 Logit Scale: 26.623 Contrastive_loss: 0.25528 (0.16401) Loss: 0.25528 (0.16401) 2025-03-19,15:43:30 | INFO | Train Epoch: 14 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.083/s, 148.083/s/gpu LR: 0.000107 Logit Scale: 26.604 Contrastive_loss: 0.16851 (0.16404) Loss: 0.16851 (0.16404) 2025-03-19,15:43:51 | INFO | Train Epoch: 14 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 150.020/s, 150.020/s/gpu LR: 0.000107 Logit Scale: 26.657 Contrastive_loss: 0.12381 (0.16375) Loss: 0.12381 (0.16375) 2025-03-19,15:44:13 | INFO | Train Epoch: 14 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.870/s, 147.870/s/gpu LR: 0.000107 Logit Scale: 26.684 Contrastive_loss: 0.10824 (0.16336) Loss: 0.10824 (0.16336) 2025-03-19,15:44:35 | INFO | Train Epoch: 14 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 147.245/s, 147.245/s/gpu LR: 0.000107 Logit Scale: 26.710 Contrastive_loss: 0.028780 (0.16241) Loss: 0.028780 (0.16241) 2025-03-19,15:44:56 | INFO | Train Epoch: 14 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 149.404/s, 149.404/s/gpu LR: 0.000107 Logit Scale: 26.698 Contrastive_loss: 0.091174 (0.16191) Loss: 0.091174 (0.16191) 2025-03-19,15:45:18 | INFO | Train Epoch: 14 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.423/s, 149.423/s/gpu LR: 0.000106 Logit Scale: 26.701 Contrastive_loss: 0.098041 (0.16147) Loss: 0.098041 (0.16147) 2025-03-19,15:45:39 | INFO | Train Epoch: 14 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 149.538/s, 149.538/s/gpu LR: 0.000106 Logit Scale: 26.677 Contrastive_loss: 0.18053 (0.16160) Loss: 0.18053 (0.16160) 2025-03-19,15:46:01 | INFO | Train Epoch: 14 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 148.761/s, 148.761/s/gpu LR: 0.000106 Logit Scale: 26.655 Contrastive_loss: 0.25433 (0.16224) Loss: 0.25433 (0.16224) 2025-03-19,15:46:22 | INFO | Train Epoch: 14 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 147.962/s, 147.962/s/gpu LR: 0.000106 Logit Scale: 26.667 Contrastive_loss: 0.17147 (0.16230) Loss: 0.17147 (0.16230) 2025-03-19,15:46:44 | INFO | Train Epoch: 14 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.536/s, 149.536/s/gpu LR: 0.000106 Logit Scale: 26.670 Contrastive_loss: 0.10329 (0.16190) Loss: 0.10329 (0.16190) 2025-03-19,15:47:05 | INFO | Train Epoch: 14 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 144.639/s, 144.639/s/gpu LR: 0.000106 Logit Scale: 26.660 Contrastive_loss: 0.10740 (0.16153) Loss: 0.10740 (0.16153) 2025-03-19,15:47:28 | INFO | Train Epoch: 14 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.223, 144.884/s, 144.884/s/gpu LR: 0.000106 Logit Scale: 26.691 Contrastive_loss: 0.11056 (0.16119) Loss: 0.11056 (0.16119) 2025-03-19,15:47:50 | INFO | Train Epoch: 14 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.222, 145.522/s, 145.522/s/gpu LR: 0.000106 Logit Scale: 26.664 Contrastive_loss: 0.32844 (0.16230) Loss: 0.32844 (0.16230) 2025-03-19,15:48:12 | INFO | Train Epoch: 14 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.219, 145.263/s, 145.263/s/gpu LR: 0.000106 Logit Scale: 26.682 Contrastive_loss: 0.25435 (0.16291) Loss: 0.25435 (0.16291) 2025-03-19,15:48:34 | INFO | Train Epoch: 14 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.224, 145.634/s, 145.634/s/gpu LR: 0.000106 Logit Scale: 26.707 Contrastive_loss: 0.10287 (0.16251) Loss: 0.10287 (0.16251) 2025-03-19,15:48:56 | INFO | Train Epoch: 14 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.222, 143.661/s, 143.661/s/gpu LR: 0.000106 Logit Scale: 26.698 Contrastive_loss: 0.10468 (0.16214) Loss: 0.10468 (0.16214) 2025-03-19,15:49:18 | INFO | Train Epoch: 14 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.222, 145.515/s, 145.515/s/gpu LR: 0.000106 Logit Scale: 26.689 Contrastive_loss: 0.22578 (0.16255) Loss: 0.22578 (0.16255) 2025-03-19,15:49:40 | INFO | Train Epoch: 14 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 149.567/s, 149.567/s/gpu LR: 0.000106 Logit Scale: 26.679 Contrastive_loss: 0.087355 (0.16207) Loss: 0.087355 (0.16207) 2025-03-19,15:50:01 | INFO | Train Epoch: 14 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 150.256/s, 150.256/s/gpu LR: 0.000106 Logit Scale: 26.699 Contrastive_loss: 0.062393 (0.16143) Loss: 0.062393 (0.16143) 2025-03-19,15:50:23 | INFO | Train Epoch: 14 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 148.675/s, 148.675/s/gpu LR: 0.000106 Logit Scale: 26.702 Contrastive_loss: 0.16828 (0.16148) Loss: 0.16828 (0.16148) 2025-03-19,15:50:45 | INFO | Train Epoch: 14 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 149.749/s, 149.749/s/gpu LR: 0.000106 Logit Scale: 26.713 Contrastive_loss: 0.53629 (0.16383) Loss: 0.53629 (0.16383) 2025-03-19,15:51:06 | INFO | Train Epoch: 14 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 148.176/s, 148.176/s/gpu LR: 0.000106 Logit Scale: 26.694 Contrastive_loss: 0.17261 (0.16389) Loss: 0.17261 (0.16389) 2025-03-19,15:51:28 | INFO | Train Epoch: 14 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 150.868/s, 150.868/s/gpu LR: 0.000106 Logit Scale: 26.669 Contrastive_loss: 0.26668 (0.16453) Loss: 0.26668 (0.16453) 2025-03-19,15:51:49 | INFO | Train Epoch: 14 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 148.595/s, 148.595/s/gpu LR: 0.000106 Logit Scale: 26.674 Contrastive_loss: 0.17243 (0.16458) Loss: 0.17243 (0.16458) 2025-03-19,15:52:11 | INFO | Train Epoch: 14 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 150.393/s, 150.393/s/gpu LR: 0.000106 Logit Scale: 26.649 Contrastive_loss: 0.49091 (0.16658) Loss: 0.49091 (0.16658) 2025-03-19,15:52:32 | INFO | Train Epoch: 14 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.213, 149.700/s, 149.700/s/gpu LR: 0.000106 Logit Scale: 26.672 Contrastive_loss: 0.060953 (0.16593) Loss: 0.060953 (0.16593) 2025-03-19,15:52:53 | INFO | Train Epoch: 14 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 149.730/s, 149.730/s/gpu LR: 0.000106 Logit Scale: 26.675 Contrastive_loss: 0.10874 (0.16559) Loss: 0.10874 (0.16559) 2025-03-19,15:53:15 | INFO | Train Epoch: 14 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 144.537/s, 144.537/s/gpu LR: 0.000106 Logit Scale: 26.659 Contrastive_loss: 0.083381 (0.16509) Loss: 0.083381 (0.16509) 2025-03-19,15:53:36 | INFO | Train Epoch: 14 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 148.701/s, 148.701/s/gpu LR: 0.000105 Logit Scale: 26.675 Contrastive_loss: 0.15312 (0.16502) Loss: 0.15312 (0.16502) 2025-03-19,15:53:58 | INFO | Train Epoch: 14 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 148.013/s, 148.013/s/gpu LR: 0.000105 Logit Scale: 26.714 Contrastive_loss: 0.060698 (0.16440) Loss: 0.060698 (0.16440) 2025-03-19,15:54:19 | INFO | Train Epoch: 14 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 148.133/s, 148.133/s/gpu LR: 0.000105 Logit Scale: 26.697 Contrastive_loss: 0.16694 (0.16441) Loss: 0.16694 (0.16441) 2025-03-19,15:54:41 | INFO | Train Epoch: 14 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 150.535/s, 150.535/s/gpu LR: 0.000105 Logit Scale: 26.699 Contrastive_loss: 0.15743 (0.16437) Loss: 0.15743 (0.16437) 2025-03-19,15:55:03 | INFO | Train Epoch: 14 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 147.491/s, 147.491/s/gpu LR: 0.000105 Logit Scale: 26.738 Contrastive_loss: 0.14671 (0.16427) Loss: 0.14671 (0.16427) 2025-03-19,15:55:25 | INFO | Train Epoch: 14 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 144.821/s, 144.821/s/gpu LR: 0.000105 Logit Scale: 26.736 Contrastive_loss: 0.24695 (0.16475) Loss: 0.24695 (0.16475) 2025-03-19,15:55:47 | INFO | Train Epoch: 14 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 147.739/s, 147.739/s/gpu LR: 0.000105 Logit Scale: 26.788 Contrastive_loss: 0.13105 (0.16456) Loss: 0.13105 (0.16456) 2025-03-19,15:56:08 | INFO | Train Epoch: 14 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 147.417/s, 147.417/s/gpu LR: 0.000105 Logit Scale: 26.751 Contrastive_loss: 0.23174 (0.16494) Loss: 0.23174 (0.16494) 2025-03-19,15:56:30 | INFO | Train Epoch: 14 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 147.049/s, 147.049/s/gpu LR: 0.000105 Logit Scale: 26.748 Contrastive_loss: 0.32724 (0.16587) Loss: 0.32724 (0.16587) 2025-03-19,15:56:52 | INFO | Train Epoch: 14 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.218, 144.864/s, 144.864/s/gpu LR: 0.000105 Logit Scale: 26.774 Contrastive_loss: 0.36429 (0.16700) Loss: 0.36429 (0.16700) 2025-03-19,15:57:13 | INFO | Train Epoch: 14 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.217, 148.110/s, 148.110/s/gpu LR: 0.000105 Logit Scale: 26.753 Contrastive_loss: 0.24472 (0.16744) Loss: 0.24472 (0.16744) 2025-03-19,15:57:35 | INFO | Train Epoch: 14 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 148.383/s, 148.383/s/gpu LR: 0.000105 Logit Scale: 26.719 Contrastive_loss: 0.21402 (0.16770) Loss: 0.21402 (0.16770) 2025-03-19,15:57:56 | INFO | Train Epoch: 14 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 150.880/s, 150.880/s/gpu LR: 0.000105 Logit Scale: 26.743 Contrastive_loss: 0.14001 (0.16754) Loss: 0.14001 (0.16754) 2025-03-19,15:58:18 | INFO | Train Epoch: 14 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 147.814/s, 147.814/s/gpu LR: 0.000105 Logit Scale: 26.730 Contrastive_loss: 0.18999 (0.16767) Loss: 0.18999 (0.16767) 2025-03-19,15:58:39 | INFO | Train Epoch: 14 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 146.753/s, 146.753/s/gpu LR: 0.000105 Logit Scale: 26.751 Contrastive_loss: 0.38714 (0.16888) Loss: 0.38714 (0.16888) 2025-03-19,15:59:01 | INFO | Train Epoch: 14 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 147.245/s, 147.245/s/gpu LR: 0.000105 Logit Scale: 26.736 Contrastive_loss: 0.29754 (0.16959) Loss: 0.29754 (0.16959) 2025-03-19,15:59:23 | INFO | Train Epoch: 14 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 150.260/s, 150.260/s/gpu LR: 0.000105 Logit Scale: 26.721 Contrastive_loss: 0.11590 (0.16929) Loss: 0.11590 (0.16929) 2025-03-19,15:59:44 | INFO | Train Epoch: 14 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.755/s, 149.755/s/gpu LR: 0.000105 Logit Scale: 26.677 Contrastive_loss: 0.13144 (0.16909) Loss: 0.13144 (0.16909) 2025-03-19,16:00:06 | INFO | Train Epoch: 14 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 149.869/s, 149.869/s/gpu LR: 0.000105 Logit Scale: 26.682 Contrastive_loss: 0.11439 (0.16879) Loss: 0.11439 (0.16879) 2025-03-19,16:00:27 | INFO | Train Epoch: 14 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.063/s, 149.063/s/gpu LR: 0.000105 Logit Scale: 26.689 Contrastive_loss: 0.18312 (0.16887) Loss: 0.18312 (0.16887) 2025-03-19,16:00:49 | INFO | Train Epoch: 14 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 150.102/s, 150.102/s/gpu LR: 0.000105 Logit Scale: 26.709 Contrastive_loss: 0.29351 (0.16954) Loss: 0.29351 (0.16954) 2025-03-19,16:01:10 | INFO | Train Epoch: 14 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 150.057/s, 150.057/s/gpu LR: 0.000105 Logit Scale: 26.703 Contrastive_loss: 0.34027 (0.17044) Loss: 0.34027 (0.17044) 2025-03-19,16:01:32 | INFO | Train Epoch: 14 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 151.522/s, 151.522/s/gpu LR: 0.000104 Logit Scale: 26.719 Contrastive_loss: 0.11483 (0.17015) Loss: 0.11483 (0.17015) 2025-03-19,16:01:53 | INFO | Train Epoch: 14 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.644/s, 149.644/s/gpu LR: 0.000104 Logit Scale: 26.729 Contrastive_loss: 0.042128 (0.16948) Loss: 0.042128 (0.16948) 2025-03-19,16:02:15 | INFO | Train Epoch: 14 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 145.555/s, 145.555/s/gpu LR: 0.000104 Logit Scale: 26.710 Contrastive_loss: 0.16564 (0.16946) Loss: 0.16564 (0.16946) 2025-03-19,16:02:37 | INFO | Train Epoch: 14 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 149.709/s, 149.709/s/gpu LR: 0.000104 Logit Scale: 26.747 Contrastive_loss: 0.14121 (0.16931) Loss: 0.14121 (0.16931) 2025-03-19,16:02:58 | INFO | Train Epoch: 14 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 148.330/s, 148.330/s/gpu LR: 0.000104 Logit Scale: 26.742 Contrastive_loss: 0.24022 (0.16968) Loss: 0.24022 (0.16968) 2025-03-19,16:03:19 | INFO | Train Epoch: 14 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 150.287/s, 150.287/s/gpu LR: 0.000104 Logit Scale: 26.723 Contrastive_loss: 0.079403 (0.16921) Loss: 0.079403 (0.16921) 2025-03-19,16:03:41 | INFO | Train Epoch: 14 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.212, 151.739/s, 151.739/s/gpu LR: 0.000104 Logit Scale: 26.712 Contrastive_loss: 0.12083 (0.16896) Loss: 0.12083 (0.16896) 2025-03-19,16:04:02 | INFO | Train Epoch: 14 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.213, 149.552/s, 149.552/s/gpu LR: 0.000104 Logit Scale: 26.715 Contrastive_loss: 0.10048 (0.16861) Loss: 0.10048 (0.16861) 2025-03-19,16:04:23 | INFO | Train Epoch: 14 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 150.148/s, 150.148/s/gpu LR: 0.000104 Logit Scale: 26.741 Contrastive_loss: 0.17720 (0.16866) Loss: 0.17720 (0.16866) 2025-03-19,16:04:45 | INFO | Train Epoch: 14 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.595/s, 149.595/s/gpu LR: 0.000104 Logit Scale: 26.723 Contrastive_loss: 0.34997 (0.16957) Loss: 0.34997 (0.16957) 2025-03-19,16:05:06 | INFO | Train Epoch: 14 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.555/s, 149.555/s/gpu LR: 0.000104 Logit Scale: 26.725 Contrastive_loss: 0.048847 (0.16897) Loss: 0.048847 (0.16897) 2025-03-19,16:05:28 | INFO | Train Epoch: 14 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.645/s, 149.645/s/gpu LR: 0.000104 Logit Scale: 26.726 Contrastive_loss: 0.20253 (0.16913) Loss: 0.20253 (0.16913) 2025-03-19,16:05:49 | INFO | Train Epoch: 14 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 151.257/s, 151.257/s/gpu LR: 0.000104 Logit Scale: 26.761 Contrastive_loss: 0.11187 (0.16885) Loss: 0.11187 (0.16885) 2025-03-19,16:06:10 | INFO | Train Epoch: 14 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 149.272/s, 149.272/s/gpu LR: 0.000104 Logit Scale: 26.761 Contrastive_loss: 0.24192 (0.16921) Loss: 0.24192 (0.16921) 2025-03-19,16:06:32 | INFO | Train Epoch: 14 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 149.619/s, 149.619/s/gpu LR: 0.000104 Logit Scale: 26.747 Contrastive_loss: 0.23917 (0.16956) Loss: 0.23917 (0.16956) 2025-03-19,16:06:53 | INFO | Train Epoch: 14 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 149.422/s, 149.422/s/gpu LR: 0.000104 Logit Scale: 26.760 Contrastive_loss: 0.16204 (0.16952) Loss: 0.16204 (0.16952) 2025-03-19,16:07:15 | INFO | Train Epoch: 14 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 150.217/s, 150.217/s/gpu LR: 0.000104 Logit Scale: 26.753 Contrastive_loss: 0.32496 (0.17028) Loss: 0.32496 (0.17028) 2025-03-19,16:07:36 | INFO | Train Epoch: 14 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.522/s, 149.522/s/gpu LR: 0.000104 Logit Scale: 26.741 Contrastive_loss: 0.024579 (0.16957) Loss: 0.024579 (0.16957) 2025-03-19,16:07:58 | INFO | Train Epoch: 14 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 149.897/s, 149.897/s/gpu LR: 0.000104 Logit Scale: 26.726 Contrastive_loss: 0.25128 (0.16996) Loss: 0.25128 (0.16996) 2025-03-19,16:08:19 | INFO | Train Epoch: 14 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 150.642/s, 150.642/s/gpu LR: 0.000104 Logit Scale: 26.772 Contrastive_loss: 0.24301 (0.17032) Loss: 0.24301 (0.17032) 2025-03-19,16:08:40 | INFO | Train Epoch: 14 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.213, 150.867/s, 150.867/s/gpu LR: 0.000104 Logit Scale: 26.758 Contrastive_loss: 0.053063 (0.16975) Loss: 0.053063 (0.16975) 2025-03-19,16:09:01 | INFO | Train Epoch: 14 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.212, 151.485/s, 151.485/s/gpu LR: 0.000104 Logit Scale: 26.761 Contrastive_loss: 0.15113 (0.16967) Loss: 0.15113 (0.16967) 2025-03-19,16:09:23 | INFO | Train Epoch: 14 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 147.811/s, 147.811/s/gpu LR: 0.000104 Logit Scale: 26.751 Contrastive_loss: 0.12751 (0.16947) Loss: 0.12751 (0.16947) 2025-03-19,16:09:44 | INFO | Train Epoch: 14 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 150.313/s, 150.313/s/gpu LR: 0.000103 Logit Scale: 26.747 Contrastive_loss: 0.11246 (0.16920) Loss: 0.11246 (0.16920) 2025-03-19,16:10:06 | INFO | Train Epoch: 14 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 150.177/s, 150.177/s/gpu LR: 0.000103 Logit Scale: 26.744 Contrastive_loss: 0.11280 (0.16893) Loss: 0.11280 (0.16893) 2025-03-19,16:10:27 | INFO | Train Epoch: 14 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 153.525/s, 153.525/s/gpu LR: 0.000103 Logit Scale: 26.744 Contrastive_loss: 0.10499 (0.16863) Loss: 0.10499 (0.16863) 2025-03-19,16:10:49 | INFO | Train Epoch: 14 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.780/s, 149.780/s/gpu LR: 0.000103 Logit Scale: 26.708 Contrastive_loss: 0.054031 (0.16810) Loss: 0.054031 (0.16810) 2025-03-19,16:11:10 | INFO | Train Epoch: 14 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.426/s, 150.426/s/gpu LR: 0.000103 Logit Scale: 26.717 Contrastive_loss: 0.15821 (0.16805) Loss: 0.15821 (0.16805) 2025-03-19,16:11:32 | INFO | Train Epoch: 14 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 149.183/s, 149.183/s/gpu LR: 0.000103 Logit Scale: 26.696 Contrastive_loss: 0.11134 (0.16779) Loss: 0.11134 (0.16779) 2025-03-19,16:11:53 | INFO | Train Epoch: 14 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.041/s, 148.041/s/gpu LR: 0.000103 Logit Scale: 26.712 Contrastive_loss: 0.056006 (0.16728) Loss: 0.056006 (0.16728) 2025-03-19,16:12:15 | INFO | Train Epoch: 14 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 150.140/s, 150.140/s/gpu LR: 0.000103 Logit Scale: 26.709 Contrastive_loss: 0.27591 (0.16778) Loss: 0.27591 (0.16778) 2025-03-19,16:12:36 | INFO | Train Epoch: 14 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.218, 145.617/s, 145.617/s/gpu LR: 0.000103 Logit Scale: 26.710 Contrastive_loss: 0.31660 (0.16845) Loss: 0.31660 (0.16845) 2025-03-19,16:12:58 | INFO | Train Epoch: 14 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.218, 147.021/s, 147.021/s/gpu LR: 0.000103 Logit Scale: 26.700 Contrastive_loss: 0.23968 (0.16878) Loss: 0.23968 (0.16878) 2025-03-19,16:13:20 | INFO | Train Epoch: 14 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.218, 142.775/s, 142.775/s/gpu LR: 0.000103 Logit Scale: 26.725 Contrastive_loss: 0.048508 (0.16823) Loss: 0.048508 (0.16823) 2025-03-19,16:13:42 | INFO | Train Epoch: 14 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.221, 144.930/s, 144.930/s/gpu LR: 0.000103 Logit Scale: 26.750 Contrastive_loss: 0.13436 (0.16808) Loss: 0.13436 (0.16808) 2025-03-19,16:14:04 | INFO | Train Epoch: 14 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.219, 147.067/s, 147.067/s/gpu LR: 0.000103 Logit Scale: 26.737 Contrastive_loss: 0.091128 (0.16774) Loss: 0.091128 (0.16774) 2025-03-19,16:14:26 | INFO | Train Epoch: 14 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 146.753/s, 146.753/s/gpu LR: 0.000103 Logit Scale: 26.703 Contrastive_loss: 0.12470 (0.16755) Loss: 0.12470 (0.16755) 2025-03-19,16:14:48 | INFO | Train Epoch: 14 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 146.372/s, 146.372/s/gpu LR: 0.000103 Logit Scale: 26.737 Contrastive_loss: 0.19301 (0.16766) Loss: 0.19301 (0.16766) 2025-03-19,16:15:10 | INFO | Train Epoch: 14 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 147.923/s, 147.923/s/gpu LR: 0.000103 Logit Scale: 26.707 Contrastive_loss: 0.027750 (0.16704) Loss: 0.027750 (0.16704) 2025-03-19,16:15:31 | INFO | Train Epoch: 14 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 140.454/s, 140.454/s/gpu LR: 0.000103 Logit Scale: 26.685 Contrastive_loss: 0.18677 (0.16713) Loss: 0.18677 (0.16713) 2025-03-19,16:15:53 | INFO | Train Epoch: 14 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.217, 148.194/s, 148.194/s/gpu LR: 0.000103 Logit Scale: 26.705 Contrastive_loss: 0.36598 (0.16800) Loss: 0.36598 (0.16800) 2025-03-19,16:16:14 | INFO | Train Epoch: 14 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 146.564/s, 146.564/s/gpu LR: 0.000103 Logit Scale: 26.673 Contrastive_loss: 0.22220 (0.16823) Loss: 0.22220 (0.16823) 2025-03-19,16:16:36 | INFO | Train Epoch: 14 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 151.450/s, 151.450/s/gpu LR: 0.000103 Logit Scale: 26.692 Contrastive_loss: 0.012847 (0.16756) Loss: 0.012847 (0.16756) 2025-03-19,16:16:57 | INFO | Train Epoch: 14 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 150.455/s, 150.455/s/gpu LR: 0.000103 Logit Scale: 26.735 Contrastive_loss: 0.19369 (0.16767) Loss: 0.19369 (0.16767) 2025-03-19,16:17:18 | INFO | Train Epoch: 14 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 149.631/s, 149.631/s/gpu LR: 0.000103 Logit Scale: 26.701 Contrastive_loss: 0.052784 (0.16718) Loss: 0.052784 (0.16718) 2025-03-19,16:17:40 | INFO | Train Epoch: 14 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 151.328/s, 151.328/s/gpu LR: 0.000103 Logit Scale: 26.711 Contrastive_loss: 0.28896 (0.16770) Loss: 0.28896 (0.16770) 2025-03-19,16:18:01 | INFO | Train Epoch: 14 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 148.889/s, 148.889/s/gpu LR: 0.000102 Logit Scale: 26.722 Contrastive_loss: 0.058694 (0.16724) Loss: 0.058694 (0.16724) 2025-03-19,16:18:23 | INFO | Train Epoch: 14 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 148.836/s, 148.836/s/gpu LR: 0.000102 Logit Scale: 26.716 Contrastive_loss: 0.16711 (0.16724) Loss: 0.16711 (0.16724) 2025-03-19,16:18:44 | INFO | Train Epoch: 14 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.424/s, 149.424/s/gpu LR: 0.000102 Logit Scale: 26.713 Contrastive_loss: 0.12488 (0.16706) Loss: 0.12488 (0.16706) 2025-03-19,16:19:05 | INFO | Train Epoch: 14 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 149.756/s, 149.756/s/gpu LR: 0.000102 Logit Scale: 26.731 Contrastive_loss: 0.035249 (0.16650) Loss: 0.035249 (0.16650) 2025-03-19,16:19:27 | INFO | Train Epoch: 14 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 144.614/s, 144.614/s/gpu LR: 0.000102 Logit Scale: 26.767 Contrastive_loss: 0.061379 (0.16606) Loss: 0.061379 (0.16606) 2025-03-19,16:19:49 | INFO | Train Epoch: 14 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.220, 145.498/s, 145.498/s/gpu LR: 0.000102 Logit Scale: 26.765 Contrastive_loss: 0.15637 (0.16602) Loss: 0.15637 (0.16602) 2025-03-19,16:19:57 | INFO | Train Epoch: 14 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.224, 146.955/s, 146.955/s/gpu LR: 0.000102 Logit Scale: 26.777 Contrastive_loss: 0.063365 (0.16560) Loss: 0.063365 (0.16560) 2025-03-19,16:19:58 | INFO | Eval Epoch: 15 [32 / 7443] Clip Loss: 3.293930 2025-03-19,16:20:03 | INFO | Eval Epoch: 15 [3232 / 7443] Clip Loss: 0.868398 2025-03-19,16:20:09 | INFO | Eval Epoch: 15 [6432 / 7443] Clip Loss: 0.665192 2025-03-19,16:20:12 | INFO | Eval Epoch: 15 image_to_text_mean_rank: 89.3562 image_to_text_median_rank: 7.0000 image_to_text_R@1: 0.1378 image_to_text_R@5: 0.4461 image_to_text_R@10: 0.6209 text_to_image_mean_rank: 57.7668 text_to_image_median_rank: 7.0000 text_to_image_R@1: 0.1380 text_to_image_R@5: 0.4478 text_to_image_R@10: 0.6238 clip_val_loss: 0.6247 epoch: 15.0000 num_samples: 7443.0000 2025-03-19,16:20:44 | INFO | Start epoch 15 2025-03-19,16:20:44 | INFO | Train Epoch: 15 [ 32/766009 (0%)] Data (t): 0.174 Batch (t): 0.383, 83.5229/s, 83.5229/s/gpu LR: 0.000102 Logit Scale: 26.777 Contrastive_loss: 0.24496 (0.24496) Loss: 0.24496 (0.24496) 2025-03-19,16:21:06 | INFO | Train Epoch: 15 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.218, 148.274/s, 148.274/s/gpu LR: 0.000102 Logit Scale: 26.804 Contrastive_loss: 0.078939 (0.16195) Loss: 0.078939 (0.16195) 2025-03-19,16:21:27 | INFO | Train Epoch: 15 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 147.081/s, 147.081/s/gpu LR: 0.000102 Logit Scale: 26.829 Contrastive_loss: 0.17376 (0.16589) Loss: 0.17376 (0.16589) 2025-03-19,16:21:49 | INFO | Train Epoch: 15 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 148.393/s, 148.393/s/gpu LR: 0.000102 Logit Scale: 26.856 Contrastive_loss: 0.13081 (0.15712) Loss: 0.13081 (0.15712) 2025-03-19,16:22:10 | INFO | Train Epoch: 15 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 150.110/s, 150.110/s/gpu LR: 0.000102 Logit Scale: 26.894 Contrastive_loss: 0.086764 (0.14305) Loss: 0.086764 (0.14305) 2025-03-19,16:22:32 | INFO | Train Epoch: 15 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.513/s, 149.513/s/gpu LR: 0.000102 Logit Scale: 26.886 Contrastive_loss: 0.041108 (0.12606) Loss: 0.041108 (0.12606) 2025-03-19,16:22:53 | INFO | Train Epoch: 15 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.744/s, 149.744/s/gpu LR: 0.000102 Logit Scale: 26.926 Contrastive_loss: 0.035262 (0.11309) Loss: 0.035262 (0.11309) 2025-03-19,16:23:15 | INFO | Train Epoch: 15 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 150.163/s, 150.163/s/gpu LR: 0.000102 Logit Scale: 26.961 Contrastive_loss: 0.051618 (0.10540) Loss: 0.051618 (0.10540) 2025-03-19,16:23:36 | INFO | Train Epoch: 15 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.341/s, 149.341/s/gpu LR: 0.000102 Logit Scale: 27.001 Contrastive_loss: 0.43712 (0.14226) Loss: 0.43712 (0.14226) 2025-03-19,16:23:58 | INFO | Train Epoch: 15 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 150.473/s, 150.473/s/gpu LR: 0.000102 Logit Scale: 26.973 Contrastive_loss: 0.25911 (0.15394) Loss: 0.25911 (0.15394) 2025-03-19,16:24:19 | INFO | Train Epoch: 15 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 149.299/s, 149.299/s/gpu LR: 0.000102 Logit Scale: 26.992 Contrastive_loss: 0.14019 (0.15269) Loss: 0.14019 (0.15269) 2025-03-19,16:24:40 | INFO | Train Epoch: 15 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 150.207/s, 150.207/s/gpu LR: 0.000102 Logit Scale: 26.976 Contrastive_loss: 0.088982 (0.14738) Loss: 0.088982 (0.14738) 2025-03-19,16:25:02 | INFO | Train Epoch: 15 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.942/s, 147.942/s/gpu LR: 0.000102 Logit Scale: 26.972 Contrastive_loss: 0.13485 (0.14642) Loss: 0.13485 (0.14642) 2025-03-19,16:25:23 | INFO | Train Epoch: 15 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 150.458/s, 150.458/s/gpu LR: 0.000102 Logit Scale: 26.925 Contrastive_loss: 0.086118 (0.14211) Loss: 0.086118 (0.14211) 2025-03-19,16:25:45 | INFO | Train Epoch: 15 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 150.053/s, 150.053/s/gpu LR: 0.000102 Logit Scale: 26.974 Contrastive_loss: 0.11386 (0.14023) Loss: 0.11386 (0.14023) 2025-03-19,16:26:06 | INFO | Train Epoch: 15 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 148.671/s, 148.671/s/gpu LR: 0.000102 Logit Scale: 26.996 Contrastive_loss: 0.056062 (0.13497) Loss: 0.056062 (0.13497) 2025-03-19,16:26:28 | INFO | Train Epoch: 15 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 148.624/s, 148.624/s/gpu LR: 0.000102 Logit Scale: 26.998 Contrastive_loss: 0.091414 (0.13241) Loss: 0.091414 (0.13241) 2025-03-19,16:26:49 | INFO | Train Epoch: 15 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 150.498/s, 150.498/s/gpu LR: 0.000101 Logit Scale: 27.035 Contrastive_loss: 0.15627 (0.13373) Loss: 0.15627 (0.13373) 2025-03-19,16:27:11 | INFO | Train Epoch: 15 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.363/s, 149.363/s/gpu LR: 0.000101 Logit Scale: 27.010 Contrastive_loss: 0.18564 (0.13646) Loss: 0.18564 (0.13646) 2025-03-19,16:27:32 | INFO | Train Epoch: 15 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.223/s, 148.223/s/gpu LR: 0.000101 Logit Scale: 26.977 Contrastive_loss: 0.14055 (0.13667) Loss: 0.14055 (0.13667) 2025-03-19,16:27:53 | INFO | Train Epoch: 15 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.212, 152.018/s, 152.018/s/gpu LR: 0.000101 Logit Scale: 26.945 Contrastive_loss: 0.27548 (0.14328) Loss: 0.27548 (0.14328) 2025-03-19,16:28:15 | INFO | Train Epoch: 15 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.213, 147.210/s, 147.210/s/gpu LR: 0.000101 Logit Scale: 26.954 Contrastive_loss: 0.069975 (0.13995) Loss: 0.069975 (0.13995) 2025-03-19,16:28:36 | INFO | Train Epoch: 15 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 147.631/s, 147.631/s/gpu LR: 0.000101 Logit Scale: 26.962 Contrastive_loss: 0.17113 (0.14130) Loss: 0.17113 (0.14130) 2025-03-19,16:28:58 | INFO | Train Epoch: 15 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 145.614/s, 145.614/s/gpu LR: 0.000101 Logit Scale: 27.012 Contrastive_loss: 0.084011 (0.13892) Loss: 0.084011 (0.13892) 2025-03-19,16:29:20 | INFO | Train Epoch: 15 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 145.804/s, 145.804/s/gpu LR: 0.000101 Logit Scale: 26.984 Contrastive_loss: 0.18235 (0.14065) Loss: 0.18235 (0.14065) 2025-03-19,16:29:42 | INFO | Train Epoch: 15 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 145.840/s, 145.840/s/gpu LR: 0.000101 Logit Scale: 26.939 Contrastive_loss: 0.22494 (0.14389) Loss: 0.22494 (0.14389) 2025-03-19,16:30:04 | INFO | Train Epoch: 15 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.218, 144.264/s, 144.264/s/gpu LR: 0.000101 Logit Scale: 26.949 Contrastive_loss: 0.0062008 (0.13879) Loss: 0.0062008 (0.13879) 2025-03-19,16:30:26 | INFO | Train Epoch: 15 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 151.701/s, 151.701/s/gpu LR: 0.000101 Logit Scale: 26.981 Contrastive_loss: 0.12526 (0.13831) Loss: 0.12526 (0.13831) 2025-03-19,16:30:47 | INFO | Train Epoch: 15 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.213, 148.340/s, 148.340/s/gpu LR: 0.000101 Logit Scale: 26.965 Contrastive_loss: 0.18937 (0.14007) Loss: 0.18937 (0.14007) 2025-03-19,16:31:09 | INFO | Train Epoch: 15 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 147.454/s, 147.454/s/gpu LR: 0.000101 Logit Scale: 26.986 Contrastive_loss: 0.010030 (0.13574) Loss: 0.010030 (0.13574) 2025-03-19,16:31:30 | INFO | Train Epoch: 15 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 148.563/s, 148.563/s/gpu LR: 0.000101 Logit Scale: 26.983 Contrastive_loss: 0.096855 (0.13448) Loss: 0.096855 (0.13448) 2025-03-19,16:31:52 | INFO | Train Epoch: 15 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 151.817/s, 151.817/s/gpu LR: 0.000101 Logit Scale: 26.944 Contrastive_loss: 0.064413 (0.13229) Loss: 0.064413 (0.13229) 2025-03-19,16:32:13 | INFO | Train Epoch: 15 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.212, 147.614/s, 147.614/s/gpu LR: 0.000101 Logit Scale: 26.960 Contrastive_loss: 0.057389 (0.13002) Loss: 0.057389 (0.13002) 2025-03-19,16:32:34 | INFO | Train Epoch: 15 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.690/s, 149.690/s/gpu LR: 0.000101 Logit Scale: 26.904 Contrastive_loss: 0.31608 (0.13550) Loss: 0.31608 (0.13550) 2025-03-19,16:32:56 | INFO | Train Epoch: 15 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 148.556/s, 148.556/s/gpu LR: 0.000101 Logit Scale: 26.896 Contrastive_loss: 0.092747 (0.13427) Loss: 0.092747 (0.13427) 2025-03-19,16:33:17 | INFO | Train Epoch: 15 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 150.495/s, 150.495/s/gpu LR: 0.000101 Logit Scale: 26.910 Contrastive_loss: 0.30992 (0.13915) Loss: 0.30992 (0.13915) 2025-03-19,16:33:39 | INFO | Train Epoch: 15 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.213, 152.370/s, 152.370/s/gpu LR: 0.000101 Logit Scale: 26.905 Contrastive_loss: 0.24088 (0.14190) Loss: 0.24088 (0.14190) 2025-03-19,16:34:00 | INFO | Train Epoch: 15 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.211, 151.382/s, 151.382/s/gpu LR: 0.000101 Logit Scale: 26.908 Contrastive_loss: 0.44250 (0.14981) Loss: 0.44250 (0.14981) 2025-03-19,16:34:21 | INFO | Train Epoch: 15 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 149.007/s, 149.007/s/gpu LR: 0.000101 Logit Scale: 26.923 Contrastive_loss: 0.064799 (0.14763) Loss: 0.064799 (0.14763) 2025-03-19,16:34:42 | INFO | Train Epoch: 15 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 150.351/s, 150.351/s/gpu LR: 0.000100 Logit Scale: 26.915 Contrastive_loss: 0.19559 (0.14883) Loss: 0.19559 (0.14883) 2025-03-19,16:35:04 | INFO | Train Epoch: 15 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.213, 149.879/s, 149.879/s/gpu LR: 0.000100 Logit Scale: 26.920 Contrastive_loss: 0.14928 (0.14884) Loss: 0.14928 (0.14884) 2025-03-19,16:35:25 | INFO | Train Epoch: 15 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.213, 148.413/s, 148.413/s/gpu LR: 0.000100 Logit Scale: 26.942 Contrastive_loss: 0.11216 (0.14797) Loss: 0.11216 (0.14797) 2025-03-19,16:35:47 | INFO | Train Epoch: 15 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.035/s, 147.035/s/gpu LR: 0.000100 Logit Scale: 26.922 Contrastive_loss: 0.17111 (0.14851) Loss: 0.17111 (0.14851) 2025-03-19,16:36:09 | INFO | Train Epoch: 15 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.219, 151.369/s, 151.369/s/gpu LR: 0.000100 Logit Scale: 26.917 Contrastive_loss: 0.18340 (0.14930) Loss: 0.18340 (0.14930) 2025-03-19,16:36:30 | INFO | Train Epoch: 15 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 142.768/s, 142.768/s/gpu LR: 0.000100 Logit Scale: 26.972 Contrastive_loss: 0.16758 (0.14971) Loss: 0.16758 (0.14971) 2025-03-19,16:36:51 | INFO | Train Epoch: 15 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.213, 150.989/s, 150.989/s/gpu LR: 0.000100 Logit Scale: 26.970 Contrastive_loss: 0.30124 (0.15300) Loss: 0.30124 (0.15300) 2025-03-19,16:37:13 | INFO | Train Epoch: 15 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.217, 151.967/s, 151.967/s/gpu LR: 0.000100 Logit Scale: 26.974 Contrastive_loss: 0.058348 (0.15099) Loss: 0.058348 (0.15099) 2025-03-19,16:37:34 | INFO | Train Epoch: 15 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.469/s, 149.469/s/gpu LR: 0.000100 Logit Scale: 26.969 Contrastive_loss: 0.25748 (0.15321) Loss: 0.25748 (0.15321) 2025-03-19,16:37:56 | INFO | Train Epoch: 15 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 148.064/s, 148.064/s/gpu LR: 0.000100 Logit Scale: 26.945 Contrastive_loss: 0.064429 (0.15139) Loss: 0.064429 (0.15139) 2025-03-19,16:38:17 | INFO | Train Epoch: 15 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.995/s, 149.995/s/gpu LR: 0.000100 Logit Scale: 26.937 Contrastive_loss: 0.060963 (0.14959) Loss: 0.060963 (0.14959) 2025-03-19,16:38:39 | INFO | Train Epoch: 15 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 150.019/s, 150.019/s/gpu LR: 0.000100 Logit Scale: 26.954 Contrastive_loss: 0.25101 (0.15157) Loss: 0.25101 (0.15157) 2025-03-19,16:39:00 | INFO | Train Epoch: 15 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 149.870/s, 149.870/s/gpu LR: 0.000100 Logit Scale: 26.934 Contrastive_loss: 0.16493 (0.15183) Loss: 0.16493 (0.15183) 2025-03-19,16:39:21 | INFO | Train Epoch: 15 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 151.687/s, 151.687/s/gpu LR: 0.000100 Logit Scale: 26.965 Contrastive_loss: 0.13046 (0.15143) Loss: 0.13046 (0.15143) 2025-03-19,16:39:43 | INFO | Train Epoch: 15 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 150.051/s, 150.051/s/gpu LR: 0.000100 Logit Scale: 27.021 Contrastive_loss: 0.063408 (0.14980) Loss: 0.063408 (0.14980) 2025-03-19,16:40:04 | INFO | Train Epoch: 15 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.212, 149.870/s, 149.870/s/gpu LR: 0.000100 Logit Scale: 27.025 Contrastive_loss: 0.23903 (0.15142) Loss: 0.23903 (0.15142) 2025-03-19,16:40:25 | INFO | Train Epoch: 15 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.212, 151.610/s, 151.610/s/gpu LR: 0.000100 Logit Scale: 27.004 Contrastive_loss: 0.17350 (0.15181) Loss: 0.17350 (0.15181) 2025-03-19,16:40:47 | INFO | Train Epoch: 15 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 147.229/s, 147.229/s/gpu LR: 0.000100 Logit Scale: 27.022 Contrastive_loss: 0.055464 (0.15012) Loss: 0.055464 (0.15012) 2025-03-19,16:41:09 | INFO | Train Epoch: 15 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.218, 146.087/s, 146.087/s/gpu LR: 0.000100 Logit Scale: 27.016 Contrastive_loss: 0.0098110 (0.14770) Loss: 0.0098110 (0.14770) 2025-03-19,16:41:31 | INFO | Train Epoch: 15 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.220, 146.112/s, 146.112/s/gpu LR: 0.000100 Logit Scale: 27.054 Contrastive_loss: 0.016366 (0.14548) Loss: 0.016366 (0.14548) 2025-03-19,16:41:52 | INFO | Train Epoch: 15 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.219, 148.495/s, 148.495/s/gpu LR: 0.000100 Logit Scale: 27.038 Contrastive_loss: 0.23411 (0.14696) Loss: 0.23411 (0.14696) 2025-03-19,16:42:14 | INFO | Train Epoch: 15 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 147.834/s, 147.834/s/gpu LR: 0.000100 Logit Scale: 27.019 Contrastive_loss: 0.060392 (0.14554) Loss: 0.060392 (0.14554) 2025-03-19,16:42:36 | INFO | Train Epoch: 15 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 148.700/s, 148.700/s/gpu LR: 0.000100 Logit Scale: 27.004 Contrastive_loss: 0.053338 (0.14405) Loss: 0.053338 (0.14405) 2025-03-19,16:42:58 | INFO | Train Epoch: 15 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.218, 147.154/s, 147.154/s/gpu LR: 0.000099 Logit Scale: 26.989 Contrastive_loss: 0.0040659 (0.14183) Loss: 0.0040659 (0.14183) 2025-03-19,16:43:19 | INFO | Train Epoch: 15 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.217, 149.254/s, 149.254/s/gpu LR: 0.000099 Logit Scale: 26.940 Contrastive_loss: 0.11960 (0.14148) Loss: 0.11960 (0.14148) 2025-03-19,16:43:41 | INFO | Train Epoch: 15 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.217, 147.444/s, 147.444/s/gpu LR: 0.000099 Logit Scale: 26.970 Contrastive_loss: 0.094567 (0.14076) Loss: 0.094567 (0.14076) 2025-03-19,16:44:02 | INFO | Train Epoch: 15 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.109/s, 149.109/s/gpu LR: 0.000099 Logit Scale: 26.900 Contrastive_loss: 0.31724 (0.14343) Loss: 0.31724 (0.14343) 2025-03-19,16:44:24 | INFO | Train Epoch: 15 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.789/s, 149.789/s/gpu LR: 0.000099 Logit Scale: 26.929 Contrastive_loss: 0.079753 (0.14248) Loss: 0.079753 (0.14248) 2025-03-19,16:44:45 | INFO | Train Epoch: 15 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 144.730/s, 144.730/s/gpu LR: 0.000099 Logit Scale: 26.876 Contrastive_loss: 0.22770 (0.14374) Loss: 0.22770 (0.14374) 2025-03-19,16:45:07 | INFO | Train Epoch: 15 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 150.529/s, 150.529/s/gpu LR: 0.000099 Logit Scale: 26.863 Contrastive_loss: 0.29984 (0.14600) Loss: 0.29984 (0.14600) 2025-03-19,16:45:28 | INFO | Train Epoch: 15 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 148.511/s, 148.511/s/gpu LR: 0.000099 Logit Scale: 26.886 Contrastive_loss: 0.12250 (0.14566) Loss: 0.12250 (0.14566) 2025-03-19,16:45:50 | INFO | Train Epoch: 15 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.541/s, 148.541/s/gpu LR: 0.000099 Logit Scale: 26.868 Contrastive_loss: 0.24182 (0.14702) Loss: 0.24182 (0.14702) 2025-03-19,16:46:12 | INFO | Train Epoch: 15 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.825/s, 149.825/s/gpu LR: 0.000099 Logit Scale: 26.894 Contrastive_loss: 0.19022 (0.14762) Loss: 0.19022 (0.14762) 2025-03-19,16:46:33 | INFO | Train Epoch: 15 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 148.224/s, 148.224/s/gpu LR: 0.000099 Logit Scale: 26.888 Contrastive_loss: 0.14694 (0.14761) Loss: 0.14694 (0.14761) 2025-03-19,16:46:54 | INFO | Train Epoch: 15 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 151.140/s, 151.140/s/gpu LR: 0.000099 Logit Scale: 26.914 Contrastive_loss: 0.34991 (0.15034) Loss: 0.34991 (0.15034) 2025-03-19,16:47:16 | INFO | Train Epoch: 15 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 149.209/s, 149.209/s/gpu LR: 0.000099 Logit Scale: 26.915 Contrastive_loss: 0.14266 (0.15024) Loss: 0.14266 (0.15024) 2025-03-19,16:47:38 | INFO | Train Epoch: 15 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 148.006/s, 148.006/s/gpu LR: 0.000099 Logit Scale: 26.871 Contrastive_loss: 0.027549 (0.14862) Loss: 0.027549 (0.14862) 2025-03-19,16:48:00 | INFO | Train Epoch: 15 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.218, 146.924/s, 146.924/s/gpu LR: 0.000099 Logit Scale: 26.842 Contrastive_loss: 0.029849 (0.14708) Loss: 0.029849 (0.14708) 2025-03-19,16:48:22 | INFO | Train Epoch: 15 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.219, 148.452/s, 148.452/s/gpu LR: 0.000099 Logit Scale: 26.867 Contrastive_loss: 0.20976 (0.14789) Loss: 0.20976 (0.14789) 2025-03-19,16:48:43 | INFO | Train Epoch: 15 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 149.408/s, 149.408/s/gpu LR: 0.000099 Logit Scale: 26.878 Contrastive_loss: 0.24434 (0.14911) Loss: 0.24434 (0.14911) 2025-03-19,16:49:05 | INFO | Train Epoch: 15 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.297/s, 147.297/s/gpu LR: 0.000099 Logit Scale: 26.902 Contrastive_loss: 0.027473 (0.14759) Loss: 0.027473 (0.14759) 2025-03-19,16:49:26 | INFO | Train Epoch: 15 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.218, 146.232/s, 146.232/s/gpu LR: 0.000099 Logit Scale: 26.933 Contrastive_loss: 0.13594 (0.14744) Loss: 0.13594 (0.14744) 2025-03-19,16:49:48 | INFO | Train Epoch: 15 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 149.909/s, 149.909/s/gpu LR: 0.000099 Logit Scale: 26.928 Contrastive_loss: 0.20563 (0.14815) Loss: 0.20563 (0.14815) 2025-03-19,16:50:10 | INFO | Train Epoch: 15 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.219, 145.465/s, 145.465/s/gpu LR: 0.000099 Logit Scale: 26.943 Contrastive_loss: 0.13732 (0.14802) Loss: 0.13732 (0.14802) 2025-03-19,16:50:32 | INFO | Train Epoch: 15 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.219, 143.617/s, 143.617/s/gpu LR: 0.000099 Logit Scale: 26.944 Contrastive_loss: 0.18184 (0.14842) Loss: 0.18184 (0.14842) 2025-03-19,16:50:53 | INFO | Train Epoch: 15 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.217, 147.593/s, 147.593/s/gpu LR: 0.000098 Logit Scale: 26.919 Contrastive_loss: 0.12137 (0.14811) Loss: 0.12137 (0.14811) 2025-03-19,16:51:15 | INFO | Train Epoch: 15 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.996/s, 148.996/s/gpu LR: 0.000098 Logit Scale: 26.914 Contrastive_loss: 0.086529 (0.14739) Loss: 0.086529 (0.14739) 2025-03-19,16:51:37 | INFO | Train Epoch: 15 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.219, 146.018/s, 146.018/s/gpu LR: 0.000098 Logit Scale: 26.902 Contrastive_loss: 0.22914 (0.14833) Loss: 0.22914 (0.14833) 2025-03-19,16:51:59 | INFO | Train Epoch: 15 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 147.665/s, 147.665/s/gpu LR: 0.000098 Logit Scale: 26.924 Contrastive_loss: 0.049842 (0.14721) Loss: 0.049842 (0.14721) 2025-03-19,16:52:20 | INFO | Train Epoch: 15 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 152.432/s, 152.432/s/gpu LR: 0.000098 Logit Scale: 26.891 Contrastive_loss: 0.047982 (0.14609) Loss: 0.047982 (0.14609) 2025-03-19,16:52:41 | INFO | Train Epoch: 15 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.211, 150.518/s, 150.518/s/gpu LR: 0.000098 Logit Scale: 26.857 Contrastive_loss: 0.076511 (0.14532) Loss: 0.076511 (0.14532) 2025-03-19,16:53:03 | INFO | Train Epoch: 15 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 150.348/s, 150.348/s/gpu LR: 0.000098 Logit Scale: 26.870 Contrastive_loss: 0.16833 (0.14557) Loss: 0.16833 (0.14557) 2025-03-19,16:53:24 | INFO | Train Epoch: 15 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.554/s, 149.554/s/gpu LR: 0.000098 Logit Scale: 26.846 Contrastive_loss: 0.13182 (0.14543) Loss: 0.13182 (0.14543) 2025-03-19,16:53:45 | INFO | Train Epoch: 15 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.213, 151.847/s, 151.847/s/gpu LR: 0.000098 Logit Scale: 26.840 Contrastive_loss: 0.12790 (0.14524) Loss: 0.12790 (0.14524) 2025-03-19,16:54:07 | INFO | Train Epoch: 15 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.213, 147.424/s, 147.424/s/gpu LR: 0.000098 Logit Scale: 26.893 Contrastive_loss: 0.070552 (0.14444) Loss: 0.070552 (0.14444) 2025-03-19,16:54:28 | INFO | Train Epoch: 15 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 150.665/s, 150.665/s/gpu LR: 0.000098 Logit Scale: 26.889 Contrastive_loss: 0.081156 (0.14378) Loss: 0.081156 (0.14378) 2025-03-19,16:54:50 | INFO | Train Epoch: 15 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 150.582/s, 150.582/s/gpu LR: 0.000098 Logit Scale: 26.945 Contrastive_loss: 0.34526 (0.14587) Loss: 0.34526 (0.14587) 2025-03-19,16:55:11 | INFO | Train Epoch: 15 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 149.677/s, 149.677/s/gpu LR: 0.000098 Logit Scale: 26.930 Contrastive_loss: 0.036596 (0.14475) Loss: 0.036596 (0.14475) 2025-03-19,16:55:33 | INFO | Train Epoch: 15 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.682/s, 149.682/s/gpu LR: 0.000098 Logit Scale: 26.929 Contrastive_loss: 0.12277 (0.14452) Loss: 0.12277 (0.14452) 2025-03-19,16:55:54 | INFO | Train Epoch: 15 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.931/s, 148.931/s/gpu LR: 0.000098 Logit Scale: 26.948 Contrastive_loss: 0.080281 (0.14388) Loss: 0.080281 (0.14388) 2025-03-19,16:56:16 | INFO | Train Epoch: 15 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 147.472/s, 147.472/s/gpu LR: 0.000098 Logit Scale: 26.928 Contrastive_loss: 0.019316 (0.14263) Loss: 0.019316 (0.14263) 2025-03-19,16:56:37 | INFO | Train Epoch: 15 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.579/s, 149.579/s/gpu LR: 0.000098 Logit Scale: 26.932 Contrastive_loss: 0.13292 (0.14253) Loss: 0.13292 (0.14253) 2025-03-19,16:56:59 | INFO | Train Epoch: 15 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 150.019/s, 150.019/s/gpu LR: 0.000098 Logit Scale: 26.887 Contrastive_loss: 0.22659 (0.14336) Loss: 0.22659 (0.14336) 2025-03-19,16:57:20 | INFO | Train Epoch: 15 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.213, 150.016/s, 150.016/s/gpu LR: 0.000098 Logit Scale: 26.884 Contrastive_loss: 0.076666 (0.14271) Loss: 0.076666 (0.14271) 2025-03-19,16:57:42 | INFO | Train Epoch: 15 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 148.895/s, 148.895/s/gpu LR: 0.000098 Logit Scale: 26.894 Contrastive_loss: 0.019276 (0.14152) Loss: 0.019276 (0.14152) 2025-03-19,16:58:03 | INFO | Train Epoch: 15 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 149.405/s, 149.405/s/gpu LR: 0.000098 Logit Scale: 26.913 Contrastive_loss: 0.051934 (0.14067) Loss: 0.051934 (0.14067) 2025-03-19,16:58:25 | INFO | Train Epoch: 15 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 148.553/s, 148.553/s/gpu LR: 0.000098 Logit Scale: 26.913 Contrastive_loss: 0.15774 (0.14083) Loss: 0.15774 (0.14083) 2025-03-19,16:58:46 | INFO | Train Epoch: 15 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 148.181/s, 148.181/s/gpu LR: 0.000098 Logit Scale: 26.871 Contrastive_loss: 0.022503 (0.13972) Loss: 0.022503 (0.13972) 2025-03-19,16:59:08 | INFO | Train Epoch: 15 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.688/s, 149.688/s/gpu LR: 0.000097 Logit Scale: 26.895 Contrastive_loss: 0.37306 (0.14189) Loss: 0.37306 (0.14189) 2025-03-19,16:59:29 | INFO | Train Epoch: 15 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 150.168/s, 150.168/s/gpu LR: 0.000097 Logit Scale: 26.865 Contrastive_loss: 0.059830 (0.14113) Loss: 0.059830 (0.14113) 2025-03-19,16:59:50 | INFO | Train Epoch: 15 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 150.437/s, 150.437/s/gpu LR: 0.000097 Logit Scale: 26.842 Contrastive_loss: 0.10109 (0.14077) Loss: 0.10109 (0.14077) 2025-03-19,17:00:12 | INFO | Train Epoch: 15 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.677/s, 149.677/s/gpu LR: 0.000097 Logit Scale: 26.829 Contrastive_loss: 0.055672 (0.14000) Loss: 0.055672 (0.14000) 2025-03-19,17:00:33 | INFO | Train Epoch: 15 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 150.012/s, 150.012/s/gpu LR: 0.000097 Logit Scale: 26.807 Contrastive_loss: 0.30160 (0.14144) Loss: 0.30160 (0.14144) 2025-03-19,17:00:55 | INFO | Train Epoch: 15 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 149.268/s, 149.268/s/gpu LR: 0.000097 Logit Scale: 26.816 Contrastive_loss: 0.40684 (0.14379) Loss: 0.40684 (0.14379) 2025-03-19,17:01:16 | INFO | Train Epoch: 15 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 146.552/s, 146.552/s/gpu LR: 0.000097 Logit Scale: 26.827 Contrastive_loss: 0.15073 (0.14385) Loss: 0.15073 (0.14385) 2025-03-19,17:01:38 | INFO | Train Epoch: 15 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 148.625/s, 148.625/s/gpu LR: 0.000097 Logit Scale: 26.833 Contrastive_loss: 0.021668 (0.14279) Loss: 0.021668 (0.14279) 2025-03-19,17:01:59 | INFO | Train Epoch: 15 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.641/s, 149.641/s/gpu LR: 0.000097 Logit Scale: 26.867 Contrastive_loss: 0.048841 (0.14198) Loss: 0.048841 (0.14198) 2025-03-19,17:02:21 | INFO | Train Epoch: 15 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 148.722/s, 148.722/s/gpu LR: 0.000097 Logit Scale: 26.872 Contrastive_loss: 0.11217 (0.14173) Loss: 0.11217 (0.14173) 2025-03-19,17:02:43 | INFO | Train Epoch: 15 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 146.081/s, 146.081/s/gpu LR: 0.000097 Logit Scale: 26.886 Contrastive_loss: 0.068649 (0.14111) Loss: 0.068649 (0.14111) 2025-03-19,17:03:04 | INFO | Train Epoch: 15 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 148.610/s, 148.610/s/gpu LR: 0.000097 Logit Scale: 26.912 Contrastive_loss: 0.17765 (0.14141) Loss: 0.17765 (0.14141) 2025-03-19,17:03:26 | INFO | Train Epoch: 15 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 149.225/s, 149.225/s/gpu LR: 0.000097 Logit Scale: 26.900 Contrastive_loss: 0.039640 (0.14057) Loss: 0.039640 (0.14057) 2025-03-19,17:03:47 | INFO | Train Epoch: 15 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 149.506/s, 149.506/s/gpu LR: 0.000097 Logit Scale: 26.925 Contrastive_loss: 0.23217 (0.14132) Loss: 0.23217 (0.14132) 2025-03-19,17:04:09 | INFO | Train Epoch: 15 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 150.599/s, 150.599/s/gpu LR: 0.000097 Logit Scale: 26.911 Contrastive_loss: 0.33443 (0.14291) Loss: 0.33443 (0.14291) 2025-03-19,17:04:30 | INFO | Train Epoch: 15 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.213, 149.660/s, 149.660/s/gpu LR: 0.000097 Logit Scale: 26.920 Contrastive_loss: 0.024060 (0.14194) Loss: 0.024060 (0.14194) 2025-03-19,17:04:51 | INFO | Train Epoch: 15 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 150.368/s, 150.368/s/gpu LR: 0.000097 Logit Scale: 26.922 Contrastive_loss: 0.14385 (0.14196) Loss: 0.14385 (0.14196) 2025-03-19,17:05:13 | INFO | Train Epoch: 15 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.213, 149.619/s, 149.619/s/gpu LR: 0.000097 Logit Scale: 26.925 Contrastive_loss: 0.40319 (0.14405) Loss: 0.40319 (0.14405) 2025-03-19,17:05:34 | INFO | Train Epoch: 15 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 148.072/s, 148.072/s/gpu LR: 0.000097 Logit Scale: 26.935 Contrastive_loss: 0.18536 (0.14437) Loss: 0.18536 (0.14437) 2025-03-19,17:05:56 | INFO | Train Epoch: 15 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.354/s, 148.354/s/gpu LR: 0.000097 Logit Scale: 26.892 Contrastive_loss: 0.46099 (0.14687) Loss: 0.46099 (0.14687) 2025-03-19,17:06:17 | INFO | Train Epoch: 15 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 148.205/s, 148.205/s/gpu LR: 0.000097 Logit Scale: 26.905 Contrastive_loss: 0.10989 (0.14658) Loss: 0.10989 (0.14658) 2025-03-19,17:06:39 | INFO | Train Epoch: 15 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.155/s, 148.155/s/gpu LR: 0.000097 Logit Scale: 26.914 Contrastive_loss: 0.043474 (0.14578) Loss: 0.043474 (0.14578) 2025-03-19,17:07:01 | INFO | Train Epoch: 15 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.217, 150.702/s, 150.702/s/gpu LR: 0.000096 Logit Scale: 26.934 Contrastive_loss: 0.082816 (0.14529) Loss: 0.082816 (0.14529) 2025-03-19,17:07:22 | INFO | Train Epoch: 15 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 149.435/s, 149.435/s/gpu LR: 0.000096 Logit Scale: 26.909 Contrastive_loss: 0.15248 (0.14535) Loss: 0.15248 (0.14535) 2025-03-19,17:07:44 | INFO | Train Epoch: 15 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 150.730/s, 150.730/s/gpu LR: 0.000096 Logit Scale: 26.883 Contrastive_loss: 0.15900 (0.14545) Loss: 0.15900 (0.14545) 2025-03-19,17:08:05 | INFO | Train Epoch: 15 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 149.823/s, 149.823/s/gpu LR: 0.000096 Logit Scale: 26.902 Contrastive_loss: 0.094338 (0.14507) Loss: 0.094338 (0.14507) 2025-03-19,17:08:26 | INFO | Train Epoch: 15 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 149.911/s, 149.911/s/gpu LR: 0.000096 Logit Scale: 26.927 Contrastive_loss: 0.14582 (0.14507) Loss: 0.14582 (0.14507) 2025-03-19,17:08:48 | INFO | Train Epoch: 15 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 149.512/s, 149.512/s/gpu LR: 0.000096 Logit Scale: 26.946 Contrastive_loss: 0.078206 (0.14458) Loss: 0.078206 (0.14458) 2025-03-19,17:09:09 | INFO | Train Epoch: 15 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 149.727/s, 149.727/s/gpu LR: 0.000096 Logit Scale: 26.929 Contrastive_loss: 0.079770 (0.14410) Loss: 0.079770 (0.14410) 2025-03-19,17:09:30 | INFO | Train Epoch: 15 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 148.438/s, 148.438/s/gpu LR: 0.000096 Logit Scale: 26.931 Contrastive_loss: 0.097513 (0.14376) Loss: 0.097513 (0.14376) 2025-03-19,17:09:52 | INFO | Train Epoch: 15 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 148.797/s, 148.797/s/gpu LR: 0.000096 Logit Scale: 26.920 Contrastive_loss: 0.061898 (0.14317) Loss: 0.061898 (0.14317) 2025-03-19,17:10:13 | INFO | Train Epoch: 15 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.810/s, 147.810/s/gpu LR: 0.000096 Logit Scale: 26.941 Contrastive_loss: 0.10436 (0.14289) Loss: 0.10436 (0.14289) 2025-03-19,17:10:35 | INFO | Train Epoch: 15 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 149.491/s, 149.491/s/gpu LR: 0.000096 Logit Scale: 26.936 Contrastive_loss: 0.15197 (0.14295) Loss: 0.15197 (0.14295) 2025-03-19,17:10:56 | INFO | Train Epoch: 15 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.213, 150.024/s, 150.024/s/gpu LR: 0.000096 Logit Scale: 26.904 Contrastive_loss: 0.059469 (0.14236) Loss: 0.059469 (0.14236) 2025-03-19,17:11:18 | INFO | Train Epoch: 15 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 150.866/s, 150.866/s/gpu LR: 0.000096 Logit Scale: 26.916 Contrastive_loss: 0.20612 (0.14281) Loss: 0.20612 (0.14281) 2025-03-19,17:11:39 | INFO | Train Epoch: 15 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.212, 151.582/s, 151.582/s/gpu LR: 0.000096 Logit Scale: 26.917 Contrastive_loss: 0.37797 (0.14446) Loss: 0.37797 (0.14446) 2025-03-19,17:12:00 | INFO | Train Epoch: 15 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.212, 149.886/s, 149.886/s/gpu LR: 0.000096 Logit Scale: 26.905 Contrastive_loss: 0.061221 (0.14388) Loss: 0.061221 (0.14388) 2025-03-19,17:12:21 | INFO | Train Epoch: 15 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 149.117/s, 149.117/s/gpu LR: 0.000096 Logit Scale: 26.918 Contrastive_loss: 0.073360 (0.14339) Loss: 0.073360 (0.14339) 2025-03-19,17:12:43 | INFO | Train Epoch: 15 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 150.857/s, 150.857/s/gpu LR: 0.000096 Logit Scale: 26.928 Contrastive_loss: 0.24105 (0.14406) Loss: 0.24105 (0.14406) 2025-03-19,17:13:04 | INFO | Train Epoch: 15 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 151.932/s, 151.932/s/gpu LR: 0.000096 Logit Scale: 26.915 Contrastive_loss: 0.020738 (0.14322) Loss: 0.020738 (0.14322) 2025-03-19,17:13:26 | INFO | Train Epoch: 15 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 150.257/s, 150.257/s/gpu LR: 0.000096 Logit Scale: 26.947 Contrastive_loss: 0.097552 (0.14291) Loss: 0.097552 (0.14291) 2025-03-19,17:13:47 | INFO | Train Epoch: 15 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 150.233/s, 150.233/s/gpu LR: 0.000096 Logit Scale: 26.983 Contrastive_loss: 0.10195 (0.14264) Loss: 0.10195 (0.14264) 2025-03-19,17:14:09 | INFO | Train Epoch: 15 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 147.914/s, 147.914/s/gpu LR: 0.000096 Logit Scale: 26.978 Contrastive_loss: 0.24010 (0.14329) Loss: 0.24010 (0.14329) 2025-03-19,17:14:30 | INFO | Train Epoch: 15 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 145.634/s, 145.634/s/gpu LR: 0.000096 Logit Scale: 26.976 Contrastive_loss: 0.11525 (0.14310) Loss: 0.11525 (0.14310) 2025-03-19,17:14:52 | INFO | Train Epoch: 15 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.218, 146.803/s, 146.803/s/gpu LR: 0.000096 Logit Scale: 26.963 Contrastive_loss: 0.054404 (0.14252) Loss: 0.054404 (0.14252) 2025-03-19,17:15:14 | INFO | Train Epoch: 15 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.218, 144.500/s, 144.500/s/gpu LR: 0.000095 Logit Scale: 26.971 Contrastive_loss: 0.19849 (0.14288) Loss: 0.19849 (0.14288) 2025-03-19,17:15:36 | INFO | Train Epoch: 15 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 148.273/s, 148.273/s/gpu LR: 0.000095 Logit Scale: 26.954 Contrastive_loss: 0.037187 (0.14220) Loss: 0.037187 (0.14220) 2025-03-19,17:15:57 | INFO | Train Epoch: 15 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 144.363/s, 144.363/s/gpu LR: 0.000095 Logit Scale: 26.931 Contrastive_loss: 0.085506 (0.14183) Loss: 0.085506 (0.14183) 2025-03-19,17:16:19 | INFO | Train Epoch: 15 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 148.313/s, 148.313/s/gpu LR: 0.000095 Logit Scale: 26.955 Contrastive_loss: 0.0088513 (0.14098) Loss: 0.0088513 (0.14098) 2025-03-19,17:16:40 | INFO | Train Epoch: 15 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 150.429/s, 150.429/s/gpu LR: 0.000095 Logit Scale: 26.941 Contrastive_loss: 0.20736 (0.14140) Loss: 0.20736 (0.14140) 2025-03-19,17:17:01 | INFO | Train Epoch: 15 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.398/s, 149.398/s/gpu LR: 0.000095 Logit Scale: 26.966 Contrastive_loss: 0.11846 (0.14126) Loss: 0.11846 (0.14126) 2025-03-19,17:17:23 | INFO | Train Epoch: 15 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.577/s, 149.577/s/gpu LR: 0.000095 Logit Scale: 26.978 Contrastive_loss: 0.12452 (0.14115) Loss: 0.12452 (0.14115) 2025-03-19,17:17:44 | INFO | Train Epoch: 15 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.809/s, 149.809/s/gpu LR: 0.000095 Logit Scale: 26.971 Contrastive_loss: 0.11087 (0.14096) Loss: 0.11087 (0.14096) 2025-03-19,17:18:06 | INFO | Train Epoch: 15 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 146.468/s, 146.468/s/gpu LR: 0.000095 Logit Scale: 26.967 Contrastive_loss: 0.056583 (0.14044) Loss: 0.056583 (0.14044) 2025-03-19,17:18:28 | INFO | Train Epoch: 15 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 148.084/s, 148.084/s/gpu LR: 0.000095 Logit Scale: 26.949 Contrastive_loss: 0.13218 (0.14039) Loss: 0.13218 (0.14039) 2025-03-19,17:18:49 | INFO | Train Epoch: 15 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 146.844/s, 146.844/s/gpu LR: 0.000095 Logit Scale: 26.951 Contrastive_loss: 0.093811 (0.14010) Loss: 0.093811 (0.14010) 2025-03-19,17:19:11 | INFO | Train Epoch: 15 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 146.906/s, 146.906/s/gpu LR: 0.000095 Logit Scale: 26.982 Contrastive_loss: 0.037270 (0.13947) Loss: 0.037270 (0.13947) 2025-03-19,17:19:33 | INFO | Train Epoch: 15 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.220, 144.225/s, 144.225/s/gpu LR: 0.000095 Logit Scale: 26.986 Contrastive_loss: 0.17623 (0.13970) Loss: 0.17623 (0.13970) 2025-03-19,17:19:55 | INFO | Train Epoch: 15 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 144.470/s, 144.470/s/gpu LR: 0.000095 Logit Scale: 27.014 Contrastive_loss: 0.46507 (0.14166) Loss: 0.46507 (0.14166) 2025-03-19,17:20:17 | INFO | Train Epoch: 15 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 148.875/s, 148.875/s/gpu LR: 0.000095 Logit Scale: 26.995 Contrastive_loss: 0.047609 (0.14109) Loss: 0.047609 (0.14109) 2025-03-19,17:20:39 | INFO | Train Epoch: 15 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 147.645/s, 147.645/s/gpu LR: 0.000095 Logit Scale: 26.976 Contrastive_loss: 0.23319 (0.14164) Loss: 0.23319 (0.14164) 2025-03-19,17:21:00 | INFO | Train Epoch: 15 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 150.938/s, 150.938/s/gpu LR: 0.000095 Logit Scale: 26.964 Contrastive_loss: 0.089446 (0.14133) Loss: 0.089446 (0.14133) 2025-03-19,17:21:22 | INFO | Train Epoch: 15 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 145.544/s, 145.544/s/gpu LR: 0.000095 Logit Scale: 26.992 Contrastive_loss: 0.24262 (0.14193) Loss: 0.24262 (0.14193) 2025-03-19,17:21:43 | INFO | Train Epoch: 15 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 150.320/s, 150.320/s/gpu LR: 0.000095 Logit Scale: 26.992 Contrastive_loss: 0.14909 (0.14197) Loss: 0.14909 (0.14197) 2025-03-19,17:22:05 | INFO | Train Epoch: 15 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 148.257/s, 148.257/s/gpu LR: 0.000095 Logit Scale: 27.019 Contrastive_loss: 0.42935 (0.14364) Loss: 0.42935 (0.14364) 2025-03-19,17:22:26 | INFO | Train Epoch: 15 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.213, 150.410/s, 150.410/s/gpu LR: 0.000095 Logit Scale: 26.989 Contrastive_loss: 0.26756 (0.14436) Loss: 0.26756 (0.14436) 2025-03-19,17:22:48 | INFO | Train Epoch: 15 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 147.479/s, 147.479/s/gpu LR: 0.000095 Logit Scale: 26.970 Contrastive_loss: 0.12911 (0.14427) Loss: 0.12911 (0.14427) 2025-03-19,17:23:09 | INFO | Train Epoch: 15 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 148.418/s, 148.418/s/gpu LR: 0.000095 Logit Scale: 26.989 Contrastive_loss: 0.14036 (0.14425) Loss: 0.14036 (0.14425) 2025-03-19,17:23:31 | INFO | Train Epoch: 15 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 146.317/s, 146.317/s/gpu LR: 0.000094 Logit Scale: 27.019 Contrastive_loss: 0.085028 (0.14391) Loss: 0.085028 (0.14391) 2025-03-19,17:23:53 | INFO | Train Epoch: 15 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 147.263/s, 147.263/s/gpu LR: 0.000094 Logit Scale: 26.990 Contrastive_loss: 0.13513 (0.14386) Loss: 0.13513 (0.14386) 2025-03-19,17:24:14 | INFO | Train Epoch: 15 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 146.136/s, 146.136/s/gpu LR: 0.000094 Logit Scale: 27.027 Contrastive_loss: 0.16700 (0.14399) Loss: 0.16700 (0.14399) 2025-03-19,17:24:36 | INFO | Train Epoch: 15 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 147.983/s, 147.983/s/gpu LR: 0.000094 Logit Scale: 27.017 Contrastive_loss: 0.10171 (0.14376) Loss: 0.10171 (0.14376) 2025-03-19,17:24:58 | INFO | Train Epoch: 15 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.212, 152.348/s, 152.348/s/gpu LR: 0.000094 Logit Scale: 27.025 Contrastive_loss: 0.18402 (0.14398) Loss: 0.18402 (0.14398) 2025-03-19,17:25:19 | INFO | Train Epoch: 15 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 150.129/s, 150.129/s/gpu LR: 0.000094 Logit Scale: 27.032 Contrastive_loss: 0.26049 (0.14462) Loss: 0.26049 (0.14462) 2025-03-19,17:25:40 | INFO | Train Epoch: 15 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.972/s, 149.972/s/gpu LR: 0.000094 Logit Scale: 27.030 Contrastive_loss: 0.25377 (0.14522) Loss: 0.25377 (0.14522) 2025-03-19,17:26:02 | INFO | Train Epoch: 15 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.182/s, 149.182/s/gpu LR: 0.000094 Logit Scale: 27.033 Contrastive_loss: 0.15474 (0.14528) Loss: 0.15474 (0.14528) 2025-03-19,17:26:23 | INFO | Train Epoch: 15 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.313/s, 149.313/s/gpu LR: 0.000094 Logit Scale: 27.012 Contrastive_loss: 0.047436 (0.14474) Loss: 0.047436 (0.14474) 2025-03-19,17:26:45 | INFO | Train Epoch: 15 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 148.221/s, 148.221/s/gpu LR: 0.000094 Logit Scale: 27.016 Contrastive_loss: 0.045335 (0.14421) Loss: 0.045335 (0.14421) 2025-03-19,17:27:06 | INFO | Train Epoch: 15 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.745/s, 149.745/s/gpu LR: 0.000094 Logit Scale: 27.017 Contrastive_loss: 0.077925 (0.14385) Loss: 0.077925 (0.14385) 2025-03-19,17:27:28 | INFO | Train Epoch: 15 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.553/s, 149.553/s/gpu LR: 0.000094 Logit Scale: 27.043 Contrastive_loss: 0.13407 (0.14380) Loss: 0.13407 (0.14380) 2025-03-19,17:27:49 | INFO | Train Epoch: 15 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 150.023/s, 150.023/s/gpu LR: 0.000094 Logit Scale: 27.048 Contrastive_loss: 0.17390 (0.14396) Loss: 0.17390 (0.14396) 2025-03-19,17:28:11 | INFO | Train Epoch: 15 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.541/s, 148.541/s/gpu LR: 0.000094 Logit Scale: 27.057 Contrastive_loss: 0.18039 (0.14415) Loss: 0.18039 (0.14415) 2025-03-19,17:28:32 | INFO | Train Epoch: 15 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.314/s, 148.314/s/gpu LR: 0.000094 Logit Scale: 27.076 Contrastive_loss: 0.21739 (0.14454) Loss: 0.21739 (0.14454) 2025-03-19,17:28:54 | INFO | Train Epoch: 15 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.978/s, 148.978/s/gpu LR: 0.000094 Logit Scale: 27.081 Contrastive_loss: 0.058248 (0.14408) Loss: 0.058248 (0.14408) 2025-03-19,17:29:15 | INFO | Train Epoch: 15 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 149.222/s, 149.222/s/gpu LR: 0.000094 Logit Scale: 27.079 Contrastive_loss: 0.11931 (0.14396) Loss: 0.11931 (0.14396) 2025-03-19,17:29:36 | INFO | Train Epoch: 15 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.212, 150.807/s, 150.807/s/gpu LR: 0.000094 Logit Scale: 27.051 Contrastive_loss: 0.096230 (0.14371) Loss: 0.096230 (0.14371) 2025-03-19,17:29:58 | INFO | Train Epoch: 15 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 148.420/s, 148.420/s/gpu LR: 0.000094 Logit Scale: 27.048 Contrastive_loss: 0.19574 (0.14398) Loss: 0.19574 (0.14398) 2025-03-19,17:30:20 | INFO | Train Epoch: 15 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 144.812/s, 144.812/s/gpu LR: 0.000094 Logit Scale: 27.063 Contrastive_loss: 0.12273 (0.14387) Loss: 0.12273 (0.14387) 2025-03-19,17:30:42 | INFO | Train Epoch: 15 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 146.513/s, 146.513/s/gpu LR: 0.000094 Logit Scale: 27.071 Contrastive_loss: 0.077485 (0.14353) Loss: 0.077485 (0.14353) 2025-03-19,17:31:04 | INFO | Train Epoch: 15 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.221, 144.602/s, 144.602/s/gpu LR: 0.000094 Logit Scale: 27.065 Contrastive_loss: 0.0074277 (0.14284) Loss: 0.0074277 (0.14284) 2025-03-19,17:31:26 | INFO | Train Epoch: 15 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.220, 145.797/s, 145.797/s/gpu LR: 0.000093 Logit Scale: 27.060 Contrastive_loss: 0.14565 (0.14285) Loss: 0.14565 (0.14285) 2025-03-19,17:31:48 | INFO | Train Epoch: 15 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.217, 149.503/s, 149.503/s/gpu LR: 0.000093 Logit Scale: 27.044 Contrastive_loss: 0.17131 (0.14299) Loss: 0.17131 (0.14299) 2025-03-19,17:32:09 | INFO | Train Epoch: 15 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 148.748/s, 148.748/s/gpu LR: 0.000093 Logit Scale: 27.042 Contrastive_loss: 0.16374 (0.14310) Loss: 0.16374 (0.14310) 2025-03-19,17:32:31 | INFO | Train Epoch: 15 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 147.697/s, 147.697/s/gpu LR: 0.000093 Logit Scale: 27.072 Contrastive_loss: 0.083317 (0.14280) Loss: 0.083317 (0.14280) 2025-03-19,17:32:52 | INFO | Train Epoch: 15 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 149.689/s, 149.689/s/gpu LR: 0.000093 Logit Scale: 27.054 Contrastive_loss: 0.23930 (0.14328) Loss: 0.23930 (0.14328) 2025-03-19,17:33:14 | INFO | Train Epoch: 15 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 148.734/s, 148.734/s/gpu LR: 0.000093 Logit Scale: 27.046 Contrastive_loss: 0.052079 (0.14283) Loss: 0.052079 (0.14283) 2025-03-19,17:33:35 | INFO | Train Epoch: 15 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.213, 151.253/s, 151.253/s/gpu LR: 0.000093 Logit Scale: 27.081 Contrastive_loss: 0.16073 (0.14292) Loss: 0.16073 (0.14292) 2025-03-19,17:33:57 | INFO | Train Epoch: 15 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 148.118/s, 148.118/s/gpu LR: 0.000093 Logit Scale: 27.053 Contrastive_loss: 0.069227 (0.14256) Loss: 0.069227 (0.14256) 2025-03-19,17:34:19 | INFO | Train Epoch: 15 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.291/s, 148.291/s/gpu LR: 0.000093 Logit Scale: 27.068 Contrastive_loss: 0.082672 (0.14227) Loss: 0.082672 (0.14227) 2025-03-19,17:34:40 | INFO | Train Epoch: 15 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.218, 139.545/s, 139.545/s/gpu LR: 0.000093 Logit Scale: 27.045 Contrastive_loss: 0.27168 (0.14289) Loss: 0.27168 (0.14289) 2025-03-19,17:35:02 | INFO | Train Epoch: 15 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 151.061/s, 151.061/s/gpu LR: 0.000093 Logit Scale: 27.048 Contrastive_loss: 0.12313 (0.14280) Loss: 0.12313 (0.14280) 2025-03-19,17:35:23 | INFO | Train Epoch: 15 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 147.829/s, 147.829/s/gpu LR: 0.000093 Logit Scale: 27.018 Contrastive_loss: 0.10463 (0.14261) Loss: 0.10463 (0.14261) 2025-03-19,17:35:45 | INFO | Train Epoch: 15 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 147.618/s, 147.618/s/gpu LR: 0.000093 Logit Scale: 27.042 Contrastive_loss: 0.20658 (0.14292) Loss: 0.20658 (0.14292) 2025-03-19,17:36:07 | INFO | Train Epoch: 15 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.218, 147.139/s, 147.139/s/gpu LR: 0.000093 Logit Scale: 27.024 Contrastive_loss: 0.089422 (0.14267) Loss: 0.089422 (0.14267) 2025-03-19,17:36:29 | INFO | Train Epoch: 15 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 148.478/s, 148.478/s/gpu LR: 0.000093 Logit Scale: 27.004 Contrastive_loss: 0.18085 (0.14285) Loss: 0.18085 (0.14285) 2025-03-19,17:36:50 | INFO | Train Epoch: 15 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 148.575/s, 148.575/s/gpu LR: 0.000093 Logit Scale: 27.069 Contrastive_loss: 0.073200 (0.14252) Loss: 0.073200 (0.14252) 2025-03-19,17:37:12 | INFO | Train Epoch: 15 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 147.613/s, 147.613/s/gpu LR: 0.000093 Logit Scale: 27.047 Contrastive_loss: 0.13454 (0.14248) Loss: 0.13454 (0.14248) 2025-03-19,17:37:33 | INFO | Train Epoch: 15 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.930/s, 148.930/s/gpu LR: 0.000093 Logit Scale: 27.023 Contrastive_loss: 0.075516 (0.14217) Loss: 0.075516 (0.14217) 2025-03-19,17:37:55 | INFO | Train Epoch: 15 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 148.145/s, 148.145/s/gpu LR: 0.000093 Logit Scale: 26.993 Contrastive_loss: 0.27795 (0.14280) Loss: 0.27795 (0.14280) 2025-03-19,17:38:16 | INFO | Train Epoch: 15 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 150.525/s, 150.525/s/gpu LR: 0.000093 Logit Scale: 26.978 Contrastive_loss: 0.27293 (0.14340) Loss: 0.27293 (0.14340) 2025-03-19,17:38:38 | INFO | Train Epoch: 15 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.238/s, 149.238/s/gpu LR: 0.000093 Logit Scale: 27.008 Contrastive_loss: 0.068871 (0.14306) Loss: 0.068871 (0.14306) 2025-03-19,17:38:59 | INFO | Train Epoch: 15 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.029/s, 149.029/s/gpu LR: 0.000093 Logit Scale: 27.056 Contrastive_loss: 0.034336 (0.14256) Loss: 0.034336 (0.14256) 2025-03-19,17:39:21 | INFO | Train Epoch: 15 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 150.547/s, 150.547/s/gpu LR: 0.000093 Logit Scale: 27.057 Contrastive_loss: 0.28720 (0.14322) Loss: 0.28720 (0.14322) 2025-03-19,17:39:42 | INFO | Train Epoch: 15 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.212, 150.481/s, 150.481/s/gpu LR: 0.000092 Logit Scale: 27.048 Contrastive_loss: 0.036903 (0.14274) Loss: 0.036903 (0.14274) 2025-03-19,17:40:03 | INFO | Train Epoch: 15 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 148.326/s, 148.326/s/gpu LR: 0.000092 Logit Scale: 27.034 Contrastive_loss: 0.045131 (0.14230) Loss: 0.045131 (0.14230) 2025-03-19,17:40:25 | INFO | Train Epoch: 15 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.113/s, 150.113/s/gpu LR: 0.000092 Logit Scale: 27.051 Contrastive_loss: 0.30363 (0.14302) Loss: 0.30363 (0.14302) 2025-03-19,17:40:46 | INFO | Train Epoch: 15 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.702/s, 149.702/s/gpu LR: 0.000092 Logit Scale: 27.049 Contrastive_loss: 0.20929 (0.14332) Loss: 0.20929 (0.14332) 2025-03-19,17:41:08 | INFO | Train Epoch: 15 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.537/s, 148.537/s/gpu LR: 0.000092 Logit Scale: 27.063 Contrastive_loss: 0.20037 (0.14357) Loss: 0.20037 (0.14357) 2025-03-19,17:41:30 | INFO | Train Epoch: 15 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 147.817/s, 147.817/s/gpu LR: 0.000092 Logit Scale: 27.070 Contrastive_loss: 0.041710 (0.14312) Loss: 0.041710 (0.14312) 2025-03-19,17:41:51 | INFO | Train Epoch: 15 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 150.632/s, 150.632/s/gpu LR: 0.000092 Logit Scale: 27.040 Contrastive_loss: 0.16291 (0.14321) Loss: 0.16291 (0.14321) 2025-03-19,17:42:12 | INFO | Train Epoch: 15 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.211, 151.217/s, 151.217/s/gpu LR: 0.000092 Logit Scale: 27.017 Contrastive_loss: 0.49774 (0.14476) Loss: 0.49774 (0.14476) 2025-03-19,17:42:34 | INFO | Train Epoch: 15 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.212, 148.390/s, 148.390/s/gpu LR: 0.000092 Logit Scale: 27.019 Contrastive_loss: 0.042448 (0.14431) Loss: 0.042448 (0.14431) 2025-03-19,17:42:55 | INFO | Train Epoch: 15 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 148.241/s, 148.241/s/gpu LR: 0.000092 Logit Scale: 27.038 Contrastive_loss: 0.15552 (0.14436) Loss: 0.15552 (0.14436) 2025-03-19,17:43:17 | INFO | Train Epoch: 15 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 149.304/s, 149.304/s/gpu LR: 0.000092 Logit Scale: 27.044 Contrastive_loss: 0.059216 (0.14399) Loss: 0.059216 (0.14399) 2025-03-19,17:43:38 | INFO | Train Epoch: 15 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 148.651/s, 148.651/s/gpu LR: 0.000092 Logit Scale: 27.063 Contrastive_loss: 0.063461 (0.14365) Loss: 0.063461 (0.14365) 2025-03-19,17:44:00 | INFO | Train Epoch: 15 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.608/s, 149.608/s/gpu LR: 0.000092 Logit Scale: 27.053 Contrastive_loss: 0.014349 (0.14309) Loss: 0.014349 (0.14309) 2025-03-19,17:44:21 | INFO | Train Epoch: 15 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 148.999/s, 148.999/s/gpu LR: 0.000092 Logit Scale: 27.070 Contrastive_loss: 0.032789 (0.14262) Loss: 0.032789 (0.14262) 2025-03-19,17:44:43 | INFO | Train Epoch: 15 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 150.620/s, 150.620/s/gpu LR: 0.000092 Logit Scale: 27.060 Contrastive_loss: 0.073965 (0.14233) Loss: 0.073965 (0.14233) 2025-03-19,17:45:04 | INFO | Train Epoch: 15 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.675/s, 147.675/s/gpu LR: 0.000092 Logit Scale: 27.023 Contrastive_loss: 0.039195 (0.14189) Loss: 0.039195 (0.14189) 2025-03-19,17:45:26 | INFO | Train Epoch: 15 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 147.697/s, 147.697/s/gpu LR: 0.000092 Logit Scale: 27.039 Contrastive_loss: 0.091698 (0.14168) Loss: 0.091698 (0.14168) 2025-03-19,17:45:48 | INFO | Train Epoch: 15 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 146.167/s, 146.167/s/gpu LR: 0.000092 Logit Scale: 27.047 Contrastive_loss: 0.031427 (0.14122) Loss: 0.031427 (0.14122) 2025-03-19,17:46:09 | INFO | Train Epoch: 15 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 148.928/s, 148.928/s/gpu LR: 0.000092 Logit Scale: 27.071 Contrastive_loss: 0.052953 (0.14085) Loss: 0.052953 (0.14085) 2025-03-19,17:46:31 | INFO | Train Epoch: 15 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.216, 148.212/s, 148.212/s/gpu LR: 0.000092 Logit Scale: 27.047 Contrastive_loss: 0.21994 (0.14118) Loss: 0.21994 (0.14118) 2025-03-19,17:46:39 | INFO | Train Epoch: 15 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.217, 149.442/s, 149.442/s/gpu LR: 0.000092 Logit Scale: 27.044 Contrastive_loss: 0.13960 (0.14117) Loss: 0.13960 (0.14117) 2025-03-19,17:46:39 | INFO | Eval Epoch: 16 [32 / 7443] Clip Loss: 3.450142 2025-03-19,17:46:45 | INFO | Eval Epoch: 16 [3232 / 7443] Clip Loss: 0.835061 2025-03-19,17:46:51 | INFO | Eval Epoch: 16 [6432 / 7443] Clip Loss: 0.639798 2025-03-19,17:46:53 | INFO | Eval Epoch: 16 image_to_text_mean_rank: 82.6601 image_to_text_median_rank: 6.0000 image_to_text_R@1: 0.1456 image_to_text_R@5: 0.4543 image_to_text_R@10: 0.6337 text_to_image_mean_rank: 54.9635 text_to_image_median_rank: 6.0000 text_to_image_R@1: 0.1424 text_to_image_R@5: 0.4564 text_to_image_R@10: 0.6340 clip_val_loss: 0.6057 epoch: 16.0000 num_samples: 7443.0000 2025-03-19,17:47:26 | INFO | Start epoch 16 2025-03-19,17:47:27 | INFO | Train Epoch: 16 [ 32/766009 (0%)] Data (t): 0.172 Batch (t): 0.369, 86.7041/s, 86.7041/s/gpu LR: 0.000092 Logit Scale: 27.044 Contrastive_loss: 0.13447 (0.13447) Loss: 0.13447 (0.13447) 2025-03-19,17:47:48 | INFO | Train Epoch: 16 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 150.122/s, 150.122/s/gpu LR: 0.000092 Logit Scale: 27.078 Contrastive_loss: 0.083544 (0.10901) Loss: 0.083544 (0.10901) 2025-03-19,17:48:10 | INFO | Train Epoch: 16 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.281/s, 149.281/s/gpu LR: 0.000092 Logit Scale: 27.110 Contrastive_loss: 0.061740 (0.093253) Loss: 0.061740 (0.093253) 2025-03-19,17:48:31 | INFO | Train Epoch: 16 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.213, 151.948/s, 151.948/s/gpu LR: 0.000091 Logit Scale: 27.088 Contrastive_loss: 0.021317 (0.075269) Loss: 0.021317 (0.075269) 2025-03-19,17:48:52 | INFO | Train Epoch: 16 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.212, 149.742/s, 149.742/s/gpu LR: 0.000091 Logit Scale: 27.130 Contrastive_loss: 0.16992 (0.094198) Loss: 0.16992 (0.094198) 2025-03-19,17:49:14 | INFO | Train Epoch: 16 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 147.780/s, 147.780/s/gpu LR: 0.000091 Logit Scale: 27.145 Contrastive_loss: 0.14930 (0.10338) Loss: 0.14930 (0.10338) 2025-03-19,17:49:36 | INFO | Train Epoch: 16 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.219, 146.683/s, 146.683/s/gpu LR: 0.000091 Logit Scale: 27.149 Contrastive_loss: 0.081049 (0.10019) Loss: 0.081049 (0.10019) 2025-03-19,17:49:57 | INFO | Train Epoch: 16 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 147.936/s, 147.936/s/gpu LR: 0.000091 Logit Scale: 27.163 Contrastive_loss: 0.036190 (0.092191) Loss: 0.036190 (0.092191) 2025-03-19,17:50:19 | INFO | Train Epoch: 16 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.219, 147.136/s, 147.136/s/gpu LR: 0.000091 Logit Scale: 27.170 Contrastive_loss: 0.040564 (0.086455) Loss: 0.040564 (0.086455) 2025-03-19,17:50:41 | INFO | Train Epoch: 16 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 149.157/s, 149.157/s/gpu LR: 0.000091 Logit Scale: 27.204 Contrastive_loss: 0.095258 (0.087335) Loss: 0.095258 (0.087335) 2025-03-19,17:51:03 | INFO | Train Epoch: 16 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 145.090/s, 145.090/s/gpu LR: 0.000091 Logit Scale: 27.183 Contrastive_loss: 0.047434 (0.083708) Loss: 0.047434 (0.083708) 2025-03-19,17:51:25 | INFO | Train Epoch: 16 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.220, 139.136/s, 139.136/s/gpu LR: 0.000091 Logit Scale: 27.177 Contrastive_loss: 0.029089 (0.079156) Loss: 0.029089 (0.079156) 2025-03-19,17:51:47 | INFO | Train Epoch: 16 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 147.531/s, 147.531/s/gpu LR: 0.000091 Logit Scale: 27.190 Contrastive_loss: 0.080349 (0.079248) Loss: 0.080349 (0.079248) 2025-03-19,17:52:08 | INFO | Train Epoch: 16 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.588/s, 148.588/s/gpu LR: 0.000091 Logit Scale: 27.204 Contrastive_loss: 0.027867 (0.075578) Loss: 0.027867 (0.075578) 2025-03-19,17:52:30 | INFO | Train Epoch: 16 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.217, 144.828/s, 144.828/s/gpu LR: 0.000091 Logit Scale: 27.213 Contrastive_loss: 0.19920 (0.083819) Loss: 0.19920 (0.083819) 2025-03-19,17:52:52 | INFO | Train Epoch: 16 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.219, 144.950/s, 144.950/s/gpu LR: 0.000091 Logit Scale: 27.240 Contrastive_loss: 0.024114 (0.080088) Loss: 0.024114 (0.080088) 2025-03-19,17:53:14 | INFO | Train Epoch: 16 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.219, 149.067/s, 149.067/s/gpu LR: 0.000091 Logit Scale: 27.241 Contrastive_loss: 0.20248 (0.087287) Loss: 0.20248 (0.087287) 2025-03-19,17:53:35 | INFO | Train Epoch: 16 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 145.659/s, 145.659/s/gpu LR: 0.000091 Logit Scale: 27.248 Contrastive_loss: 0.32711 (0.10061) Loss: 0.32711 (0.10061) 2025-03-19,17:53:57 | INFO | Train Epoch: 16 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.220, 144.266/s, 144.266/s/gpu LR: 0.000091 Logit Scale: 27.258 Contrastive_loss: 0.043433 (0.097601) Loss: 0.043433 (0.097601) 2025-03-19,17:54:19 | INFO | Train Epoch: 16 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.218, 149.086/s, 149.086/s/gpu LR: 0.000091 Logit Scale: 27.234 Contrastive_loss: 0.090430 (0.097242) Loss: 0.090430 (0.097242) 2025-03-19,17:54:41 | INFO | Train Epoch: 16 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.249/s, 149.249/s/gpu LR: 0.000091 Logit Scale: 27.213 Contrastive_loss: 0.21227 (0.10272) Loss: 0.21227 (0.10272) 2025-03-19,17:55:02 | INFO | Train Epoch: 16 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 149.410/s, 149.410/s/gpu LR: 0.000091 Logit Scale: 27.243 Contrastive_loss: 0.10810 (0.10296) Loss: 0.10810 (0.10296) 2025-03-19,17:55:24 | INFO | Train Epoch: 16 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.213, 151.639/s, 151.639/s/gpu LR: 0.000091 Logit Scale: 27.223 Contrastive_loss: 0.072705 (0.10165) Loss: 0.072705 (0.10165) 2025-03-19,17:55:45 | INFO | Train Epoch: 16 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.211, 151.900/s, 151.900/s/gpu LR: 0.000091 Logit Scale: 27.232 Contrastive_loss: 0.093711 (0.10132) Loss: 0.093711 (0.10132) 2025-03-19,17:56:06 | INFO | Train Epoch: 16 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 151.378/s, 151.378/s/gpu LR: 0.000091 Logit Scale: 27.193 Contrastive_loss: 0.20564 (0.10549) Loss: 0.20564 (0.10549) 2025-03-19,17:56:27 | INFO | Train Epoch: 16 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.211, 150.918/s, 150.918/s/gpu LR: 0.000091 Logit Scale: 27.205 Contrastive_loss: 0.13913 (0.10679) Loss: 0.13913 (0.10679) 2025-03-19,17:56:49 | INFO | Train Epoch: 16 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 148.899/s, 148.899/s/gpu LR: 0.000090 Logit Scale: 27.231 Contrastive_loss: 0.21342 (0.11073) Loss: 0.21342 (0.11073) 2025-03-19,17:57:10 | INFO | Train Epoch: 16 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 146.873/s, 146.873/s/gpu LR: 0.000090 Logit Scale: 27.214 Contrastive_loss: 0.078984 (0.10960) Loss: 0.078984 (0.10960) 2025-03-19,17:57:32 | INFO | Train Epoch: 16 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.853/s, 148.853/s/gpu LR: 0.000090 Logit Scale: 27.197 Contrastive_loss: 0.059731 (0.10788) Loss: 0.059731 (0.10788) 2025-03-19,17:57:53 | INFO | Train Epoch: 16 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.390/s, 148.390/s/gpu LR: 0.000090 Logit Scale: 27.194 Contrastive_loss: 0.13164 (0.10867) Loss: 0.13164 (0.10867) 2025-03-19,17:58:15 | INFO | Train Epoch: 16 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 143.698/s, 143.698/s/gpu LR: 0.000090 Logit Scale: 27.179 Contrastive_loss: 0.0067824 (0.10539) Loss: 0.0067824 (0.10539) 2025-03-19,17:58:37 | INFO | Train Epoch: 16 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.219, 144.128/s, 144.128/s/gpu LR: 0.000090 Logit Scale: 27.196 Contrastive_loss: 0.15279 (0.10687) Loss: 0.15279 (0.10687) 2025-03-19,17:58:59 | INFO | Train Epoch: 16 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 150.015/s, 150.015/s/gpu LR: 0.000090 Logit Scale: 27.205 Contrastive_loss: 0.046106 (0.10503) Loss: 0.046106 (0.10503) 2025-03-19,17:59:20 | INFO | Train Epoch: 16 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.213, 151.981/s, 151.981/s/gpu LR: 0.000090 Logit Scale: 27.207 Contrastive_loss: 0.21530 (0.10827) Loss: 0.21530 (0.10827) 2025-03-19,17:59:42 | INFO | Train Epoch: 16 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 145.639/s, 145.639/s/gpu LR: 0.000090 Logit Scale: 27.200 Contrastive_loss: 0.14365 (0.10928) Loss: 0.14365 (0.10928) 2025-03-19,18:00:04 | INFO | Train Epoch: 16 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.220, 145.591/s, 145.591/s/gpu LR: 0.000090 Logit Scale: 27.178 Contrastive_loss: 0.066952 (0.10810) Loss: 0.066952 (0.10810) 2025-03-19,18:00:25 | INFO | Train Epoch: 16 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 148.901/s, 148.901/s/gpu LR: 0.000090 Logit Scale: 27.196 Contrastive_loss: 0.077865 (0.10729) Loss: 0.077865 (0.10729) 2025-03-19,18:00:47 | INFO | Train Epoch: 16 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 148.857/s, 148.857/s/gpu LR: 0.000090 Logit Scale: 27.198 Contrastive_loss: 0.17045 (0.10895) Loss: 0.17045 (0.10895) 2025-03-19,18:01:09 | INFO | Train Epoch: 16 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 148.637/s, 148.637/s/gpu LR: 0.000090 Logit Scale: 27.208 Contrastive_loss: 0.10632 (0.10888) Loss: 0.10632 (0.10888) 2025-03-19,18:01:30 | INFO | Train Epoch: 16 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 146.773/s, 146.773/s/gpu LR: 0.000090 Logit Scale: 27.202 Contrastive_loss: 0.17926 (0.11064) Loss: 0.17926 (0.11064) 2025-03-19,18:01:52 | INFO | Train Epoch: 16 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 149.149/s, 149.149/s/gpu LR: 0.000090 Logit Scale: 27.182 Contrastive_loss: 0.052747 (0.10923) Loss: 0.052747 (0.10923) 2025-03-19,18:02:13 | INFO | Train Epoch: 16 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 147.487/s, 147.487/s/gpu LR: 0.000090 Logit Scale: 27.193 Contrastive_loss: 0.025327 (0.10723) Loss: 0.025327 (0.10723) 2025-03-19,18:02:35 | INFO | Train Epoch: 16 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 147.950/s, 147.950/s/gpu LR: 0.000090 Logit Scale: 27.223 Contrastive_loss: 0.075295 (0.10649) Loss: 0.075295 (0.10649) 2025-03-19,18:02:56 | INFO | Train Epoch: 16 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 150.978/s, 150.978/s/gpu LR: 0.000090 Logit Scale: 27.212 Contrastive_loss: 0.065343 (0.10555) Loss: 0.065343 (0.10555) 2025-03-19,18:03:18 | INFO | Train Epoch: 16 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 148.385/s, 148.385/s/gpu LR: 0.000090 Logit Scale: 27.214 Contrastive_loss: 0.097900 (0.10538) Loss: 0.097900 (0.10538) 2025-03-19,18:03:39 | INFO | Train Epoch: 16 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 150.165/s, 150.165/s/gpu LR: 0.000090 Logit Scale: 27.224 Contrastive_loss: 0.10218 (0.10531) Loss: 0.10218 (0.10531) 2025-03-19,18:04:01 | INFO | Train Epoch: 16 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 150.253/s, 150.253/s/gpu LR: 0.000090 Logit Scale: 27.210 Contrastive_loss: 0.16323 (0.10655) Loss: 0.16323 (0.10655) 2025-03-19,18:04:22 | INFO | Train Epoch: 16 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.213, 150.237/s, 150.237/s/gpu LR: 0.000090 Logit Scale: 27.218 Contrastive_loss: 0.048834 (0.10534) Loss: 0.048834 (0.10534) 2025-03-19,18:04:43 | INFO | Train Epoch: 16 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 148.694/s, 148.694/s/gpu LR: 0.000089 Logit Scale: 27.201 Contrastive_loss: 0.052254 (0.10426) Loss: 0.052254 (0.10426) 2025-03-19,18:05:05 | INFO | Train Epoch: 16 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 148.863/s, 148.863/s/gpu LR: 0.000089 Logit Scale: 27.201 Contrastive_loss: 0.24053 (0.10699) Loss: 0.24053 (0.10699) 2025-03-19,18:05:26 | INFO | Train Epoch: 16 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 150.195/s, 150.195/s/gpu LR: 0.000089 Logit Scale: 27.164 Contrastive_loss: 0.017312 (0.10523) Loss: 0.017312 (0.10523) 2025-03-19,18:05:48 | INFO | Train Epoch: 16 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 151.179/s, 151.179/s/gpu LR: 0.000089 Logit Scale: 27.173 Contrastive_loss: 0.20531 (0.10715) Loss: 0.20531 (0.10715) 2025-03-19,18:06:09 | INFO | Train Epoch: 16 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.211, 151.480/s, 151.480/s/gpu LR: 0.000089 Logit Scale: 27.142 Contrastive_loss: 0.0032232 (0.10519) Loss: 0.0032232 (0.10519) 2025-03-19,18:06:30 | INFO | Train Epoch: 16 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.213, 149.306/s, 149.306/s/gpu LR: 0.000089 Logit Scale: 27.130 Contrastive_loss: 0.055889 (0.10428) Loss: 0.055889 (0.10428) 2025-03-19,18:06:52 | INFO | Train Epoch: 16 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 149.569/s, 149.569/s/gpu LR: 0.000089 Logit Scale: 27.136 Contrastive_loss: 0.055573 (0.10339) Loss: 0.055573 (0.10339) 2025-03-19,18:07:13 | INFO | Train Epoch: 16 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.213, 148.187/s, 148.187/s/gpu LR: 0.000089 Logit Scale: 27.142 Contrastive_loss: 0.17017 (0.10459) Loss: 0.17017 (0.10459) 2025-03-19,18:07:35 | INFO | Train Epoch: 16 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 148.169/s, 148.169/s/gpu LR: 0.000089 Logit Scale: 27.151 Contrastive_loss: 0.090625 (0.10434) Loss: 0.090625 (0.10434) 2025-03-19,18:07:56 | INFO | Train Epoch: 16 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.998/s, 149.998/s/gpu LR: 0.000089 Logit Scale: 27.133 Contrastive_loss: 0.082959 (0.10397) Loss: 0.082959 (0.10397) 2025-03-19,18:08:18 | INFO | Train Epoch: 16 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.274/s, 149.274/s/gpu LR: 0.000089 Logit Scale: 27.118 Contrastive_loss: 0.038209 (0.10286) Loss: 0.038209 (0.10286) 2025-03-19,18:08:39 | INFO | Train Epoch: 16 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 150.626/s, 150.626/s/gpu LR: 0.000089 Logit Scale: 27.112 Contrastive_loss: 0.077563 (0.10244) Loss: 0.077563 (0.10244) 2025-03-19,18:09:01 | INFO | Train Epoch: 16 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 148.708/s, 148.708/s/gpu LR: 0.000089 Logit Scale: 27.102 Contrastive_loss: 0.19899 (0.10402) Loss: 0.19899 (0.10402) 2025-03-19,18:09:22 | INFO | Train Epoch: 16 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 149.648/s, 149.648/s/gpu LR: 0.000089 Logit Scale: 27.088 Contrastive_loss: 0.045610 (0.10308) Loss: 0.045610 (0.10308) 2025-03-19,18:09:43 | INFO | Train Epoch: 16 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 150.060/s, 150.060/s/gpu LR: 0.000089 Logit Scale: 27.057 Contrastive_loss: 0.084122 (0.10278) Loss: 0.084122 (0.10278) 2025-03-19,18:10:05 | INFO | Train Epoch: 16 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 149.434/s, 149.434/s/gpu LR: 0.000089 Logit Scale: 27.052 Contrastive_loss: 0.019841 (0.10148) Loss: 0.019841 (0.10148) 2025-03-19,18:10:26 | INFO | Train Epoch: 16 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 150.055/s, 150.055/s/gpu LR: 0.000089 Logit Scale: 27.035 Contrastive_loss: 0.074912 (0.10107) Loss: 0.074912 (0.10107) 2025-03-19,18:10:48 | INFO | Train Epoch: 16 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.213, 151.708/s, 151.708/s/gpu LR: 0.000089 Logit Scale: 27.052 Contrastive_loss: 0.17756 (0.10223) Loss: 0.17756 (0.10223) 2025-03-19,18:11:09 | INFO | Train Epoch: 16 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.211, 151.859/s, 151.859/s/gpu LR: 0.000089 Logit Scale: 27.048 Contrastive_loss: 0.10751 (0.10231) Loss: 0.10751 (0.10231) 2025-03-19,18:11:30 | INFO | Train Epoch: 16 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.478/s, 149.478/s/gpu LR: 0.000089 Logit Scale: 27.097 Contrastive_loss: 0.11942 (0.10256) Loss: 0.11942 (0.10256) 2025-03-19,18:11:52 | INFO | Train Epoch: 16 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 149.749/s, 149.749/s/gpu LR: 0.000089 Logit Scale: 27.116 Contrastive_loss: 0.042942 (0.10170) Loss: 0.042942 (0.10170) 2025-03-19,18:12:13 | INFO | Train Epoch: 16 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 150.504/s, 150.504/s/gpu LR: 0.000089 Logit Scale: 27.104 Contrastive_loss: 0.038476 (0.10079) Loss: 0.038476 (0.10079) 2025-03-19,18:12:35 | INFO | Train Epoch: 16 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 151.525/s, 151.525/s/gpu LR: 0.000089 Logit Scale: 27.078 Contrastive_loss: 0.10880 (0.10091) Loss: 0.10880 (0.10091) 2025-03-19,18:12:56 | INFO | Train Epoch: 16 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.211, 151.682/s, 151.682/s/gpu LR: 0.000088 Logit Scale: 27.102 Contrastive_loss: 0.20101 (0.10230) Loss: 0.20101 (0.10230) 2025-03-19,18:13:17 | INFO | Train Epoch: 16 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 148.418/s, 148.418/s/gpu LR: 0.000088 Logit Scale: 27.080 Contrastive_loss: 0.13168 (0.10270) Loss: 0.13168 (0.10270) 2025-03-19,18:13:39 | INFO | Train Epoch: 16 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.212, 151.662/s, 151.662/s/gpu LR: 0.000088 Logit Scale: 27.119 Contrastive_loss: 0.10904 (0.10278) Loss: 0.10904 (0.10278) 2025-03-19,18:14:00 | INFO | Train Epoch: 16 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 144.865/s, 144.865/s/gpu LR: 0.000088 Logit Scale: 27.119 Contrastive_loss: 0.022752 (0.10172) Loss: 0.022752 (0.10172) 2025-03-19,18:14:22 | INFO | Train Epoch: 16 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.221, 144.646/s, 144.646/s/gpu LR: 0.000088 Logit Scale: 27.090 Contrastive_loss: 0.017966 (0.10062) Loss: 0.017966 (0.10062) 2025-03-19,18:14:44 | INFO | Train Epoch: 16 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.219, 145.815/s, 145.815/s/gpu LR: 0.000088 Logit Scale: 27.084 Contrastive_loss: 0.052220 (0.099987) Loss: 0.052220 (0.099987) 2025-03-19,18:15:06 | INFO | Train Epoch: 16 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.221, 150.996/s, 150.996/s/gpu LR: 0.000088 Logit Scale: 27.093 Contrastive_loss: 0.11611 (0.10019) Loss: 0.11611 (0.10019) 2025-03-19,18:15:28 | INFO | Train Epoch: 16 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 149.700/s, 149.700/s/gpu LR: 0.000088 Logit Scale: 27.077 Contrastive_loss: 0.0090625 (0.099040) Loss: 0.0090625 (0.099040) 2025-03-19,18:15:50 | INFO | Train Epoch: 16 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.709/s, 149.709/s/gpu LR: 0.000088 Logit Scale: 27.093 Contrastive_loss: 0.069495 (0.098671) Loss: 0.069495 (0.098671) 2025-03-19,18:16:11 | INFO | Train Epoch: 16 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 149.894/s, 149.894/s/gpu LR: 0.000088 Logit Scale: 27.104 Contrastive_loss: 0.26761 (0.10076) Loss: 0.26761 (0.10076) 2025-03-19,18:16:33 | INFO | Train Epoch: 16 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 148.602/s, 148.602/s/gpu LR: 0.000088 Logit Scale: 27.138 Contrastive_loss: 0.029449 (0.099887) Loss: 0.029449 (0.099887) 2025-03-19,18:16:55 | INFO | Train Epoch: 16 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.217, 146.924/s, 146.924/s/gpu LR: 0.000088 Logit Scale: 27.102 Contrastive_loss: 0.47263 (0.10438) Loss: 0.47263 (0.10438) 2025-03-19,18:17:16 | INFO | Train Epoch: 16 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 149.222/s, 149.222/s/gpu LR: 0.000088 Logit Scale: 27.051 Contrastive_loss: 0.22160 (0.10577) Loss: 0.22160 (0.10577) 2025-03-19,18:17:38 | INFO | Train Epoch: 16 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.861/s, 148.861/s/gpu LR: 0.000088 Logit Scale: 27.061 Contrastive_loss: 0.24842 (0.10745) Loss: 0.24842 (0.10745) 2025-03-19,18:17:59 | INFO | Train Epoch: 16 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 148.896/s, 148.896/s/gpu LR: 0.000088 Logit Scale: 27.058 Contrastive_loss: 0.17561 (0.10824) Loss: 0.17561 (0.10824) 2025-03-19,18:18:20 | INFO | Train Epoch: 16 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.213, 151.519/s, 151.519/s/gpu LR: 0.000088 Logit Scale: 27.067 Contrastive_loss: 0.15101 (0.10874) Loss: 0.15101 (0.10874) 2025-03-19,18:18:42 | INFO | Train Epoch: 16 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 147.149/s, 147.149/s/gpu LR: 0.000088 Logit Scale: 27.078 Contrastive_loss: 0.14311 (0.10913) Loss: 0.14311 (0.10913) 2025-03-19,18:19:03 | INFO | Train Epoch: 16 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 148.276/s, 148.276/s/gpu LR: 0.000088 Logit Scale: 27.072 Contrastive_loss: 0.087914 (0.10889) Loss: 0.087914 (0.10889) 2025-03-19,18:19:25 | INFO | Train Epoch: 16 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.217, 148.050/s, 148.050/s/gpu LR: 0.000088 Logit Scale: 27.082 Contrastive_loss: 0.20077 (0.10991) Loss: 0.20077 (0.10991) 2025-03-19,18:19:47 | INFO | Train Epoch: 16 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.217, 148.958/s, 148.958/s/gpu LR: 0.000088 Logit Scale: 27.084 Contrastive_loss: 0.038679 (0.10913) Loss: 0.038679 (0.10913) 2025-03-19,18:20:08 | INFO | Train Epoch: 16 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.212, 152.188/s, 152.188/s/gpu LR: 0.000088 Logit Scale: 27.083 Contrastive_loss: 0.11873 (0.10923) Loss: 0.11873 (0.10923) 2025-03-19,18:20:30 | INFO | Train Epoch: 16 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.659/s, 148.659/s/gpu LR: 0.000088 Logit Scale: 27.106 Contrastive_loss: 0.29476 (0.11123) Loss: 0.29476 (0.11123) 2025-03-19,18:20:51 | INFO | Train Epoch: 16 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.375/s, 148.375/s/gpu LR: 0.000088 Logit Scale: 27.120 Contrastive_loss: 0.21143 (0.11229) Loss: 0.21143 (0.11229) 2025-03-19,18:21:13 | INFO | Train Epoch: 16 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.633/s, 149.633/s/gpu LR: 0.000087 Logit Scale: 27.106 Contrastive_loss: 0.039558 (0.11153) Loss: 0.039558 (0.11153) 2025-03-19,18:21:34 | INFO | Train Epoch: 16 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.213, 145.715/s, 145.715/s/gpu LR: 0.000087 Logit Scale: 27.101 Contrastive_loss: 0.062614 (0.11102) Loss: 0.062614 (0.11102) 2025-03-19,18:21:55 | INFO | Train Epoch: 16 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.121/s, 149.121/s/gpu LR: 0.000087 Logit Scale: 27.108 Contrastive_loss: 0.081374 (0.11071) Loss: 0.081374 (0.11071) 2025-03-19,18:22:17 | INFO | Train Epoch: 16 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.699/s, 149.699/s/gpu LR: 0.000087 Logit Scale: 27.135 Contrastive_loss: 0.094384 (0.11054) Loss: 0.094384 (0.11054) 2025-03-19,18:22:38 | INFO | Train Epoch: 16 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 148.957/s, 148.957/s/gpu LR: 0.000087 Logit Scale: 27.171 Contrastive_loss: 0.050239 (0.10993) Loss: 0.050239 (0.10993) 2025-03-19,18:23:00 | INFO | Train Epoch: 16 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 147.732/s, 147.732/s/gpu LR: 0.000087 Logit Scale: 27.179 Contrastive_loss: 0.40179 (0.11285) Loss: 0.40179 (0.11285) 2025-03-19,18:23:22 | INFO | Train Epoch: 16 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 147.895/s, 147.895/s/gpu LR: 0.000087 Logit Scale: 27.177 Contrastive_loss: 0.017903 (0.11191) Loss: 0.017903 (0.11191) 2025-03-19,18:23:43 | INFO | Train Epoch: 16 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.217, 148.526/s, 148.526/s/gpu LR: 0.000087 Logit Scale: 27.176 Contrastive_loss: 0.052365 (0.11133) Loss: 0.052365 (0.11133) 2025-03-19,18:24:05 | INFO | Train Epoch: 16 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 146.571/s, 146.571/s/gpu LR: 0.000087 Logit Scale: 27.188 Contrastive_loss: 0.083925 (0.11106) Loss: 0.083925 (0.11106) 2025-03-19,18:24:26 | INFO | Train Epoch: 16 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 147.443/s, 147.443/s/gpu LR: 0.000087 Logit Scale: 27.198 Contrastive_loss: 0.058237 (0.11056) Loss: 0.058237 (0.11056) 2025-03-19,18:24:48 | INFO | Train Epoch: 16 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.519/s, 149.519/s/gpu LR: 0.000087 Logit Scale: 27.206 Contrastive_loss: 0.13150 (0.11076) Loss: 0.13150 (0.11076) 2025-03-19,18:25:10 | INFO | Train Epoch: 16 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 145.452/s, 145.452/s/gpu LR: 0.000087 Logit Scale: 27.200 Contrastive_loss: 0.074468 (0.11041) Loss: 0.074468 (0.11041) 2025-03-19,18:25:31 | INFO | Train Epoch: 16 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 147.437/s, 147.437/s/gpu LR: 0.000087 Logit Scale: 27.237 Contrastive_loss: 0.22724 (0.11150) Loss: 0.22724 (0.11150) 2025-03-19,18:25:53 | INFO | Train Epoch: 16 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 150.081/s, 150.081/s/gpu LR: 0.000087 Logit Scale: 27.236 Contrastive_loss: 0.022854 (0.11068) Loss: 0.022854 (0.11068) 2025-03-19,18:26:14 | INFO | Train Epoch: 16 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 149.985/s, 149.985/s/gpu LR: 0.000087 Logit Scale: 27.235 Contrastive_loss: 0.017400 (0.10983) Loss: 0.017400 (0.10983) 2025-03-19,18:26:35 | INFO | Train Epoch: 16 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 148.580/s, 148.580/s/gpu LR: 0.000087 Logit Scale: 27.266 Contrastive_loss: 0.20221 (0.11067) Loss: 0.20221 (0.11067) 2025-03-19,18:26:57 | INFO | Train Epoch: 16 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 148.994/s, 148.994/s/gpu LR: 0.000087 Logit Scale: 27.264 Contrastive_loss: 0.27037 (0.11211) Loss: 0.27037 (0.11211) 2025-03-19,18:27:19 | INFO | Train Epoch: 16 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 146.271/s, 146.271/s/gpu LR: 0.000087 Logit Scale: 27.229 Contrastive_loss: 0.12744 (0.11224) Loss: 0.12744 (0.11224) 2025-03-19,18:27:40 | INFO | Train Epoch: 16 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 151.304/s, 151.304/s/gpu LR: 0.000087 Logit Scale: 27.237 Contrastive_loss: 0.16714 (0.11273) Loss: 0.16714 (0.11273) 2025-03-19,18:28:02 | INFO | Train Epoch: 16 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 147.288/s, 147.288/s/gpu LR: 0.000087 Logit Scale: 27.216 Contrastive_loss: 0.050469 (0.11218) Loss: 0.050469 (0.11218) 2025-03-19,18:28:24 | INFO | Train Epoch: 16 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 150.894/s, 150.894/s/gpu LR: 0.000087 Logit Scale: 27.210 Contrastive_loss: 0.034336 (0.11151) Loss: 0.034336 (0.11151) 2025-03-19,18:28:45 | INFO | Train Epoch: 16 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 147.754/s, 147.754/s/gpu LR: 0.000087 Logit Scale: 27.214 Contrastive_loss: 0.056584 (0.11103) Loss: 0.056584 (0.11103) 2025-03-19,18:29:07 | INFO | Train Epoch: 16 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 148.513/s, 148.513/s/gpu LR: 0.000086 Logit Scale: 27.210 Contrastive_loss: 0.00052185 (0.11009) Loss: 0.00052185 (0.11009) 2025-03-19,18:29:28 | INFO | Train Epoch: 16 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 150.169/s, 150.169/s/gpu LR: 0.000086 Logit Scale: 27.184 Contrastive_loss: 0.0075205 (0.10922) Loss: 0.0075205 (0.10922) 2025-03-19,18:29:50 | INFO | Train Epoch: 16 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.213, 151.024/s, 151.024/s/gpu LR: 0.000086 Logit Scale: 27.175 Contrastive_loss: 0.0068201 (0.10836) Loss: 0.0068201 (0.10836) 2025-03-19,18:30:11 | INFO | Train Epoch: 16 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 148.825/s, 148.825/s/gpu LR: 0.000086 Logit Scale: 27.186 Contrastive_loss: 0.067050 (0.10801) Loss: 0.067050 (0.10801) 2025-03-19,18:30:32 | INFO | Train Epoch: 16 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 150.876/s, 150.876/s/gpu LR: 0.000086 Logit Scale: 27.178 Contrastive_loss: 0.070766 (0.10771) Loss: 0.070766 (0.10771) 2025-03-19,18:30:54 | INFO | Train Epoch: 16 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.306/s, 149.306/s/gpu LR: 0.000086 Logit Scale: 27.152 Contrastive_loss: 0.031507 (0.10708) Loss: 0.031507 (0.10708) 2025-03-19,18:31:15 | INFO | Train Epoch: 16 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.533/s, 149.533/s/gpu LR: 0.000086 Logit Scale: 27.159 Contrastive_loss: 0.16820 (0.10758) Loss: 0.16820 (0.10758) 2025-03-19,18:31:37 | INFO | Train Epoch: 16 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.885/s, 149.885/s/gpu LR: 0.000086 Logit Scale: 27.189 Contrastive_loss: 0.41543 (0.11006) Loss: 0.41543 (0.11006) 2025-03-19,18:31:58 | INFO | Train Epoch: 16 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 150.854/s, 150.854/s/gpu LR: 0.000086 Logit Scale: 27.177 Contrastive_loss: 0.012703 (0.10928) Loss: 0.012703 (0.10928) 2025-03-19,18:32:20 | INFO | Train Epoch: 16 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.218, 147.080/s, 147.080/s/gpu LR: 0.000086 Logit Scale: 27.151 Contrastive_loss: 0.053437 (0.10884) Loss: 0.053437 (0.10884) 2025-03-19,18:32:42 | INFO | Train Epoch: 16 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 145.850/s, 145.850/s/gpu LR: 0.000086 Logit Scale: 27.171 Contrastive_loss: 0.057890 (0.10844) Loss: 0.057890 (0.10844) 2025-03-19,18:33:04 | INFO | Train Epoch: 16 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 146.710/s, 146.710/s/gpu LR: 0.000086 Logit Scale: 27.172 Contrastive_loss: 0.28559 (0.10982) Loss: 0.28559 (0.10982) 2025-03-19,18:33:25 | INFO | Train Epoch: 16 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 150.135/s, 150.135/s/gpu LR: 0.000086 Logit Scale: 27.137 Contrastive_loss: 0.064006 (0.10947) Loss: 0.064006 (0.10947) 2025-03-19,18:33:47 | INFO | Train Epoch: 16 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 147.717/s, 147.717/s/gpu LR: 0.000086 Logit Scale: 27.121 Contrastive_loss: 0.21641 (0.11029) Loss: 0.21641 (0.11029) 2025-03-19,18:34:08 | INFO | Train Epoch: 16 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 143.546/s, 143.546/s/gpu LR: 0.000086 Logit Scale: 27.107 Contrastive_loss: 0.041792 (0.10977) Loss: 0.041792 (0.10977) 2025-03-19,18:34:30 | INFO | Train Epoch: 16 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 146.855/s, 146.855/s/gpu LR: 0.000086 Logit Scale: 27.131 Contrastive_loss: 0.062450 (0.10941) Loss: 0.062450 (0.10941) 2025-03-19,18:34:52 | INFO | Train Epoch: 16 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 151.189/s, 151.189/s/gpu LR: 0.000086 Logit Scale: 27.150 Contrastive_loss: 0.47463 (0.11215) Loss: 0.47463 (0.11215) 2025-03-19,18:35:13 | INFO | Train Epoch: 16 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 150.042/s, 150.042/s/gpu LR: 0.000086 Logit Scale: 27.146 Contrastive_loss: 0.21792 (0.11294) Loss: 0.21792 (0.11294) 2025-03-19,18:35:34 | INFO | Train Epoch: 16 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 151.455/s, 151.455/s/gpu LR: 0.000086 Logit Scale: 27.180 Contrastive_loss: 0.29448 (0.11429) Loss: 0.29448 (0.11429) 2025-03-19,18:35:56 | INFO | Train Epoch: 16 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.212, 150.836/s, 150.836/s/gpu LR: 0.000086 Logit Scale: 27.154 Contrastive_loss: 0.011631 (0.11353) Loss: 0.011631 (0.11353) 2025-03-19,18:36:17 | INFO | Train Epoch: 16 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 149.162/s, 149.162/s/gpu LR: 0.000086 Logit Scale: 27.130 Contrastive_loss: 0.14333 (0.11375) Loss: 0.14333 (0.11375) 2025-03-19,18:36:38 | INFO | Train Epoch: 16 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 150.006/s, 150.006/s/gpu LR: 0.000086 Logit Scale: 27.121 Contrastive_loss: 0.11134 (0.11373) Loss: 0.11134 (0.11373) 2025-03-19,18:37:00 | INFO | Train Epoch: 16 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.084/s, 149.084/s/gpu LR: 0.000086 Logit Scale: 27.177 Contrastive_loss: 0.11877 (0.11377) Loss: 0.11877 (0.11377) 2025-03-19,18:37:21 | INFO | Train Epoch: 16 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.213, 150.897/s, 150.897/s/gpu LR: 0.000085 Logit Scale: 27.128 Contrastive_loss: 0.40627 (0.11586) Loss: 0.40627 (0.11586) 2025-03-19,18:37:42 | INFO | Train Epoch: 16 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.409/s, 149.409/s/gpu LR: 0.000085 Logit Scale: 27.109 Contrastive_loss: 0.028577 (0.11524) Loss: 0.028577 (0.11524) 2025-03-19,18:38:04 | INFO | Train Epoch: 16 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 150.141/s, 150.141/s/gpu LR: 0.000085 Logit Scale: 27.118 Contrastive_loss: 0.16026 (0.11556) Loss: 0.16026 (0.11556) 2025-03-19,18:38:25 | INFO | Train Epoch: 16 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.213, 151.439/s, 151.439/s/gpu LR: 0.000085 Logit Scale: 27.140 Contrastive_loss: 0.17792 (0.11599) Loss: 0.17792 (0.11599) 2025-03-19,18:38:47 | INFO | Train Epoch: 16 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.213, 148.530/s, 148.530/s/gpu LR: 0.000085 Logit Scale: 27.141 Contrastive_loss: 0.15489 (0.11626) Loss: 0.15489 (0.11626) 2025-03-19,18:39:08 | INFO | Train Epoch: 16 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 148.693/s, 148.693/s/gpu LR: 0.000085 Logit Scale: 27.167 Contrastive_loss: 0.039421 (0.11573) Loss: 0.039421 (0.11573) 2025-03-19,18:39:30 | INFO | Train Epoch: 16 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 150.928/s, 150.928/s/gpu LR: 0.000085 Logit Scale: 27.183 Contrastive_loss: 0.12165 (0.11577) Loss: 0.12165 (0.11577) 2025-03-19,18:39:51 | INFO | Train Epoch: 16 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.212, 143.572/s, 143.572/s/gpu LR: 0.000085 Logit Scale: 27.152 Contrastive_loss: 0.16299 (0.11610) Loss: 0.16299 (0.11610) 2025-03-19,18:40:12 | INFO | Train Epoch: 16 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 149.546/s, 149.546/s/gpu LR: 0.000085 Logit Scale: 27.156 Contrastive_loss: 0.10765 (0.11604) Loss: 0.10765 (0.11604) 2025-03-19,18:40:34 | INFO | Train Epoch: 16 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.213, 151.167/s, 151.167/s/gpu LR: 0.000085 Logit Scale: 27.180 Contrastive_loss: 0.14523 (0.11623) Loss: 0.14523 (0.11623) 2025-03-19,18:40:55 | INFO | Train Epoch: 16 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.213, 151.500/s, 151.500/s/gpu LR: 0.000085 Logit Scale: 27.185 Contrastive_loss: 0.032970 (0.11568) Loss: 0.032970 (0.11568) 2025-03-19,18:41:16 | INFO | Train Epoch: 16 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 150.731/s, 150.731/s/gpu LR: 0.000085 Logit Scale: 27.156 Contrastive_loss: 0.27355 (0.11672) Loss: 0.27355 (0.11672) 2025-03-19,18:41:38 | INFO | Train Epoch: 16 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 149.613/s, 149.613/s/gpu LR: 0.000085 Logit Scale: 27.167 Contrastive_loss: 0.083232 (0.11650) Loss: 0.083232 (0.11650) 2025-03-19,18:41:59 | INFO | Train Epoch: 16 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 152.494/s, 152.494/s/gpu LR: 0.000085 Logit Scale: 27.198 Contrastive_loss: 0.0041997 (0.11577) Loss: 0.0041997 (0.11577) 2025-03-19,18:42:20 | INFO | Train Epoch: 16 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 148.197/s, 148.197/s/gpu LR: 0.000085 Logit Scale: 27.156 Contrastive_loss: 0.13361 (0.11589) Loss: 0.13361 (0.11589) 2025-03-19,18:42:42 | INFO | Train Epoch: 16 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 151.838/s, 151.838/s/gpu LR: 0.000085 Logit Scale: 27.156 Contrastive_loss: 0.045855 (0.11543) Loss: 0.045855 (0.11543) 2025-03-19,18:43:03 | INFO | Train Epoch: 16 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 150.117/s, 150.117/s/gpu LR: 0.000085 Logit Scale: 27.128 Contrastive_loss: 0.067667 (0.11513) Loss: 0.067667 (0.11513) 2025-03-19,18:43:25 | INFO | Train Epoch: 16 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 149.272/s, 149.272/s/gpu LR: 0.000085 Logit Scale: 27.137 Contrastive_loss: 0.15602 (0.11539) Loss: 0.15602 (0.11539) 2025-03-19,18:43:46 | INFO | Train Epoch: 16 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 148.482/s, 148.482/s/gpu LR: 0.000085 Logit Scale: 27.151 Contrastive_loss: 0.074544 (0.11513) Loss: 0.074544 (0.11513) 2025-03-19,18:44:07 | INFO | Train Epoch: 16 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.213, 150.266/s, 150.266/s/gpu LR: 0.000085 Logit Scale: 27.184 Contrastive_loss: 0.062080 (0.11480) Loss: 0.062080 (0.11480) 2025-03-19,18:44:29 | INFO | Train Epoch: 16 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.361/s, 149.361/s/gpu LR: 0.000085 Logit Scale: 27.184 Contrastive_loss: 0.041440 (0.11434) Loss: 0.041440 (0.11434) 2025-03-19,18:44:50 | INFO | Train Epoch: 16 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 150.183/s, 150.183/s/gpu LR: 0.000085 Logit Scale: 27.184 Contrastive_loss: 0.12482 (0.11440) Loss: 0.12482 (0.11440) 2025-03-19,18:45:11 | INFO | Train Epoch: 16 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.213, 146.372/s, 146.372/s/gpu LR: 0.000085 Logit Scale: 27.188 Contrastive_loss: 0.16745 (0.11473) Loss: 0.16745 (0.11473) 2025-03-19,18:45:33 | INFO | Train Epoch: 16 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 148.069/s, 148.069/s/gpu LR: 0.000084 Logit Scale: 27.194 Contrastive_loss: 0.073672 (0.11448) Loss: 0.073672 (0.11448) 2025-03-19,18:45:54 | INFO | Train Epoch: 16 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 145.373/s, 145.373/s/gpu LR: 0.000084 Logit Scale: 27.182 Contrastive_loss: 0.14028 (0.11464) Loss: 0.14028 (0.11464) 2025-03-19,18:46:16 | INFO | Train Epoch: 16 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 145.150/s, 145.150/s/gpu LR: 0.000084 Logit Scale: 27.177 Contrastive_loss: 0.063665 (0.11433) Loss: 0.063665 (0.11433) 2025-03-19,18:46:38 | INFO | Train Epoch: 16 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 149.585/s, 149.585/s/gpu LR: 0.000084 Logit Scale: 27.175 Contrastive_loss: 0.15376 (0.11456) Loss: 0.15376 (0.11456) 2025-03-19,18:46:59 | INFO | Train Epoch: 16 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.213, 148.879/s, 148.879/s/gpu LR: 0.000084 Logit Scale: 27.169 Contrastive_loss: 0.12586 (0.11463) Loss: 0.12586 (0.11463) 2025-03-19,18:47:21 | INFO | Train Epoch: 16 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.214, 149.129/s, 149.129/s/gpu LR: 0.000084 Logit Scale: 27.140 Contrastive_loss: 0.11066 (0.11461) Loss: 0.11066 (0.11461) 2025-03-19,18:47:42 | INFO | Train Epoch: 16 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 147.404/s, 147.404/s/gpu LR: 0.000084 Logit Scale: 27.186 Contrastive_loss: 0.051811 (0.11424) Loss: 0.051811 (0.11424) 2025-03-19,18:48:03 | INFO | Train Epoch: 16 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.212, 150.517/s, 150.517/s/gpu LR: 0.000084 Logit Scale: 27.195 Contrastive_loss: 0.14720 (0.11443) Loss: 0.14720 (0.11443) 2025-03-19,18:48:25 | INFO | Train Epoch: 16 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 144.070/s, 144.070/s/gpu LR: 0.000084 Logit Scale: 27.166 Contrastive_loss: 0.097578 (0.11433) Loss: 0.097578 (0.11433) 2025-03-19,18:48:47 | INFO | Train Epoch: 16 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 147.459/s, 147.459/s/gpu LR: 0.000084 Logit Scale: 27.168 Contrastive_loss: 0.43550 (0.11620) Loss: 0.43550 (0.11620) 2025-03-19,18:49:08 | INFO | Train Epoch: 16 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 149.120/s, 149.120/s/gpu LR: 0.000084 Logit Scale: 27.150 Contrastive_loss: 0.070274 (0.11593) Loss: 0.070274 (0.11593) 2025-03-19,18:49:30 | INFO | Train Epoch: 16 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 149.853/s, 149.853/s/gpu LR: 0.000084 Logit Scale: 27.156 Contrastive_loss: 0.22534 (0.11656) Loss: 0.22534 (0.11656) 2025-03-19,18:49:51 | INFO | Train Epoch: 16 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 147.869/s, 147.869/s/gpu LR: 0.000084 Logit Scale: 27.161 Contrastive_loss: 0.13625 (0.11668) Loss: 0.13625 (0.11668) 2025-03-19,18:50:13 | INFO | Train Epoch: 16 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 142.840/s, 142.840/s/gpu LR: 0.000084 Logit Scale: 27.163 Contrastive_loss: 0.14834 (0.11686) Loss: 0.14834 (0.11686) 2025-03-19,18:50:35 | INFO | Train Epoch: 16 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 149.898/s, 149.898/s/gpu LR: 0.000084 Logit Scale: 27.164 Contrastive_loss: 0.12328 (0.11689) Loss: 0.12328 (0.11689) 2025-03-19,18:50:57 | INFO | Train Epoch: 16 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 145.983/s, 145.983/s/gpu LR: 0.000084 Logit Scale: 27.209 Contrastive_loss: 0.087626 (0.11673) Loss: 0.087626 (0.11673) 2025-03-19,18:51:19 | INFO | Train Epoch: 16 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.221, 147.590/s, 147.590/s/gpu LR: 0.000084 Logit Scale: 27.190 Contrastive_loss: 0.062307 (0.11642) Loss: 0.062307 (0.11642) 2025-03-19,18:51:40 | INFO | Train Epoch: 16 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.449/s, 149.449/s/gpu LR: 0.000084 Logit Scale: 27.169 Contrastive_loss: 0.12485 (0.11647) Loss: 0.12485 (0.11647) 2025-03-19,18:52:02 | INFO | Train Epoch: 16 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 144.750/s, 144.750/s/gpu LR: 0.000084 Logit Scale: 27.210 Contrastive_loss: 0.025681 (0.11597) Loss: 0.025681 (0.11597) 2025-03-19,18:52:24 | INFO | Train Epoch: 16 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.221, 145.450/s, 145.450/s/gpu LR: 0.000084 Logit Scale: 27.220 Contrastive_loss: 0.24586 (0.11668) Loss: 0.24586 (0.11668) 2025-03-19,18:52:46 | INFO | Train Epoch: 16 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 147.688/s, 147.688/s/gpu LR: 0.000084 Logit Scale: 27.206 Contrastive_loss: 0.20476 (0.11716) Loss: 0.20476 (0.11716) 2025-03-19,18:53:08 | INFO | Train Epoch: 16 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.220, 146.007/s, 146.007/s/gpu LR: 0.000084 Logit Scale: 27.213 Contrastive_loss: 0.092277 (0.11703) Loss: 0.092277 (0.11703) 2025-03-19,18:53:30 | INFO | Train Epoch: 16 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.221, 146.630/s, 146.630/s/gpu LR: 0.000084 Logit Scale: 27.212 Contrastive_loss: 0.32677 (0.11816) Loss: 0.32677 (0.11816) 2025-03-19,18:53:52 | INFO | Train Epoch: 16 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.220, 145.842/s, 145.842/s/gpu LR: 0.000083 Logit Scale: 27.211 Contrastive_loss: 0.20033 (0.11860) Loss: 0.20033 (0.11860) 2025-03-19,18:54:14 | INFO | Train Epoch: 16 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.218, 147.823/s, 147.823/s/gpu LR: 0.000083 Logit Scale: 27.205 Contrastive_loss: 0.20537 (0.11907) Loss: 0.20537 (0.11907) 2025-03-19,18:54:36 | INFO | Train Epoch: 16 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.220, 142.463/s, 142.463/s/gpu LR: 0.000083 Logit Scale: 27.252 Contrastive_loss: 0.098375 (0.11896) Loss: 0.098375 (0.11896) 2025-03-19,18:54:58 | INFO | Train Epoch: 16 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.222, 145.650/s, 145.650/s/gpu LR: 0.000083 Logit Scale: 27.254 Contrastive_loss: 0.13247 (0.11903) Loss: 0.13247 (0.11903) 2025-03-19,18:55:20 | INFO | Train Epoch: 16 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.220, 144.719/s, 144.719/s/gpu LR: 0.000083 Logit Scale: 27.243 Contrastive_loss: 0.085277 (0.11885) Loss: 0.085277 (0.11885) 2025-03-19,18:55:42 | INFO | Train Epoch: 16 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.220, 145.842/s, 145.842/s/gpu LR: 0.000083 Logit Scale: 27.253 Contrastive_loss: 0.41570 (0.12041) Loss: 0.41570 (0.12041) 2025-03-19,18:56:04 | INFO | Train Epoch: 16 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.220, 146.381/s, 146.381/s/gpu LR: 0.000083 Logit Scale: 27.244 Contrastive_loss: 0.11745 (0.12039) Loss: 0.11745 (0.12039) 2025-03-19,18:56:26 | INFO | Train Epoch: 16 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.219, 145.882/s, 145.882/s/gpu LR: 0.000083 Logit Scale: 27.217 Contrastive_loss: 0.011674 (0.11983) Loss: 0.011674 (0.11983) 2025-03-19,18:56:48 | INFO | Train Epoch: 16 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.220, 147.016/s, 147.016/s/gpu LR: 0.000083 Logit Scale: 27.213 Contrastive_loss: 0.21493 (0.12032) Loss: 0.21493 (0.12032) 2025-03-19,18:57:10 | INFO | Train Epoch: 16 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.222, 144.504/s, 144.504/s/gpu LR: 0.000083 Logit Scale: 27.198 Contrastive_loss: 0.098784 (0.12021) Loss: 0.098784 (0.12021) 2025-03-19,18:57:32 | INFO | Train Epoch: 16 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.221, 144.618/s, 144.618/s/gpu LR: 0.000083 Logit Scale: 27.217 Contrastive_loss: 0.054134 (0.11987) Loss: 0.054134 (0.11987) 2025-03-19,18:57:54 | INFO | Train Epoch: 16 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.220, 145.929/s, 145.929/s/gpu LR: 0.000083 Logit Scale: 27.206 Contrastive_loss: 0.056966 (0.11955) Loss: 0.056966 (0.11955) 2025-03-19,18:58:16 | INFO | Train Epoch: 16 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.222, 144.618/s, 144.618/s/gpu LR: 0.000083 Logit Scale: 27.204 Contrastive_loss: 0.072487 (0.11931) Loss: 0.072487 (0.11931) 2025-03-19,18:58:38 | INFO | Train Epoch: 16 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.220, 149.868/s, 149.868/s/gpu LR: 0.000083 Logit Scale: 27.244 Contrastive_loss: 0.017202 (0.11880) Loss: 0.017202 (0.11880) 2025-03-19,18:59:00 | INFO | Train Epoch: 16 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.217, 147.770/s, 147.770/s/gpu LR: 0.000083 Logit Scale: 27.223 Contrastive_loss: 0.039825 (0.11840) Loss: 0.039825 (0.11840) 2025-03-19,18:59:21 | INFO | Train Epoch: 16 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 149.611/s, 149.611/s/gpu LR: 0.000083 Logit Scale: 27.248 Contrastive_loss: 0.12845 (0.11845) Loss: 0.12845 (0.11845) 2025-03-19,18:59:43 | INFO | Train Epoch: 16 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 148.788/s, 148.788/s/gpu LR: 0.000083 Logit Scale: 27.255 Contrastive_loss: 0.012404 (0.11793) Loss: 0.012404 (0.11793) 2025-03-19,19:00:05 | INFO | Train Epoch: 16 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.219, 149.233/s, 149.233/s/gpu LR: 0.000083 Logit Scale: 27.217 Contrastive_loss: 0.16771 (0.11817) Loss: 0.16771 (0.11817) 2025-03-19,19:00:26 | INFO | Train Epoch: 16 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 149.684/s, 149.684/s/gpu LR: 0.000083 Logit Scale: 27.204 Contrastive_loss: 0.038889 (0.11779) Loss: 0.038889 (0.11779) 2025-03-19,19:00:48 | INFO | Train Epoch: 16 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 149.424/s, 149.424/s/gpu LR: 0.000083 Logit Scale: 27.180 Contrastive_loss: 0.15410 (0.11796) Loss: 0.15410 (0.11796) 2025-03-19,19:01:09 | INFO | Train Epoch: 16 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 148.008/s, 148.008/s/gpu LR: 0.000083 Logit Scale: 27.190 Contrastive_loss: 0.27257 (0.11871) Loss: 0.27257 (0.11871) 2025-03-19,19:01:31 | INFO | Train Epoch: 16 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 151.439/s, 151.439/s/gpu LR: 0.000083 Logit Scale: 27.178 Contrastive_loss: 0.19137 (0.11906) Loss: 0.19137 (0.11906) 2025-03-19,19:01:52 | INFO | Train Epoch: 16 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.212, 151.387/s, 151.387/s/gpu LR: 0.000083 Logit Scale: 27.188 Contrastive_loss: 0.11360 (0.11904) Loss: 0.11360 (0.11904) 2025-03-19,19:02:13 | INFO | Train Epoch: 16 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.213, 143.267/s, 143.267/s/gpu LR: 0.000082 Logit Scale: 27.173 Contrastive_loss: 0.046499 (0.11869) Loss: 0.046499 (0.11869) 2025-03-19,19:02:35 | INFO | Train Epoch: 16 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 146.645/s, 146.645/s/gpu LR: 0.000082 Logit Scale: 27.158 Contrastive_loss: 0.054590 (0.11839) Loss: 0.054590 (0.11839) 2025-03-19,19:02:56 | INFO | Train Epoch: 16 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.121/s, 149.121/s/gpu LR: 0.000082 Logit Scale: 27.152 Contrastive_loss: 0.14051 (0.11849) Loss: 0.14051 (0.11849) 2025-03-19,19:03:18 | INFO | Train Epoch: 16 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.221, 147.630/s, 147.630/s/gpu LR: 0.000082 Logit Scale: 27.146 Contrastive_loss: 0.13347 (0.11856) Loss: 0.13347 (0.11856) 2025-03-19,19:03:40 | INFO | Train Epoch: 16 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.433/s, 148.433/s/gpu LR: 0.000082 Logit Scale: 27.153 Contrastive_loss: 0.22976 (0.11908) Loss: 0.22976 (0.11908) 2025-03-19,19:04:01 | INFO | Train Epoch: 16 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 149.448/s, 149.448/s/gpu LR: 0.000082 Logit Scale: 27.163 Contrastive_loss: 0.21113 (0.11951) Loss: 0.21113 (0.11951) 2025-03-19,19:04:23 | INFO | Train Epoch: 16 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 147.367/s, 147.367/s/gpu LR: 0.000082 Logit Scale: 27.147 Contrastive_loss: 0.067899 (0.11927) Loss: 0.067899 (0.11927) 2025-03-19,19:04:44 | INFO | Train Epoch: 16 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.471/s, 150.471/s/gpu LR: 0.000082 Logit Scale: 27.139 Contrastive_loss: 0.10963 (0.11923) Loss: 0.10963 (0.11923) 2025-03-19,19:05:06 | INFO | Train Epoch: 16 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.069/s, 150.069/s/gpu LR: 0.000082 Logit Scale: 27.183 Contrastive_loss: 0.039757 (0.11886) Loss: 0.039757 (0.11886) 2025-03-19,19:05:27 | INFO | Train Epoch: 16 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.213, 148.560/s, 148.560/s/gpu LR: 0.000082 Logit Scale: 27.201 Contrastive_loss: 0.020823 (0.11841) Loss: 0.020823 (0.11841) 2025-03-19,19:05:49 | INFO | Train Epoch: 16 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.948/s, 148.948/s/gpu LR: 0.000082 Logit Scale: 27.191 Contrastive_loss: 0.15064 (0.11856) Loss: 0.15064 (0.11856) 2025-03-19,19:06:10 | INFO | Train Epoch: 16 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.213, 152.396/s, 152.396/s/gpu LR: 0.000082 Logit Scale: 27.155 Contrastive_loss: 0.064245 (0.11831) Loss: 0.064245 (0.11831) 2025-03-19,19:06:32 | INFO | Train Epoch: 16 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 148.786/s, 148.786/s/gpu LR: 0.000082 Logit Scale: 27.166 Contrastive_loss: 0.18489 (0.11861) Loss: 0.18489 (0.11861) 2025-03-19,19:06:53 | INFO | Train Epoch: 16 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 149.374/s, 149.374/s/gpu LR: 0.000082 Logit Scale: 27.172 Contrastive_loss: 0.011744 (0.11813) Loss: 0.011744 (0.11813) 2025-03-19,19:07:15 | INFO | Train Epoch: 16 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.811/s, 149.811/s/gpu LR: 0.000082 Logit Scale: 27.184 Contrastive_loss: 0.29905 (0.11894) Loss: 0.29905 (0.11894) 2025-03-19,19:07:36 | INFO | Train Epoch: 16 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.058/s, 150.058/s/gpu LR: 0.000082 Logit Scale: 27.178 Contrastive_loss: 0.027240 (0.11853) Loss: 0.027240 (0.11853) 2025-03-19,19:07:57 | INFO | Train Epoch: 16 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 146.482/s, 146.482/s/gpu LR: 0.000082 Logit Scale: 27.189 Contrastive_loss: 0.045020 (0.11821) Loss: 0.045020 (0.11821) 2025-03-19,19:08:19 | INFO | Train Epoch: 16 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.220, 148.781/s, 148.781/s/gpu LR: 0.000082 Logit Scale: 27.230 Contrastive_loss: 0.17800 (0.11847) Loss: 0.17800 (0.11847) 2025-03-19,19:08:41 | INFO | Train Epoch: 16 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.106/s, 149.106/s/gpu LR: 0.000082 Logit Scale: 27.224 Contrastive_loss: 0.079365 (0.11830) Loss: 0.079365 (0.11830) 2025-03-19,19:09:02 | INFO | Train Epoch: 16 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 145.576/s, 145.576/s/gpu LR: 0.000082 Logit Scale: 27.223 Contrastive_loss: 0.26689 (0.11895) Loss: 0.26689 (0.11895) 2025-03-19,19:09:24 | INFO | Train Epoch: 16 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 147.805/s, 147.805/s/gpu LR: 0.000082 Logit Scale: 27.227 Contrastive_loss: 0.015323 (0.11850) Loss: 0.015323 (0.11850) 2025-03-19,19:09:46 | INFO | Train Epoch: 16 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.218, 149.040/s, 149.040/s/gpu LR: 0.000082 Logit Scale: 27.223 Contrastive_loss: 0.19115 (0.11882) Loss: 0.19115 (0.11882) 2025-03-19,19:10:07 | INFO | Train Epoch: 16 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 149.977/s, 149.977/s/gpu LR: 0.000082 Logit Scale: 27.222 Contrastive_loss: 0.064136 (0.11858) Loss: 0.064136 (0.11858) 2025-03-19,19:10:29 | INFO | Train Epoch: 16 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.319/s, 149.319/s/gpu LR: 0.000081 Logit Scale: 27.231 Contrastive_loss: 0.071808 (0.11838) Loss: 0.071808 (0.11838) 2025-03-19,19:10:50 | INFO | Train Epoch: 16 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.042/s, 149.042/s/gpu LR: 0.000081 Logit Scale: 27.229 Contrastive_loss: 0.089016 (0.11825) Loss: 0.089016 (0.11825) 2025-03-19,19:11:12 | INFO | Train Epoch: 16 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 148.531/s, 148.531/s/gpu LR: 0.000081 Logit Scale: 27.266 Contrastive_loss: 0.16318 (0.11844) Loss: 0.16318 (0.11844) 2025-03-19,19:11:34 | INFO | Train Epoch: 16 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.836/s, 147.836/s/gpu LR: 0.000081 Logit Scale: 27.263 Contrastive_loss: 0.20142 (0.11880) Loss: 0.20142 (0.11880) 2025-03-19,19:11:55 | INFO | Train Epoch: 16 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.536/s, 147.536/s/gpu LR: 0.000081 Logit Scale: 27.248 Contrastive_loss: 0.078726 (0.11863) Loss: 0.078726 (0.11863) 2025-03-19,19:12:17 | INFO | Train Epoch: 16 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 148.462/s, 148.462/s/gpu LR: 0.000081 Logit Scale: 27.247 Contrastive_loss: 0.16494 (0.11882) Loss: 0.16494 (0.11882) 2025-03-19,19:12:39 | INFO | Train Epoch: 16 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 149.873/s, 149.873/s/gpu LR: 0.000081 Logit Scale: 27.220 Contrastive_loss: 0.18574 (0.11910) Loss: 0.18574 (0.11910) 2025-03-19,19:13:00 | INFO | Train Epoch: 16 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.524/s, 149.524/s/gpu LR: 0.000081 Logit Scale: 27.213 Contrastive_loss: 0.10278 (0.11903) Loss: 0.10278 (0.11903) 2025-03-19,19:13:22 | INFO | Train Epoch: 16 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.216, 148.580/s, 148.580/s/gpu LR: 0.000081 Logit Scale: 27.265 Contrastive_loss: 0.13996 (0.11912) Loss: 0.13996 (0.11912) 2025-03-19,19:13:29 | INFO | Train Epoch: 16 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.217, 150.089/s, 150.089/s/gpu LR: 0.000081 Logit Scale: 27.242 Contrastive_loss: 0.11185 (0.11909) Loss: 0.11185 (0.11909) 2025-03-19,19:13:30 | INFO | Eval Epoch: 17 [32 / 7443] Clip Loss: 3.368744 2025-03-19,19:13:35 | INFO | Eval Epoch: 17 [3232 / 7443] Clip Loss: 0.829561 2025-03-19,19:13:41 | INFO | Eval Epoch: 17 [6432 / 7443] Clip Loss: 0.625759 2025-03-19,19:13:44 | INFO | Eval Epoch: 17 image_to_text_mean_rank: 81.2355 image_to_text_median_rank: 6.0000 image_to_text_R@1: 0.1443 image_to_text_R@5: 0.4612 image_to_text_R@10: 0.6415 text_to_image_mean_rank: 52.4368 text_to_image_median_rank: 6.0000 text_to_image_R@1: 0.1494 text_to_image_R@5: 0.4669 text_to_image_R@10: 0.6394 clip_val_loss: 0.5853 epoch: 17.0000 num_samples: 7443.0000 2025-03-19,19:14:17 | INFO | Start epoch 17 2025-03-19,19:14:18 | INFO | Train Epoch: 17 [ 32/766009 (0%)] Data (t): 0.169 Batch (t): 0.367, 87.2404/s, 87.2404/s/gpu LR: 0.000081 Logit Scale: 27.242 Contrastive_loss: 0.13602 (0.13602) Loss: 0.13602 (0.13602) 2025-03-19,19:14:39 | INFO | Train Epoch: 17 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.212, 149.559/s, 149.559/s/gpu LR: 0.000081 Logit Scale: 27.265 Contrastive_loss: 0.20878 (0.17240) Loss: 0.20878 (0.17240) 2025-03-19,19:15:01 | INFO | Train Epoch: 17 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.220, 143.741/s, 143.741/s/gpu LR: 0.000081 Logit Scale: 27.302 Contrastive_loss: 0.0025159 (0.11577) Loss: 0.0025159 (0.11577) 2025-03-19,19:15:23 | INFO | Train Epoch: 17 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 147.840/s, 147.840/s/gpu LR: 0.000081 Logit Scale: 27.323 Contrastive_loss: 0.16990 (0.12930) Loss: 0.16990 (0.12930) 2025-03-19,19:15:44 | INFO | Train Epoch: 17 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 148.906/s, 148.906/s/gpu LR: 0.000081 Logit Scale: 27.354 Contrastive_loss: 0.020601 (0.10756) Loss: 0.020601 (0.10756) 2025-03-19,19:16:06 | INFO | Train Epoch: 17 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 151.702/s, 151.702/s/gpu LR: 0.000081 Logit Scale: 27.367 Contrastive_loss: 0.038366 (0.096030) Loss: 0.038366 (0.096030) 2025-03-19,19:16:27 | INFO | Train Epoch: 17 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 146.811/s, 146.811/s/gpu LR: 0.000081 Logit Scale: 27.388 Contrastive_loss: 0.14474 (0.10299) Loss: 0.14474 (0.10299) 2025-03-19,19:16:49 | INFO | Train Epoch: 17 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 149.197/s, 149.197/s/gpu LR: 0.000081 Logit Scale: 27.409 Contrastive_loss: 0.016980 (0.092238) Loss: 0.016980 (0.092238) 2025-03-19,19:17:10 | INFO | Train Epoch: 17 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 148.853/s, 148.853/s/gpu LR: 0.000081 Logit Scale: 27.414 Contrastive_loss: 0.029823 (0.085303) Loss: 0.029823 (0.085303) 2025-03-19,19:17:32 | INFO | Train Epoch: 17 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.797/s, 149.797/s/gpu LR: 0.000081 Logit Scale: 27.441 Contrastive_loss: 0.052311 (0.082004) Loss: 0.052311 (0.082004) 2025-03-19,19:17:53 | INFO | Train Epoch: 17 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.095/s, 149.095/s/gpu LR: 0.000081 Logit Scale: 27.453 Contrastive_loss: 0.027654 (0.077063) Loss: 0.027654 (0.077063) 2025-03-19,19:18:15 | INFO | Train Epoch: 17 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.190/s, 147.190/s/gpu LR: 0.000081 Logit Scale: 27.448 Contrastive_loss: 0.29420 (0.095158) Loss: 0.29420 (0.095158) 2025-03-19,19:18:36 | INFO | Train Epoch: 17 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 148.928/s, 148.928/s/gpu LR: 0.000081 Logit Scale: 27.456 Contrastive_loss: 0.087403 (0.094562) Loss: 0.087403 (0.094562) 2025-03-19,19:18:58 | INFO | Train Epoch: 17 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.143/s, 149.143/s/gpu LR: 0.000081 Logit Scale: 27.435 Contrastive_loss: 0.019935 (0.089231) Loss: 0.019935 (0.089231) 2025-03-19,19:19:20 | INFO | Train Epoch: 17 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.924/s, 148.924/s/gpu LR: 0.000080 Logit Scale: 27.470 Contrastive_loss: 0.16843 (0.094511) Loss: 0.16843 (0.094511) 2025-03-19,19:19:41 | INFO | Train Epoch: 17 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 150.070/s, 150.070/s/gpu LR: 0.000080 Logit Scale: 27.453 Contrastive_loss: 0.012761 (0.089401) Loss: 0.012761 (0.089401) 2025-03-19,19:20:03 | INFO | Train Epoch: 17 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.656/s, 149.656/s/gpu LR: 0.000080 Logit Scale: 27.475 Contrastive_loss: 0.12307 (0.091382) Loss: 0.12307 (0.091382) 2025-03-19,19:20:24 | INFO | Train Epoch: 17 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 148.630/s, 148.630/s/gpu LR: 0.000080 Logit Scale: 27.480 Contrastive_loss: 0.26530 (0.10104) Loss: 0.26530 (0.10104) 2025-03-19,19:20:46 | INFO | Train Epoch: 17 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.852/s, 148.852/s/gpu LR: 0.000080 Logit Scale: 27.476 Contrastive_loss: 0.070728 (0.099448) Loss: 0.070728 (0.099448) 2025-03-19,19:21:07 | INFO | Train Epoch: 17 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 149.222/s, 149.222/s/gpu LR: 0.000080 Logit Scale: 27.486 Contrastive_loss: 0.16958 (0.10295) Loss: 0.16958 (0.10295) 2025-03-19,19:21:28 | INFO | Train Epoch: 17 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.192/s, 148.192/s/gpu LR: 0.000080 Logit Scale: 27.493 Contrastive_loss: 0.14520 (0.10497) Loss: 0.14520 (0.10497) 2025-03-19,19:21:50 | INFO | Train Epoch: 17 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 147.871/s, 147.871/s/gpu LR: 0.000080 Logit Scale: 27.488 Contrastive_loss: 0.12260 (0.10577) Loss: 0.12260 (0.10577) 2025-03-19,19:22:12 | INFO | Train Epoch: 17 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.217, 148.981/s, 148.981/s/gpu LR: 0.000080 Logit Scale: 27.468 Contrastive_loss: 0.14291 (0.10738) Loss: 0.14291 (0.10738) 2025-03-19,19:22:33 | INFO | Train Epoch: 17 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 147.592/s, 147.592/s/gpu LR: 0.000080 Logit Scale: 27.414 Contrastive_loss: 0.013394 (0.10347) Loss: 0.013394 (0.10347) 2025-03-19,19:22:55 | INFO | Train Epoch: 17 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 148.058/s, 148.058/s/gpu LR: 0.000080 Logit Scale: 27.389 Contrastive_loss: 0.28591 (0.11076) Loss: 0.28591 (0.11076) 2025-03-19,19:23:17 | INFO | Train Epoch: 17 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 146.352/s, 146.352/s/gpu LR: 0.000080 Logit Scale: 27.377 Contrastive_loss: 0.10276 (0.11046) Loss: 0.10276 (0.11046) 2025-03-19,19:23:38 | INFO | Train Epoch: 17 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 148.764/s, 148.764/s/gpu LR: 0.000080 Logit Scale: 27.420 Contrastive_loss: 0.029794 (0.10747) Loss: 0.029794 (0.10747) 2025-03-19,19:24:00 | INFO | Train Epoch: 17 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 147.896/s, 147.896/s/gpu LR: 0.000080 Logit Scale: 27.421 Contrastive_loss: 0.081756 (0.10655) Loss: 0.081756 (0.10655) 2025-03-19,19:24:21 | INFO | Train Epoch: 17 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 149.853/s, 149.853/s/gpu LR: 0.000080 Logit Scale: 27.438 Contrastive_loss: 0.14398 (0.10784) Loss: 0.14398 (0.10784) 2025-03-19,19:24:43 | INFO | Train Epoch: 17 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.370/s, 148.370/s/gpu LR: 0.000080 Logit Scale: 27.441 Contrastive_loss: 0.092304 (0.10732) Loss: 0.092304 (0.10732) 2025-03-19,19:25:05 | INFO | Train Epoch: 17 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.584/s, 149.584/s/gpu LR: 0.000080 Logit Scale: 27.457 Contrastive_loss: 0.20587 (0.11050) Loss: 0.20587 (0.11050) 2025-03-19,19:25:26 | INFO | Train Epoch: 17 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.237/s, 149.237/s/gpu LR: 0.000080 Logit Scale: 27.463 Contrastive_loss: 0.095010 (0.11002) Loss: 0.095010 (0.11002) 2025-03-19,19:25:48 | INFO | Train Epoch: 17 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 149.566/s, 149.566/s/gpu LR: 0.000080 Logit Scale: 27.440 Contrastive_loss: 0.21236 (0.11312) Loss: 0.21236 (0.11312) 2025-03-19,19:26:09 | INFO | Train Epoch: 17 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 149.377/s, 149.377/s/gpu LR: 0.000080 Logit Scale: 27.449 Contrastive_loss: 0.051873 (0.11132) Loss: 0.051873 (0.11132) 2025-03-19,19:26:30 | INFO | Train Epoch: 17 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 149.998/s, 149.998/s/gpu LR: 0.000080 Logit Scale: 27.464 Contrastive_loss: 0.092819 (0.11079) Loss: 0.092819 (0.11079) 2025-03-19,19:26:52 | INFO | Train Epoch: 17 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.543/s, 148.543/s/gpu LR: 0.000080 Logit Scale: 27.459 Contrastive_loss: 0.050201 (0.10911) Loss: 0.050201 (0.10911) 2025-03-19,19:27:13 | INFO | Train Epoch: 17 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 150.137/s, 150.137/s/gpu LR: 0.000080 Logit Scale: 27.459 Contrastive_loss: 0.0037268 (0.10626) Loss: 0.0037268 (0.10626) 2025-03-19,19:27:35 | INFO | Train Epoch: 17 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 151.990/s, 151.990/s/gpu LR: 0.000079 Logit Scale: 27.459 Contrastive_loss: 0.10380 (0.10619) Loss: 0.10380 (0.10619) 2025-03-19,19:27:56 | INFO | Train Epoch: 17 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 150.837/s, 150.837/s/gpu LR: 0.000079 Logit Scale: 27.457 Contrastive_loss: 0.089424 (0.10576) Loss: 0.089424 (0.10576) 2025-03-19,19:28:18 | INFO | Train Epoch: 17 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 145.486/s, 145.486/s/gpu LR: 0.000079 Logit Scale: 27.445 Contrastive_loss: 0.25295 (0.10944) Loss: 0.25295 (0.10944) 2025-03-19,19:28:39 | INFO | Train Epoch: 17 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.218, 147.472/s, 147.472/s/gpu LR: 0.000079 Logit Scale: 27.448 Contrastive_loss: 0.048891 (0.10797) Loss: 0.048891 (0.10797) 2025-03-19,19:29:01 | INFO | Train Epoch: 17 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 150.526/s, 150.526/s/gpu LR: 0.000079 Logit Scale: 27.429 Contrastive_loss: 0.25044 (0.11136) Loss: 0.25044 (0.11136) 2025-03-19,19:29:23 | INFO | Train Epoch: 17 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 148.001/s, 148.001/s/gpu LR: 0.000079 Logit Scale: 27.420 Contrastive_loss: 0.080861 (0.11065) Loss: 0.080861 (0.11065) 2025-03-19,19:29:44 | INFO | Train Epoch: 17 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 150.722/s, 150.722/s/gpu LR: 0.000079 Logit Scale: 27.404 Contrastive_loss: 0.056535 (0.10942) Loss: 0.056535 (0.10942) 2025-03-19,19:30:05 | INFO | Train Epoch: 17 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 150.798/s, 150.798/s/gpu LR: 0.000079 Logit Scale: 27.427 Contrastive_loss: 0.039582 (0.10787) Loss: 0.039582 (0.10787) 2025-03-19,19:30:27 | INFO | Train Epoch: 17 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 152.437/s, 152.437/s/gpu LR: 0.000079 Logit Scale: 27.429 Contrastive_loss: 0.10923 (0.10790) Loss: 0.10923 (0.10790) 2025-03-19,19:30:48 | INFO | Train Epoch: 17 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 143.769/s, 143.769/s/gpu LR: 0.000079 Logit Scale: 27.427 Contrastive_loss: 0.038129 (0.10641) Loss: 0.038129 (0.10641) 2025-03-19,19:31:10 | INFO | Train Epoch: 17 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.117/s, 149.117/s/gpu LR: 0.000079 Logit Scale: 27.426 Contrastive_loss: 0.052223 (0.10528) Loss: 0.052223 (0.10528) 2025-03-19,19:31:31 | INFO | Train Epoch: 17 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 148.607/s, 148.607/s/gpu LR: 0.000079 Logit Scale: 27.419 Contrastive_loss: 0.18147 (0.10684) Loss: 0.18147 (0.10684) 2025-03-19,19:31:53 | INFO | Train Epoch: 17 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.442/s, 149.442/s/gpu LR: 0.000079 Logit Scale: 27.434 Contrastive_loss: 0.097925 (0.10666) Loss: 0.097925 (0.10666) 2025-03-19,19:32:14 | INFO | Train Epoch: 17 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.568/s, 149.568/s/gpu LR: 0.000079 Logit Scale: 27.441 Contrastive_loss: 0.095286 (0.10644) Loss: 0.095286 (0.10644) 2025-03-19,19:32:35 | INFO | Train Epoch: 17 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 148.073/s, 148.073/s/gpu LR: 0.000079 Logit Scale: 27.462 Contrastive_loss: 0.010021 (0.10458) Loss: 0.010021 (0.10458) 2025-03-19,19:32:57 | INFO | Train Epoch: 17 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 151.299/s, 151.299/s/gpu LR: 0.000079 Logit Scale: 27.436 Contrastive_loss: 0.039729 (0.10336) Loss: 0.039729 (0.10336) 2025-03-19,19:33:18 | INFO | Train Epoch: 17 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 148.875/s, 148.875/s/gpu LR: 0.000079 Logit Scale: 27.456 Contrastive_loss: 0.11113 (0.10350) Loss: 0.11113 (0.10350) 2025-03-19,19:33:40 | INFO | Train Epoch: 17 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 147.887/s, 147.887/s/gpu LR: 0.000079 Logit Scale: 27.433 Contrastive_loss: 0.095734 (0.10336) Loss: 0.095734 (0.10336) 2025-03-19,19:34:02 | INFO | Train Epoch: 17 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 145.890/s, 145.890/s/gpu LR: 0.000079 Logit Scale: 27.446 Contrastive_loss: 0.11440 (0.10356) Loss: 0.11440 (0.10356) 2025-03-19,19:34:23 | INFO | Train Epoch: 17 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 146.331/s, 146.331/s/gpu LR: 0.000079 Logit Scale: 27.426 Contrastive_loss: 0.19307 (0.10513) Loss: 0.19307 (0.10513) 2025-03-19,19:34:45 | INFO | Train Epoch: 17 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.219, 149.121/s, 149.121/s/gpu LR: 0.000079 Logit Scale: 27.432 Contrastive_loss: 0.032703 (0.10388) Loss: 0.032703 (0.10388) 2025-03-19,19:35:07 | INFO | Train Epoch: 17 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 147.774/s, 147.774/s/gpu LR: 0.000079 Logit Scale: 27.406 Contrastive_loss: 0.16099 (0.10485) Loss: 0.16099 (0.10485) 2025-03-19,19:35:29 | INFO | Train Epoch: 17 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 147.948/s, 147.948/s/gpu LR: 0.000079 Logit Scale: 27.409 Contrastive_loss: 0.044443 (0.10384) Loss: 0.044443 (0.10384) 2025-03-19,19:35:50 | INFO | Train Epoch: 17 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 145.469/s, 145.469/s/gpu LR: 0.000078 Logit Scale: 27.416 Contrastive_loss: 0.15692 (0.10471) Loss: 0.15692 (0.10471) 2025-03-19,19:36:12 | INFO | Train Epoch: 17 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 147.680/s, 147.680/s/gpu LR: 0.000078 Logit Scale: 27.427 Contrastive_loss: 0.14732 (0.10540) Loss: 0.14732 (0.10540) 2025-03-19,19:36:34 | INFO | Train Epoch: 17 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 148.357/s, 148.357/s/gpu LR: 0.000078 Logit Scale: 27.441 Contrastive_loss: 0.10504 (0.10539) Loss: 0.10504 (0.10539) 2025-03-19,19:36:55 | INFO | Train Epoch: 17 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.217, 148.015/s, 148.015/s/gpu LR: 0.000078 Logit Scale: 27.443 Contrastive_loss: 0.27612 (0.10806) Loss: 0.27612 (0.10806) 2025-03-19,19:37:17 | INFO | Train Epoch: 17 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 145.374/s, 145.374/s/gpu LR: 0.000078 Logit Scale: 27.454 Contrastive_loss: 0.051207 (0.10719) Loss: 0.051207 (0.10719) 2025-03-19,19:37:39 | INFO | Train Epoch: 17 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 149.378/s, 149.378/s/gpu LR: 0.000078 Logit Scale: 27.459 Contrastive_loss: 0.30966 (0.11025) Loss: 0.30966 (0.11025) 2025-03-19,19:38:00 | INFO | Train Epoch: 17 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 149.440/s, 149.440/s/gpu LR: 0.000078 Logit Scale: 27.433 Contrastive_loss: 0.0074204 (0.10872) Loss: 0.0074204 (0.10872) 2025-03-19,19:38:22 | INFO | Train Epoch: 17 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.621/s, 149.621/s/gpu LR: 0.000078 Logit Scale: 27.430 Contrastive_loss: 0.089916 (0.10844) Loss: 0.089916 (0.10844) 2025-03-19,19:38:43 | INFO | Train Epoch: 17 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.311/s, 149.311/s/gpu LR: 0.000078 Logit Scale: 27.435 Contrastive_loss: 0.00098793 (0.10689) Loss: 0.00098793 (0.10689) 2025-03-19,19:39:04 | INFO | Train Epoch: 17 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.570/s, 149.570/s/gpu LR: 0.000078 Logit Scale: 27.441 Contrastive_loss: 0.20595 (0.10830) Loss: 0.20595 (0.10830) 2025-03-19,19:39:26 | INFO | Train Epoch: 17 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 149.259/s, 149.259/s/gpu LR: 0.000078 Logit Scale: 27.415 Contrastive_loss: 0.046355 (0.10743) Loss: 0.046355 (0.10743) 2025-03-19,19:39:47 | INFO | Train Epoch: 17 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.366/s, 149.366/s/gpu LR: 0.000078 Logit Scale: 27.403 Contrastive_loss: 0.072284 (0.10694) Loss: 0.072284 (0.10694) 2025-03-19,19:40:09 | INFO | Train Epoch: 17 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 148.133/s, 148.133/s/gpu LR: 0.000078 Logit Scale: 27.434 Contrastive_loss: 0.039146 (0.10601) Loss: 0.039146 (0.10601) 2025-03-19,19:40:30 | INFO | Train Epoch: 17 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.213, 149.528/s, 149.528/s/gpu LR: 0.000078 Logit Scale: 27.405 Contrastive_loss: 0.076394 (0.10561) Loss: 0.076394 (0.10561) 2025-03-19,19:40:52 | INFO | Train Epoch: 17 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 148.227/s, 148.227/s/gpu LR: 0.000078 Logit Scale: 27.412 Contrastive_loss: 0.22043 (0.10714) Loss: 0.22043 (0.10714) 2025-03-19,19:41:13 | INFO | Train Epoch: 17 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 148.119/s, 148.119/s/gpu LR: 0.000078 Logit Scale: 27.394 Contrastive_loss: 0.32933 (0.11007) Loss: 0.32933 (0.11007) 2025-03-19,19:41:35 | INFO | Train Epoch: 17 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 147.787/s, 147.787/s/gpu LR: 0.000078 Logit Scale: 27.395 Contrastive_loss: 0.17303 (0.11088) Loss: 0.17303 (0.11088) 2025-03-19,19:41:56 | INFO | Train Epoch: 17 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 148.574/s, 148.574/s/gpu LR: 0.000078 Logit Scale: 27.388 Contrastive_loss: 0.12954 (0.11112) Loss: 0.12954 (0.11112) 2025-03-19,19:42:18 | INFO | Train Epoch: 17 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 148.684/s, 148.684/s/gpu LR: 0.000078 Logit Scale: 27.412 Contrastive_loss: 0.12918 (0.11135) Loss: 0.12918 (0.11135) 2025-03-19,19:42:40 | INFO | Train Epoch: 17 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 146.517/s, 146.517/s/gpu LR: 0.000078 Logit Scale: 27.433 Contrastive_loss: 0.065803 (0.11078) Loss: 0.065803 (0.11078) 2025-03-19,19:43:01 | INFO | Train Epoch: 17 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 147.685/s, 147.685/s/gpu LR: 0.000078 Logit Scale: 27.416 Contrastive_loss: 0.078026 (0.11038) Loss: 0.078026 (0.11038) 2025-03-19,19:43:23 | INFO | Train Epoch: 17 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.217, 148.031/s, 148.031/s/gpu LR: 0.000078 Logit Scale: 27.435 Contrastive_loss: 0.073951 (0.10993) Loss: 0.073951 (0.10993) 2025-03-19,19:43:44 | INFO | Train Epoch: 17 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 151.565/s, 151.565/s/gpu LR: 0.000078 Logit Scale: 27.444 Contrastive_loss: 0.010091 (0.10873) Loss: 0.010091 (0.10873) 2025-03-19,19:44:06 | INFO | Train Epoch: 17 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.219, 146.400/s, 146.400/s/gpu LR: 0.000077 Logit Scale: 27.435 Contrastive_loss: 0.090896 (0.10852) Loss: 0.090896 (0.10852) 2025-03-19,19:44:28 | INFO | Train Epoch: 17 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 151.058/s, 151.058/s/gpu LR: 0.000077 Logit Scale: 27.427 Contrastive_loss: 0.069186 (0.10806) Loss: 0.069186 (0.10806) 2025-03-19,19:44:49 | INFO | Train Epoch: 17 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 142.859/s, 142.859/s/gpu LR: 0.000077 Logit Scale: 27.406 Contrastive_loss: 0.060041 (0.10750) Loss: 0.060041 (0.10750) 2025-03-19,19:45:12 | INFO | Train Epoch: 17 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.224, 144.562/s, 144.562/s/gpu LR: 0.000077 Logit Scale: 27.408 Contrastive_loss: 0.037322 (0.10669) Loss: 0.037322 (0.10669) 2025-03-19,19:45:34 | INFO | Train Epoch: 17 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.222, 144.274/s, 144.274/s/gpu LR: 0.000077 Logit Scale: 27.419 Contrastive_loss: 0.018656 (0.10569) Loss: 0.018656 (0.10569) 2025-03-19,19:45:56 | INFO | Train Epoch: 17 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.219, 148.553/s, 148.553/s/gpu LR: 0.000077 Logit Scale: 27.412 Contrastive_loss: 0.17461 (0.10646) Loss: 0.17461 (0.10646) 2025-03-19,19:46:18 | INFO | Train Epoch: 17 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.219, 145.446/s, 145.446/s/gpu LR: 0.000077 Logit Scale: 27.454 Contrastive_loss: 0.33799 (0.10904) Loss: 0.33799 (0.10904) 2025-03-19,19:46:39 | INFO | Train Epoch: 17 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 149.674/s, 149.674/s/gpu LR: 0.000077 Logit Scale: 27.437 Contrastive_loss: 0.18650 (0.10989) Loss: 0.18650 (0.10989) 2025-03-19,19:47:01 | INFO | Train Epoch: 17 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.666/s, 148.666/s/gpu LR: 0.000077 Logit Scale: 27.446 Contrastive_loss: 0.042864 (0.10916) Loss: 0.042864 (0.10916) 2025-03-19,19:47:22 | INFO | Train Epoch: 17 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 149.404/s, 149.404/s/gpu LR: 0.000077 Logit Scale: 27.406 Contrastive_loss: 0.12906 (0.10937) Loss: 0.12906 (0.10937) 2025-03-19,19:47:44 | INFO | Train Epoch: 17 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 147.770/s, 147.770/s/gpu LR: 0.000077 Logit Scale: 27.409 Contrastive_loss: 0.033566 (0.10857) Loss: 0.033566 (0.10857) 2025-03-19,19:48:05 | INFO | Train Epoch: 17 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 149.440/s, 149.440/s/gpu LR: 0.000077 Logit Scale: 27.407 Contrastive_loss: 0.15785 (0.10909) Loss: 0.15785 (0.10909) 2025-03-19,19:48:27 | INFO | Train Epoch: 17 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.444/s, 149.444/s/gpu LR: 0.000077 Logit Scale: 27.391 Contrastive_loss: 0.053623 (0.10851) Loss: 0.053623 (0.10851) 2025-03-19,19:48:49 | INFO | Train Epoch: 17 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.217, 146.785/s, 146.785/s/gpu LR: 0.000077 Logit Scale: 27.406 Contrastive_loss: 0.15400 (0.10898) Loss: 0.15400 (0.10898) 2025-03-19,19:49:10 | INFO | Train Epoch: 17 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.220, 145.667/s, 145.667/s/gpu LR: 0.000077 Logit Scale: 27.390 Contrastive_loss: 0.042051 (0.10829) Loss: 0.042051 (0.10829) 2025-03-19,19:49:32 | INFO | Train Epoch: 17 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.218, 146.540/s, 146.540/s/gpu LR: 0.000077 Logit Scale: 27.400 Contrastive_loss: 0.13359 (0.10855) Loss: 0.13359 (0.10855) 2025-03-19,19:49:54 | INFO | Train Epoch: 17 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.218, 147.266/s, 147.266/s/gpu LR: 0.000077 Logit Scale: 27.395 Contrastive_loss: 0.059324 (0.10806) Loss: 0.059324 (0.10806) 2025-03-19,19:50:16 | INFO | Train Epoch: 17 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.218, 146.499/s, 146.499/s/gpu LR: 0.000077 Logit Scale: 27.395 Contrastive_loss: 0.0062664 (0.10705) Loss: 0.0062664 (0.10705) 2025-03-19,19:50:38 | INFO | Train Epoch: 17 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.220, 145.794/s, 145.794/s/gpu LR: 0.000077 Logit Scale: 27.419 Contrastive_loss: 0.044487 (0.10644) Loss: 0.044487 (0.10644) 2025-03-19,19:51:00 | INFO | Train Epoch: 17 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.220, 145.136/s, 145.136/s/gpu LR: 0.000077 Logit Scale: 27.423 Contrastive_loss: 0.16013 (0.10696) Loss: 0.16013 (0.10696) 2025-03-19,19:51:22 | INFO | Train Epoch: 17 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.219, 147.937/s, 147.937/s/gpu LR: 0.000077 Logit Scale: 27.437 Contrastive_loss: 0.11641 (0.10705) Loss: 0.11641 (0.10705) 2025-03-19,19:51:43 | INFO | Train Epoch: 17 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 146.230/s, 146.230/s/gpu LR: 0.000077 Logit Scale: 27.447 Contrastive_loss: 0.093798 (0.10692) Loss: 0.093798 (0.10692) 2025-03-19,19:52:05 | INFO | Train Epoch: 17 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.218, 146.313/s, 146.313/s/gpu LR: 0.000077 Logit Scale: 27.441 Contrastive_loss: 0.20566 (0.10785) Loss: 0.20566 (0.10785) 2025-03-19,19:52:27 | INFO | Train Epoch: 17 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.219, 141.745/s, 141.745/s/gpu LR: 0.000076 Logit Scale: 27.459 Contrastive_loss: 0.072034 (0.10752) Loss: 0.072034 (0.10752) 2025-03-19,19:52:49 | INFO | Train Epoch: 17 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.218, 149.705/s, 149.705/s/gpu LR: 0.000076 Logit Scale: 27.459 Contrastive_loss: 0.023700 (0.10674) Loss: 0.023700 (0.10674) 2025-03-19,19:53:10 | INFO | Train Epoch: 17 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 149.940/s, 149.940/s/gpu LR: 0.000076 Logit Scale: 27.459 Contrastive_loss: 0.16642 (0.10729) Loss: 0.16642 (0.10729) 2025-03-19,19:53:32 | INFO | Train Epoch: 17 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.218, 148.849/s, 148.849/s/gpu LR: 0.000076 Logit Scale: 27.435 Contrastive_loss: 0.10011 (0.10723) Loss: 0.10011 (0.10723) 2025-03-19,19:53:54 | INFO | Train Epoch: 17 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 148.631/s, 148.631/s/gpu LR: 0.000076 Logit Scale: 27.463 Contrastive_loss: 0.081928 (0.10700) Loss: 0.081928 (0.10700) 2025-03-19,19:54:15 | INFO | Train Epoch: 17 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.448/s, 149.448/s/gpu LR: 0.000076 Logit Scale: 27.467 Contrastive_loss: 0.020913 (0.10623) Loss: 0.020913 (0.10623) 2025-03-19,19:54:37 | INFO | Train Epoch: 17 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 148.415/s, 148.415/s/gpu LR: 0.000076 Logit Scale: 27.477 Contrastive_loss: 0.13402 (0.10647) Loss: 0.13402 (0.10647) 2025-03-19,19:54:59 | INFO | Train Epoch: 17 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 144.758/s, 144.758/s/gpu LR: 0.000076 Logit Scale: 27.475 Contrastive_loss: 0.13266 (0.10670) Loss: 0.13266 (0.10670) 2025-03-19,19:55:20 | INFO | Train Epoch: 17 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 149.879/s, 149.879/s/gpu LR: 0.000076 Logit Scale: 27.495 Contrastive_loss: 0.23560 (0.10783) Loss: 0.23560 (0.10783) 2025-03-19,19:55:42 | INFO | Train Epoch: 17 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 144.138/s, 144.138/s/gpu LR: 0.000076 Logit Scale: 27.493 Contrastive_loss: 0.099644 (0.10775) Loss: 0.099644 (0.10775) 2025-03-19,19:56:04 | INFO | Train Epoch: 17 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.221, 145.855/s, 145.855/s/gpu LR: 0.000076 Logit Scale: 27.460 Contrastive_loss: 0.013025 (0.10695) Loss: 0.013025 (0.10695) 2025-03-19,19:56:26 | INFO | Train Epoch: 17 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.219, 149.913/s, 149.913/s/gpu LR: 0.000076 Logit Scale: 27.445 Contrastive_loss: 0.0069520 (0.10610) Loss: 0.0069520 (0.10610) 2025-03-19,19:56:48 | INFO | Train Epoch: 17 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 145.939/s, 145.939/s/gpu LR: 0.000076 Logit Scale: 27.459 Contrastive_loss: 0.099457 (0.10604) Loss: 0.099457 (0.10604) 2025-03-19,19:57:10 | INFO | Train Epoch: 17 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.220, 146.899/s, 146.899/s/gpu LR: 0.000076 Logit Scale: 27.464 Contrastive_loss: 0.42738 (0.10872) Loss: 0.42738 (0.10872) 2025-03-19,19:57:31 | INFO | Train Epoch: 17 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 148.403/s, 148.403/s/gpu LR: 0.000076 Logit Scale: 27.444 Contrastive_loss: 0.091702 (0.10858) Loss: 0.091702 (0.10858) 2025-03-19,19:57:53 | INFO | Train Epoch: 17 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.815/s, 149.815/s/gpu LR: 0.000076 Logit Scale: 27.447 Contrastive_loss: 0.10029 (0.10851) Loss: 0.10029 (0.10851) 2025-03-19,19:58:14 | INFO | Train Epoch: 17 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.576/s, 149.576/s/gpu LR: 0.000076 Logit Scale: 27.450 Contrastive_loss: 0.064600 (0.10815) Loss: 0.064600 (0.10815) 2025-03-19,19:58:36 | INFO | Train Epoch: 17 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 148.365/s, 148.365/s/gpu LR: 0.000076 Logit Scale: 27.469 Contrastive_loss: 0.0084173 (0.10735) Loss: 0.0084173 (0.10735) 2025-03-19,19:58:57 | INFO | Train Epoch: 17 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 148.883/s, 148.883/s/gpu LR: 0.000076 Logit Scale: 27.432 Contrastive_loss: 0.18861 (0.10800) Loss: 0.18861 (0.10800) 2025-03-19,19:59:19 | INFO | Train Epoch: 17 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 148.367/s, 148.367/s/gpu LR: 0.000076 Logit Scale: 27.439 Contrastive_loss: 0.15977 (0.10841) Loss: 0.15977 (0.10841) 2025-03-19,19:59:40 | INFO | Train Epoch: 17 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 149.564/s, 149.564/s/gpu LR: 0.000076 Logit Scale: 27.447 Contrastive_loss: 0.22790 (0.10935) Loss: 0.22790 (0.10935) 2025-03-19,20:00:02 | INFO | Train Epoch: 17 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 149.945/s, 149.945/s/gpu LR: 0.000076 Logit Scale: 27.456 Contrastive_loss: 0.35096 (0.11124) Loss: 0.35096 (0.11124) 2025-03-19,20:00:23 | INFO | Train Epoch: 17 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 149.590/s, 149.590/s/gpu LR: 0.000076 Logit Scale: 27.437 Contrastive_loss: 0.37247 (0.11326) Loss: 0.37247 (0.11326) 2025-03-19,20:00:45 | INFO | Train Epoch: 17 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 149.150/s, 149.150/s/gpu LR: 0.000076 Logit Scale: 27.442 Contrastive_loss: 0.32877 (0.11492) Loss: 0.32877 (0.11492) 2025-03-19,20:01:06 | INFO | Train Epoch: 17 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 148.970/s, 148.970/s/gpu LR: 0.000075 Logit Scale: 27.440 Contrastive_loss: 0.18221 (0.11544) Loss: 0.18221 (0.11544) 2025-03-19,20:01:28 | INFO | Train Epoch: 17 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 148.663/s, 148.663/s/gpu LR: 0.000075 Logit Scale: 27.441 Contrastive_loss: 0.096099 (0.11529) Loss: 0.096099 (0.11529) 2025-03-19,20:01:49 | INFO | Train Epoch: 17 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 147.339/s, 147.339/s/gpu LR: 0.000075 Logit Scale: 27.426 Contrastive_loss: 0.088119 (0.11508) Loss: 0.088119 (0.11508) 2025-03-19,20:02:11 | INFO | Train Epoch: 17 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 142.025/s, 142.025/s/gpu LR: 0.000075 Logit Scale: 27.460 Contrastive_loss: 0.17391 (0.11552) Loss: 0.17391 (0.11552) 2025-03-19,20:02:33 | INFO | Train Epoch: 17 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.219, 146.794/s, 146.794/s/gpu LR: 0.000075 Logit Scale: 27.460 Contrastive_loss: 0.27009 (0.11667) Loss: 0.27009 (0.11667) 2025-03-19,20:02:55 | INFO | Train Epoch: 17 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.219, 147.350/s, 147.350/s/gpu LR: 0.000075 Logit Scale: 27.460 Contrastive_loss: 0.084097 (0.11643) Loss: 0.084097 (0.11643) 2025-03-19,20:03:17 | INFO | Train Epoch: 17 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.218, 148.709/s, 148.709/s/gpu LR: 0.000075 Logit Scale: 27.450 Contrastive_loss: 0.16808 (0.11681) Loss: 0.16808 (0.11681) 2025-03-19,20:03:38 | INFO | Train Epoch: 17 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 149.713/s, 149.713/s/gpu LR: 0.000075 Logit Scale: 27.460 Contrastive_loss: 0.019997 (0.11610) Loss: 0.019997 (0.11610) 2025-03-19,20:04:00 | INFO | Train Epoch: 17 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 150.451/s, 150.451/s/gpu LR: 0.000075 Logit Scale: 27.458 Contrastive_loss: 0.045938 (0.11560) Loss: 0.045938 (0.11560) 2025-03-19,20:04:21 | INFO | Train Epoch: 17 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 148.921/s, 148.921/s/gpu LR: 0.000075 Logit Scale: 27.440 Contrastive_loss: 0.091846 (0.11543) Loss: 0.091846 (0.11543) 2025-03-19,20:04:43 | INFO | Train Epoch: 17 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 146.806/s, 146.806/s/gpu LR: 0.000075 Logit Scale: 27.443 Contrastive_loss: 0.17769 (0.11587) Loss: 0.17769 (0.11587) 2025-03-19,20:05:04 | INFO | Train Epoch: 17 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 149.774/s, 149.774/s/gpu LR: 0.000075 Logit Scale: 27.424 Contrastive_loss: 0.074247 (0.11558) Loss: 0.074247 (0.11558) 2025-03-19,20:05:26 | INFO | Train Epoch: 17 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 152.208/s, 152.208/s/gpu LR: 0.000075 Logit Scale: 27.365 Contrastive_loss: 0.14558 (0.11579) Loss: 0.14558 (0.11579) 2025-03-19,20:05:47 | INFO | Train Epoch: 17 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.213, 150.214/s, 150.214/s/gpu LR: 0.000075 Logit Scale: 27.369 Contrastive_loss: 0.16087 (0.11610) Loss: 0.16087 (0.11610) 2025-03-19,20:06:09 | INFO | Train Epoch: 17 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 147.910/s, 147.910/s/gpu LR: 0.000075 Logit Scale: 27.360 Contrastive_loss: 0.085374 (0.11589) Loss: 0.085374 (0.11589) 2025-03-19,20:06:30 | INFO | Train Epoch: 17 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.543/s, 148.543/s/gpu LR: 0.000075 Logit Scale: 27.343 Contrastive_loss: 0.0092761 (0.11516) Loss: 0.0092761 (0.11516) 2025-03-19,20:06:52 | INFO | Train Epoch: 17 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.345/s, 148.345/s/gpu LR: 0.000075 Logit Scale: 27.372 Contrastive_loss: 0.011657 (0.11446) Loss: 0.011657 (0.11446) 2025-03-19,20:07:13 | INFO | Train Epoch: 17 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.775/s, 148.775/s/gpu LR: 0.000075 Logit Scale: 27.382 Contrastive_loss: 0.29633 (0.11568) Loss: 0.29633 (0.11568) 2025-03-19,20:07:35 | INFO | Train Epoch: 17 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.621/s, 149.621/s/gpu LR: 0.000075 Logit Scale: 27.378 Contrastive_loss: 0.22755 (0.11644) Loss: 0.22755 (0.11644) 2025-03-19,20:07:56 | INFO | Train Epoch: 17 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.805/s, 149.805/s/gpu LR: 0.000075 Logit Scale: 27.377 Contrastive_loss: 0.19648 (0.11697) Loss: 0.19648 (0.11697) 2025-03-19,20:08:18 | INFO | Train Epoch: 17 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 150.440/s, 150.440/s/gpu LR: 0.000075 Logit Scale: 27.384 Contrastive_loss: 0.035101 (0.11643) Loss: 0.035101 (0.11643) 2025-03-19,20:08:39 | INFO | Train Epoch: 17 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 152.007/s, 152.007/s/gpu LR: 0.000075 Logit Scale: 27.399 Contrastive_loss: 0.21925 (0.11710) Loss: 0.21925 (0.11710) 2025-03-19,20:09:01 | INFO | Train Epoch: 17 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 150.061/s, 150.061/s/gpu LR: 0.000075 Logit Scale: 27.414 Contrastive_loss: 0.0014478 (0.11635) Loss: 0.0014478 (0.11635) 2025-03-19,20:09:22 | INFO | Train Epoch: 17 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 150.887/s, 150.887/s/gpu LR: 0.000074 Logit Scale: 27.393 Contrastive_loss: 0.058523 (0.11597) Loss: 0.058523 (0.11597) 2025-03-19,20:09:44 | INFO | Train Epoch: 17 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 146.326/s, 146.326/s/gpu LR: 0.000074 Logit Scale: 27.402 Contrastive_loss: 0.12328 (0.11602) Loss: 0.12328 (0.11602) 2025-03-19,20:10:05 | INFO | Train Epoch: 17 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 148.454/s, 148.454/s/gpu LR: 0.000074 Logit Scale: 27.413 Contrastive_loss: 0.040191 (0.11553) Loss: 0.040191 (0.11553) 2025-03-19,20:10:27 | INFO | Train Epoch: 17 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 150.060/s, 150.060/s/gpu LR: 0.000074 Logit Scale: 27.405 Contrastive_loss: 0.062798 (0.11520) Loss: 0.062798 (0.11520) 2025-03-19,20:10:48 | INFO | Train Epoch: 17 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.242/s, 149.242/s/gpu LR: 0.000074 Logit Scale: 27.394 Contrastive_loss: 0.079663 (0.11497) Loss: 0.079663 (0.11497) 2025-03-19,20:11:10 | INFO | Train Epoch: 17 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.218, 146.600/s, 146.600/s/gpu LR: 0.000074 Logit Scale: 27.417 Contrastive_loss: 0.066561 (0.11467) Loss: 0.066561 (0.11467) 2025-03-19,20:11:32 | INFO | Train Epoch: 17 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 146.574/s, 146.574/s/gpu LR: 0.000074 Logit Scale: 27.392 Contrastive_loss: 0.048711 (0.11426) Loss: 0.048711 (0.11426) 2025-03-19,20:11:54 | INFO | Train Epoch: 17 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 150.084/s, 150.084/s/gpu LR: 0.000074 Logit Scale: 27.393 Contrastive_loss: 0.10543 (0.11420) Loss: 0.10543 (0.11420) 2025-03-19,20:12:15 | INFO | Train Epoch: 17 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 150.925/s, 150.925/s/gpu LR: 0.000074 Logit Scale: 27.360 Contrastive_loss: 0.39142 (0.11591) Loss: 0.39142 (0.11591) 2025-03-19,20:12:36 | INFO | Train Epoch: 17 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 151.880/s, 151.880/s/gpu LR: 0.000074 Logit Scale: 27.350 Contrastive_loss: 0.018330 (0.11531) Loss: 0.018330 (0.11531) 2025-03-19,20:12:58 | INFO | Train Epoch: 17 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.213, 149.935/s, 149.935/s/gpu LR: 0.000074 Logit Scale: 27.357 Contrastive_loss: 0.11066 (0.11528) Loss: 0.11066 (0.11528) 2025-03-19,20:13:19 | INFO | Train Epoch: 17 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.212, 151.340/s, 151.340/s/gpu LR: 0.000074 Logit Scale: 27.347 Contrastive_loss: 0.13653 (0.11541) Loss: 0.13653 (0.11541) 2025-03-19,20:13:40 | INFO | Train Epoch: 17 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.213, 148.687/s, 148.687/s/gpu LR: 0.000074 Logit Scale: 27.357 Contrastive_loss: 0.32601 (0.11668) Loss: 0.32601 (0.11668) 2025-03-19,20:14:02 | INFO | Train Epoch: 17 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 149.116/s, 149.116/s/gpu LR: 0.000074 Logit Scale: 27.370 Contrastive_loss: 0.13737 (0.11681) Loss: 0.13737 (0.11681) 2025-03-19,20:14:23 | INFO | Train Epoch: 17 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 149.449/s, 149.449/s/gpu LR: 0.000074 Logit Scale: 27.366 Contrastive_loss: 0.10465 (0.11673) Loss: 0.10465 (0.11673) 2025-03-19,20:14:45 | INFO | Train Epoch: 17 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 147.629/s, 147.629/s/gpu LR: 0.000074 Logit Scale: 27.369 Contrastive_loss: 0.037502 (0.11626) Loss: 0.037502 (0.11626) 2025-03-19,20:15:07 | INFO | Train Epoch: 17 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 141.307/s, 141.307/s/gpu LR: 0.000074 Logit Scale: 27.398 Contrastive_loss: 0.27899 (0.11722) Loss: 0.27899 (0.11722) 2025-03-19,20:15:29 | INFO | Train Epoch: 17 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.223, 143.912/s, 143.912/s/gpu LR: 0.000074 Logit Scale: 27.402 Contrastive_loss: 0.079384 (0.11700) Loss: 0.079384 (0.11700) 2025-03-19,20:15:51 | INFO | Train Epoch: 17 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 146.572/s, 146.572/s/gpu LR: 0.000074 Logit Scale: 27.388 Contrastive_loss: 0.15153 (0.11720) Loss: 0.15153 (0.11720) 2025-03-19,20:16:13 | INFO | Train Epoch: 17 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.220, 145.800/s, 145.800/s/gpu LR: 0.000074 Logit Scale: 27.364 Contrastive_loss: 0.048258 (0.11680) Loss: 0.048258 (0.11680) 2025-03-19,20:16:35 | INFO | Train Epoch: 17 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 146.078/s, 146.078/s/gpu LR: 0.000074 Logit Scale: 27.364 Contrastive_loss: 0.088416 (0.11664) Loss: 0.088416 (0.11664) 2025-03-19,20:16:57 | INFO | Train Epoch: 17 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.220, 146.163/s, 146.163/s/gpu LR: 0.000074 Logit Scale: 27.390 Contrastive_loss: 0.23254 (0.11730) Loss: 0.23254 (0.11730) 2025-03-19,20:17:19 | INFO | Train Epoch: 17 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.221, 146.229/s, 146.229/s/gpu LR: 0.000074 Logit Scale: 27.385 Contrastive_loss: 0.20730 (0.11781) Loss: 0.20730 (0.11781) 2025-03-19,20:17:41 | INFO | Train Epoch: 17 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 144.845/s, 144.845/s/gpu LR: 0.000073 Logit Scale: 27.354 Contrastive_loss: 0.11039 (0.11777) Loss: 0.11039 (0.11777) 2025-03-19,20:18:03 | INFO | Train Epoch: 17 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 144.782/s, 144.782/s/gpu LR: 0.000073 Logit Scale: 27.381 Contrastive_loss: 0.034558 (0.11730) Loss: 0.034558 (0.11730) 2025-03-19,20:18:24 | INFO | Train Epoch: 17 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 146.138/s, 146.138/s/gpu LR: 0.000073 Logit Scale: 27.395 Contrastive_loss: 0.44656 (0.11914) Loss: 0.44656 (0.11914) 2025-03-19,20:18:47 | INFO | Train Epoch: 17 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.221, 145.046/s, 145.046/s/gpu LR: 0.000073 Logit Scale: 27.376 Contrastive_loss: 0.091409 (0.11899) Loss: 0.091409 (0.11899) 2025-03-19,20:19:08 | INFO | Train Epoch: 17 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.218, 150.972/s, 150.972/s/gpu LR: 0.000073 Logit Scale: 27.394 Contrastive_loss: 0.12089 (0.11900) Loss: 0.12089 (0.11900) 2025-03-19,20:19:30 | INFO | Train Epoch: 17 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.213, 149.787/s, 149.787/s/gpu LR: 0.000073 Logit Scale: 27.398 Contrastive_loss: 0.040047 (0.11857) Loss: 0.040047 (0.11857) 2025-03-19,20:19:51 | INFO | Train Epoch: 17 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.831/s, 149.831/s/gpu LR: 0.000073 Logit Scale: 27.381 Contrastive_loss: 0.15160 (0.11875) Loss: 0.15160 (0.11875) 2025-03-19,20:20:13 | INFO | Train Epoch: 17 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 147.452/s, 147.452/s/gpu LR: 0.000073 Logit Scale: 27.356 Contrastive_loss: 0.017478 (0.11820) Loss: 0.017478 (0.11820) 2025-03-19,20:20:34 | INFO | Train Epoch: 17 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.212, 151.192/s, 151.192/s/gpu LR: 0.000073 Logit Scale: 27.364 Contrastive_loss: 0.13932 (0.11831) Loss: 0.13932 (0.11831) 2025-03-19,20:20:55 | INFO | Train Epoch: 17 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.213, 150.114/s, 150.114/s/gpu LR: 0.000073 Logit Scale: 27.370 Contrastive_loss: 0.12066 (0.11832) Loss: 0.12066 (0.11832) 2025-03-19,20:21:16 | INFO | Train Epoch: 17 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 147.427/s, 147.427/s/gpu LR: 0.000073 Logit Scale: 27.354 Contrastive_loss: 0.44031 (0.12004) Loss: 0.44031 (0.12004) 2025-03-19,20:21:38 | INFO | Train Epoch: 17 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 149.284/s, 149.284/s/gpu LR: 0.000073 Logit Scale: 27.331 Contrastive_loss: 0.36589 (0.12135) Loss: 0.36589 (0.12135) 2025-03-19,20:22:00 | INFO | Train Epoch: 17 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.217, 149.190/s, 149.190/s/gpu LR: 0.000073 Logit Scale: 27.312 Contrastive_loss: 0.073655 (0.12110) Loss: 0.073655 (0.12110) 2025-03-19,20:22:21 | INFO | Train Epoch: 17 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.553/s, 149.553/s/gpu LR: 0.000073 Logit Scale: 27.295 Contrastive_loss: 0.25939 (0.12183) Loss: 0.25939 (0.12183) 2025-03-19,20:22:43 | INFO | Train Epoch: 17 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.093/s, 149.093/s/gpu LR: 0.000073 Logit Scale: 27.316 Contrastive_loss: 0.057382 (0.12149) Loss: 0.057382 (0.12149) 2025-03-19,20:23:05 | INFO | Train Epoch: 17 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 147.544/s, 147.544/s/gpu LR: 0.000073 Logit Scale: 27.312 Contrastive_loss: 0.038583 (0.12106) Loss: 0.038583 (0.12106) 2025-03-19,20:23:26 | INFO | Train Epoch: 17 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 147.455/s, 147.455/s/gpu LR: 0.000073 Logit Scale: 27.310 Contrastive_loss: 0.093949 (0.12092) Loss: 0.093949 (0.12092) 2025-03-19,20:23:48 | INFO | Train Epoch: 17 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 149.106/s, 149.106/s/gpu LR: 0.000073 Logit Scale: 27.304 Contrastive_loss: 0.050463 (0.12056) Loss: 0.050463 (0.12056) 2025-03-19,20:24:09 | INFO | Train Epoch: 17 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 150.037/s, 150.037/s/gpu LR: 0.000073 Logit Scale: 27.329 Contrastive_loss: 0.18012 (0.12086) Loss: 0.18012 (0.12086) 2025-03-19,20:24:31 | INFO | Train Epoch: 17 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.050/s, 149.050/s/gpu LR: 0.000073 Logit Scale: 27.299 Contrastive_loss: 0.12717 (0.12089) Loss: 0.12717 (0.12089) 2025-03-19,20:24:52 | INFO | Train Epoch: 17 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 148.369/s, 148.369/s/gpu LR: 0.000073 Logit Scale: 27.317 Contrastive_loss: 0.10598 (0.12082) Loss: 0.10598 (0.12082) 2025-03-19,20:25:14 | INFO | Train Epoch: 17 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.218, 148.757/s, 148.757/s/gpu LR: 0.000073 Logit Scale: 27.297 Contrastive_loss: 0.079652 (0.12061) Loss: 0.079652 (0.12061) 2025-03-19,20:25:35 | INFO | Train Epoch: 17 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 151.219/s, 151.219/s/gpu LR: 0.000073 Logit Scale: 27.283 Contrastive_loss: 0.076456 (0.12039) Loss: 0.076456 (0.12039) 2025-03-19,20:25:57 | INFO | Train Epoch: 17 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 147.955/s, 147.955/s/gpu LR: 0.000073 Logit Scale: 27.285 Contrastive_loss: 0.029249 (0.11993) Loss: 0.029249 (0.11993) 2025-03-19,20:26:19 | INFO | Train Epoch: 17 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.351/s, 148.351/s/gpu LR: 0.000072 Logit Scale: 27.299 Contrastive_loss: 0.011439 (0.11939) Loss: 0.011439 (0.11939) 2025-03-19,20:26:40 | INFO | Train Epoch: 17 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 149.262/s, 149.262/s/gpu LR: 0.000072 Logit Scale: 27.280 Contrastive_loss: 0.056389 (0.11908) Loss: 0.056389 (0.11908) 2025-03-19,20:27:02 | INFO | Train Epoch: 17 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 148.958/s, 148.958/s/gpu LR: 0.000072 Logit Scale: 27.282 Contrastive_loss: 0.15588 (0.11926) Loss: 0.15588 (0.11926) 2025-03-19,20:27:23 | INFO | Train Epoch: 17 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 148.899/s, 148.899/s/gpu LR: 0.000072 Logit Scale: 27.327 Contrastive_loss: 0.046855 (0.11891) Loss: 0.046855 (0.11891) 2025-03-19,20:27:45 | INFO | Train Epoch: 17 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 147.002/s, 147.002/s/gpu LR: 0.000072 Logit Scale: 27.338 Contrastive_loss: 0.014339 (0.11840) Loss: 0.014339 (0.11840) 2025-03-19,20:28:07 | INFO | Train Epoch: 17 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.222, 144.092/s, 144.092/s/gpu LR: 0.000072 Logit Scale: 27.343 Contrastive_loss: 0.13839 (0.11849) Loss: 0.13839 (0.11849) 2025-03-19,20:28:29 | INFO | Train Epoch: 17 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.219, 147.957/s, 147.957/s/gpu LR: 0.000072 Logit Scale: 27.337 Contrastive_loss: 0.15456 (0.11867) Loss: 0.15456 (0.11867) 2025-03-19,20:28:51 | INFO | Train Epoch: 17 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.220, 146.872/s, 146.872/s/gpu LR: 0.000072 Logit Scale: 27.300 Contrastive_loss: 0.080359 (0.11848) Loss: 0.080359 (0.11848) 2025-03-19,20:29:13 | INFO | Train Epoch: 17 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.220, 145.059/s, 145.059/s/gpu LR: 0.000072 Logit Scale: 27.329 Contrastive_loss: 0.10322 (0.11841) Loss: 0.10322 (0.11841) 2025-03-19,20:29:34 | INFO | Train Epoch: 17 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.218, 146.493/s, 146.493/s/gpu LR: 0.000072 Logit Scale: 27.342 Contrastive_loss: 0.084261 (0.11825) Loss: 0.084261 (0.11825) 2025-03-19,20:29:56 | INFO | Train Epoch: 17 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 147.170/s, 147.170/s/gpu LR: 0.000072 Logit Scale: 27.360 Contrastive_loss: 0.36197 (0.11940) Loss: 0.36197 (0.11940) 2025-03-19,20:30:18 | INFO | Train Epoch: 17 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.218, 141.842/s, 141.842/s/gpu LR: 0.000072 Logit Scale: 27.340 Contrastive_loss: 0.12225 (0.11942) Loss: 0.12225 (0.11942) 2025-03-19,20:30:40 | INFO | Train Epoch: 17 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.220, 146.724/s, 146.724/s/gpu LR: 0.000072 Logit Scale: 27.331 Contrastive_loss: 0.038584 (0.11904) Loss: 0.038584 (0.11904) 2025-03-19,20:31:02 | INFO | Train Epoch: 17 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 147.739/s, 147.739/s/gpu LR: 0.000072 Logit Scale: 27.345 Contrastive_loss: 0.053783 (0.11873) Loss: 0.053783 (0.11873) 2025-03-19,20:31:24 | INFO | Train Epoch: 17 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 147.956/s, 147.956/s/gpu LR: 0.000072 Logit Scale: 27.359 Contrastive_loss: 0.072886 (0.11852) Loss: 0.072886 (0.11852) 2025-03-19,20:31:45 | INFO | Train Epoch: 17 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 148.597/s, 148.597/s/gpu LR: 0.000072 Logit Scale: 27.353 Contrastive_loss: 0.065894 (0.11827) Loss: 0.065894 (0.11827) 2025-03-19,20:32:07 | INFO | Train Epoch: 17 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 147.750/s, 147.750/s/gpu LR: 0.000072 Logit Scale: 27.357 Contrastive_loss: 0.075121 (0.11808) Loss: 0.075121 (0.11808) 2025-03-19,20:32:29 | INFO | Train Epoch: 17 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 147.903/s, 147.903/s/gpu LR: 0.000072 Logit Scale: 27.360 Contrastive_loss: 0.34219 (0.11910) Loss: 0.34219 (0.11910) 2025-03-19,20:32:50 | INFO | Train Epoch: 17 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.212, 147.806/s, 147.806/s/gpu LR: 0.000072 Logit Scale: 27.363 Contrastive_loss: 0.062197 (0.11884) Loss: 0.062197 (0.11884) 2025-03-19,20:33:11 | INFO | Train Epoch: 17 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.767/s, 149.767/s/gpu LR: 0.000072 Logit Scale: 27.388 Contrastive_loss: 0.021505 (0.11840) Loss: 0.021505 (0.11840) 2025-03-19,20:33:33 | INFO | Train Epoch: 17 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 147.992/s, 147.992/s/gpu LR: 0.000072 Logit Scale: 27.389 Contrastive_loss: 0.030158 (0.11800) Loss: 0.030158 (0.11800) 2025-03-19,20:33:54 | INFO | Train Epoch: 17 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 149.982/s, 149.982/s/gpu LR: 0.000072 Logit Scale: 27.386 Contrastive_loss: 0.10638 (0.11795) Loss: 0.10638 (0.11795) 2025-03-19,20:34:16 | INFO | Train Epoch: 17 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.018/s, 150.018/s/gpu LR: 0.000072 Logit Scale: 27.403 Contrastive_loss: 0.32065 (0.11886) Loss: 0.32065 (0.11886) 2025-03-19,20:34:37 | INFO | Train Epoch: 17 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.213, 150.733/s, 150.733/s/gpu LR: 0.000071 Logit Scale: 27.417 Contrastive_loss: 0.0058108 (0.11835) Loss: 0.0058108 (0.11835) 2025-03-19,20:34:58 | INFO | Train Epoch: 17 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 149.360/s, 149.360/s/gpu LR: 0.000071 Logit Scale: 27.425 Contrastive_loss: 0.022419 (0.11793) Loss: 0.022419 (0.11793) 2025-03-19,20:35:20 | INFO | Train Epoch: 17 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.443/s, 149.443/s/gpu LR: 0.000071 Logit Scale: 27.438 Contrastive_loss: 0.019681 (0.11749) Loss: 0.019681 (0.11749) 2025-03-19,20:35:41 | INFO | Train Epoch: 17 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 152.074/s, 152.074/s/gpu LR: 0.000071 Logit Scale: 27.427 Contrastive_loss: 0.034023 (0.11713) Loss: 0.034023 (0.11713) 2025-03-19,20:36:03 | INFO | Train Epoch: 17 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 147.802/s, 147.802/s/gpu LR: 0.000071 Logit Scale: 27.414 Contrastive_loss: 0.070335 (0.11692) Loss: 0.070335 (0.11692) 2025-03-19,20:36:24 | INFO | Train Epoch: 17 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 149.848/s, 149.848/s/gpu LR: 0.000071 Logit Scale: 27.407 Contrastive_loss: 0.036019 (0.11657) Loss: 0.036019 (0.11657) 2025-03-19,20:36:46 | INFO | Train Epoch: 17 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 147.869/s, 147.869/s/gpu LR: 0.000071 Logit Scale: 27.410 Contrastive_loss: 0.15743 (0.11674) Loss: 0.15743 (0.11674) 2025-03-19,20:37:07 | INFO | Train Epoch: 17 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 148.952/s, 148.952/s/gpu LR: 0.000071 Logit Scale: 27.411 Contrastive_loss: 0.080321 (0.11659) Loss: 0.080321 (0.11659) 2025-03-19,20:37:29 | INFO | Train Epoch: 17 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 151.566/s, 151.566/s/gpu LR: 0.000071 Logit Scale: 27.413 Contrastive_loss: 0.19518 (0.11693) Loss: 0.19518 (0.11693) 2025-03-19,20:37:50 | INFO | Train Epoch: 17 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 148.461/s, 148.461/s/gpu LR: 0.000071 Logit Scale: 27.427 Contrastive_loss: 0.084415 (0.11679) Loss: 0.084415 (0.11679) 2025-03-19,20:38:12 | INFO | Train Epoch: 17 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.217, 147.238/s, 147.238/s/gpu LR: 0.000071 Logit Scale: 27.443 Contrastive_loss: 0.040011 (0.11646) Loss: 0.040011 (0.11646) 2025-03-19,20:38:33 | INFO | Train Epoch: 17 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.864/s, 147.864/s/gpu LR: 0.000071 Logit Scale: 27.448 Contrastive_loss: 0.035794 (0.11611) Loss: 0.035794 (0.11611) 2025-03-19,20:38:55 | INFO | Train Epoch: 17 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 150.231/s, 150.231/s/gpu LR: 0.000071 Logit Scale: 27.435 Contrastive_loss: 0.13135 (0.11618) Loss: 0.13135 (0.11618) 2025-03-19,20:39:16 | INFO | Train Epoch: 17 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.570/s, 149.570/s/gpu LR: 0.000071 Logit Scale: 27.439 Contrastive_loss: 0.019047 (0.11577) Loss: 0.019047 (0.11577) 2025-03-19,20:39:38 | INFO | Train Epoch: 17 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 148.957/s, 148.957/s/gpu LR: 0.000071 Logit Scale: 27.436 Contrastive_loss: 0.13356 (0.11584) Loss: 0.13356 (0.11584) 2025-03-19,20:39:59 | INFO | Train Epoch: 17 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 149.431/s, 149.431/s/gpu LR: 0.000071 Logit Scale: 27.416 Contrastive_loss: 0.035685 (0.11551) Loss: 0.035685 (0.11551) 2025-03-19,20:40:20 | INFO | Train Epoch: 17 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.214, 150.132/s, 150.132/s/gpu LR: 0.000071 Logit Scale: 27.417 Contrastive_loss: 0.24229 (0.11604) Loss: 0.24229 (0.11604) 2025-03-19,20:40:28 | INFO | Train Epoch: 17 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.216, 150.321/s, 150.321/s/gpu LR: 0.000071 Logit Scale: 27.432 Contrastive_loss: 0.13187 (0.11610) Loss: 0.13187 (0.11610) 2025-03-19,20:40:29 | INFO | Eval Epoch: 18 [32 / 7443] Clip Loss: 3.189835 2025-03-19,20:40:34 | INFO | Eval Epoch: 18 [3232 / 7443] Clip Loss: 0.819787 2025-03-19,20:40:40 | INFO | Eval Epoch: 18 [6432 / 7443] Clip Loss: 0.620082 2025-03-19,20:40:43 | INFO | Eval Epoch: 18 image_to_text_mean_rank: 74.8055 image_to_text_median_rank: 6.0000 image_to_text_R@1: 0.1464 image_to_text_R@5: 0.4727 image_to_text_R@10: 0.6489 text_to_image_mean_rank: 49.2061 text_to_image_median_rank: 6.0000 text_to_image_R@1: 0.1556 text_to_image_R@5: 0.4810 text_to_image_R@10: 0.6497 clip_val_loss: 0.5786 epoch: 18.0000 num_samples: 7443.0000 2025-03-19,20:41:16 | INFO | Start epoch 18 2025-03-19,20:41:17 | INFO | Train Epoch: 18 [ 32/766009 (0%)] Data (t): 0.183 Batch (t): 0.385, 83.1267/s, 83.1267/s/gpu LR: 0.000071 Logit Scale: 27.433 Contrastive_loss: 0.025230 (0.025230) Loss: 0.025230 (0.025230) 2025-03-19,20:41:38 | INFO | Train Epoch: 18 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 150.317/s, 150.317/s/gpu LR: 0.000071 Logit Scale: 27.456 Contrastive_loss: 0.053784 (0.039507) Loss: 0.053784 (0.039507) 2025-03-19,20:42:00 | INFO | Train Epoch: 18 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 140.318/s, 140.318/s/gpu LR: 0.000071 Logit Scale: 27.474 Contrastive_loss: 0.022498 (0.033837) Loss: 0.022498 (0.033837) 2025-03-19,20:42:21 | INFO | Train Epoch: 18 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 149.572/s, 149.572/s/gpu LR: 0.000071 Logit Scale: 27.498 Contrastive_loss: 0.17303 (0.068637) Loss: 0.17303 (0.068637) 2025-03-19,20:42:43 | INFO | Train Epoch: 18 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 145.254/s, 145.254/s/gpu LR: 0.000071 Logit Scale: 27.526 Contrastive_loss: 0.35169 (0.12525) Loss: 0.35169 (0.12525) 2025-03-19,20:43:04 | INFO | Train Epoch: 18 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.217, 147.152/s, 147.152/s/gpu LR: 0.000071 Logit Scale: 27.558 Contrastive_loss: 0.061282 (0.11459) Loss: 0.061282 (0.11459) 2025-03-19,20:43:26 | INFO | Train Epoch: 18 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 148.885/s, 148.885/s/gpu LR: 0.000071 Logit Scale: 27.581 Contrastive_loss: 0.063479 (0.10729) Loss: 0.063479 (0.10729) 2025-03-19,20:43:48 | INFO | Train Epoch: 18 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.446/s, 149.446/s/gpu LR: 0.000070 Logit Scale: 27.607 Contrastive_loss: 0.089843 (0.10510) Loss: 0.089843 (0.10510) 2025-03-19,20:44:09 | INFO | Train Epoch: 18 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.268/s, 149.268/s/gpu LR: 0.000070 Logit Scale: 27.632 Contrastive_loss: 0.15193 (0.11031) Loss: 0.15193 (0.11031) 2025-03-19,20:44:31 | INFO | Train Epoch: 18 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.218, 150.923/s, 150.923/s/gpu LR: 0.000070 Logit Scale: 27.646 Contrastive_loss: 0.085478 (0.10783) Loss: 0.085478 (0.10783) 2025-03-19,20:44:52 | INFO | Train Epoch: 18 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 148.130/s, 148.130/s/gpu LR: 0.000070 Logit Scale: 27.633 Contrastive_loss: 0.069000 (0.10430) Loss: 0.069000 (0.10430) 2025-03-19,20:45:14 | INFO | Train Epoch: 18 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 149.637/s, 149.637/s/gpu LR: 0.000070 Logit Scale: 27.649 Contrastive_loss: 0.060232 (0.10062) Loss: 0.060232 (0.10062) 2025-03-19,20:45:35 | INFO | Train Epoch: 18 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 148.191/s, 148.191/s/gpu LR: 0.000070 Logit Scale: 27.635 Contrastive_loss: 0.31415 (0.11705) Loss: 0.31415 (0.11705) 2025-03-19,20:45:57 | INFO | Train Epoch: 18 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 148.216/s, 148.216/s/gpu LR: 0.000070 Logit Scale: 27.612 Contrastive_loss: 0.018225 (0.10999) Loss: 0.018225 (0.10999) 2025-03-19,20:46:18 | INFO | Train Epoch: 18 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 145.949/s, 145.949/s/gpu LR: 0.000070 Logit Scale: 27.641 Contrastive_loss: 0.026094 (0.10440) Loss: 0.026094 (0.10440) 2025-03-19,20:46:40 | INFO | Train Epoch: 18 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.217, 145.354/s, 145.354/s/gpu LR: 0.000070 Logit Scale: 27.654 Contrastive_loss: 0.082969 (0.10306) Loss: 0.082969 (0.10306) 2025-03-19,20:47:02 | INFO | Train Epoch: 18 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.220, 148.755/s, 148.755/s/gpu LR: 0.000070 Logit Scale: 27.685 Contrastive_loss: 0.057502 (0.10038) Loss: 0.057502 (0.10038) 2025-03-19,20:47:24 | INFO | Train Epoch: 18 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 84.3522/s, 84.3522/s/gpu LR: 0.000070 Logit Scale: 27.664 Contrastive_loss: 0.025811 (0.096235) Loss: 0.025811 (0.096235) 2025-03-19,20:47:45 | INFO | Train Epoch: 18 [ 57632/766009 (8%)] Data (t): 0.000 Batch (t): 0.214, 148.111/s, 148.111/s/gpu LR: 0.000070 Logit Scale: 27.679 Contrastive_loss: 0.061803 (0.094423) Loss: 0.061803 (0.094423) 2025-03-19,20:48:07 | INFO | Train Epoch: 18 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 149.650/s, 149.650/s/gpu LR: 0.000070 Logit Scale: 27.655 Contrastive_loss: 0.025081 (0.090956) Loss: 0.025081 (0.090956) 2025-03-19,20:48:28 | INFO | Train Epoch: 18 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 145.846/s, 145.846/s/gpu LR: 0.000070 Logit Scale: 27.686 Contrastive_loss: 0.010844 (0.087141) Loss: 0.010844 (0.087141) 2025-03-19,20:48:50 | INFO | Train Epoch: 18 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.217, 145.375/s, 145.375/s/gpu LR: 0.000070 Logit Scale: 27.683 Contrastive_loss: 0.14820 (0.089916) Loss: 0.14820 (0.089916) 2025-03-19,20:49:12 | INFO | Train Epoch: 18 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.221, 146.142/s, 146.142/s/gpu LR: 0.000070 Logit Scale: 27.677 Contrastive_loss: 0.0035497 (0.086161) Loss: 0.0035497 (0.086161) 2025-03-19,20:49:34 | INFO | Train Epoch: 18 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 144.302/s, 144.302/s/gpu LR: 0.000070 Logit Scale: 27.668 Contrastive_loss: 0.14040 (0.088421) Loss: 0.14040 (0.088421) 2025-03-19,20:49:56 | INFO | Train Epoch: 18 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.218, 149.183/s, 149.183/s/gpu LR: 0.000070 Logit Scale: 27.684 Contrastive_loss: 0.30262 (0.096989) Loss: 0.30262 (0.096989) 2025-03-19,20:50:17 | INFO | Train Epoch: 18 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.049/s, 149.049/s/gpu LR: 0.000070 Logit Scale: 27.678 Contrastive_loss: 0.0074855 (0.093547) Loss: 0.0074855 (0.093547) 2025-03-19,20:50:39 | INFO | Train Epoch: 18 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 149.242/s, 149.242/s/gpu LR: 0.000070 Logit Scale: 27.667 Contrastive_loss: 0.078090 (0.092974) Loss: 0.078090 (0.092974) 2025-03-19,20:51:01 | INFO | Train Epoch: 18 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.215, 149.003/s, 149.003/s/gpu LR: 0.000070 Logit Scale: 27.662 Contrastive_loss: 0.046507 (0.091315) Loss: 0.046507 (0.091315) 2025-03-19,20:51:22 | INFO | Train Epoch: 18 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 149.892/s, 149.892/s/gpu LR: 0.000070 Logit Scale: 27.670 Contrastive_loss: 0.14059 (0.093014) Loss: 0.14059 (0.093014) 2025-03-19,20:51:44 | INFO | Train Epoch: 18 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 151.030/s, 151.030/s/gpu LR: 0.000070 Logit Scale: 27.686 Contrastive_loss: 0.074138 (0.092384) Loss: 0.074138 (0.092384) 2025-03-19,20:52:05 | INFO | Train Epoch: 18 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.012/s, 149.012/s/gpu LR: 0.000070 Logit Scale: 27.693 Contrastive_loss: 0.073318 (0.091769) Loss: 0.073318 (0.091769) 2025-03-19,20:52:27 | INFO | Train Epoch: 18 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.246/s, 149.246/s/gpu LR: 0.000069 Logit Scale: 27.698 Contrastive_loss: 0.13961 (0.093264) Loss: 0.13961 (0.093264) 2025-03-19,20:52:48 | INFO | Train Epoch: 18 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.213, 149.381/s, 149.381/s/gpu LR: 0.000069 Logit Scale: 27.706 Contrastive_loss: 0.0089379 (0.090709) Loss: 0.0089379 (0.090709) 2025-03-19,20:53:09 | INFO | Train Epoch: 18 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 150.615/s, 150.615/s/gpu LR: 0.000069 Logit Scale: 27.711 Contrastive_loss: 0.038039 (0.089160) Loss: 0.038039 (0.089160) 2025-03-19,20:53:31 | INFO | Train Epoch: 18 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.219, 146.030/s, 146.030/s/gpu LR: 0.000069 Logit Scale: 27.735 Contrastive_loss: 0.15667 (0.091089) Loss: 0.15667 (0.091089) 2025-03-19,20:53:53 | INFO | Train Epoch: 18 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.220, 149.864/s, 149.864/s/gpu LR: 0.000069 Logit Scale: 27.727 Contrastive_loss: 0.16802 (0.093226) Loss: 0.16802 (0.093226) 2025-03-19,20:54:15 | INFO | Train Epoch: 18 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 149.708/s, 149.708/s/gpu LR: 0.000069 Logit Scale: 27.729 Contrastive_loss: 0.24823 (0.097415) Loss: 0.24823 (0.097415) 2025-03-19,20:54:36 | INFO | Train Epoch: 18 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.038/s, 149.038/s/gpu LR: 0.000069 Logit Scale: 27.684 Contrastive_loss: 0.015178 (0.095251) Loss: 0.015178 (0.095251) 2025-03-19,20:54:58 | INFO | Train Epoch: 18 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 150.326/s, 150.326/s/gpu LR: 0.000069 Logit Scale: 27.683 Contrastive_loss: 0.051629 (0.094132) Loss: 0.051629 (0.094132) 2025-03-19,20:55:19 | INFO | Train Epoch: 18 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 144.349/s, 144.349/s/gpu LR: 0.000069 Logit Scale: 27.665 Contrastive_loss: 0.14998 (0.095529) Loss: 0.14998 (0.095529) 2025-03-19,20:55:41 | INFO | Train Epoch: 18 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.221, 144.601/s, 144.601/s/gpu LR: 0.000069 Logit Scale: 27.683 Contrastive_loss: 0.088291 (0.095352) Loss: 0.088291 (0.095352) 2025-03-19,20:56:03 | INFO | Train Epoch: 18 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.218, 149.514/s, 149.514/s/gpu LR: 0.000069 Logit Scale: 27.686 Contrastive_loss: 0.17039 (0.097139) Loss: 0.17039 (0.097139) 2025-03-19,20:56:24 | INFO | Train Epoch: 18 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 151.899/s, 151.899/s/gpu LR: 0.000069 Logit Scale: 27.677 Contrastive_loss: 0.0025014 (0.094938) Loss: 0.0025014 (0.094938) 2025-03-19,20:56:46 | INFO | Train Epoch: 18 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 148.577/s, 148.577/s/gpu LR: 0.000069 Logit Scale: 27.691 Contrastive_loss: 0.10454 (0.095156) Loss: 0.10454 (0.095156) 2025-03-19,20:57:07 | INFO | Train Epoch: 18 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 149.572/s, 149.572/s/gpu LR: 0.000069 Logit Scale: 27.712 Contrastive_loss: 0.16342 (0.096673) Loss: 0.16342 (0.096673) 2025-03-19,20:57:29 | INFO | Train Epoch: 18 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.212, 151.267/s, 151.267/s/gpu LR: 0.000069 Logit Scale: 27.701 Contrastive_loss: 0.0084761 (0.094756) Loss: 0.0084761 (0.094756) 2025-03-19,20:57:50 | INFO | Train Epoch: 18 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 149.956/s, 149.956/s/gpu LR: 0.000069 Logit Scale: 27.714 Contrastive_loss: 0.031463 (0.093409) Loss: 0.031463 (0.093409) 2025-03-19,20:58:11 | INFO | Train Epoch: 18 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.212, 150.867/s, 150.867/s/gpu LR: 0.000069 Logit Scale: 27.690 Contrastive_loss: 0.16795 (0.094962) Loss: 0.16795 (0.094962) 2025-03-19,20:58:33 | INFO | Train Epoch: 18 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 148.541/s, 148.541/s/gpu LR: 0.000069 Logit Scale: 27.671 Contrastive_loss: 0.20849 (0.097279) Loss: 0.20849 (0.097279) 2025-03-19,20:58:54 | INFO | Train Epoch: 18 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 150.398/s, 150.398/s/gpu LR: 0.000069 Logit Scale: 27.656 Contrastive_loss: 0.074299 (0.096820) Loss: 0.074299 (0.096820) 2025-03-19,20:59:15 | INFO | Train Epoch: 18 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 151.339/s, 151.339/s/gpu LR: 0.000069 Logit Scale: 27.656 Contrastive_loss: 0.059723 (0.096092) Loss: 0.059723 (0.096092) 2025-03-19,20:59:37 | INFO | Train Epoch: 18 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.213, 147.662/s, 147.662/s/gpu LR: 0.000069 Logit Scale: 27.653 Contrastive_loss: 0.076630 (0.095718) Loss: 0.076630 (0.095718) 2025-03-19,20:59:59 | INFO | Train Epoch: 18 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.221, 145.027/s, 145.027/s/gpu LR: 0.000069 Logit Scale: 27.664 Contrastive_loss: 0.0018249 (0.093946) Loss: 0.0018249 (0.093946) 2025-03-19,21:00:21 | INFO | Train Epoch: 18 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.220, 147.202/s, 147.202/s/gpu LR: 0.000069 Logit Scale: 27.694 Contrastive_loss: 0.059073 (0.093301) Loss: 0.059073 (0.093301) 2025-03-19,21:00:43 | INFO | Train Epoch: 18 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.219, 146.108/s, 146.108/s/gpu LR: 0.000069 Logit Scale: 27.705 Contrastive_loss: 0.088917 (0.093221) Loss: 0.088917 (0.093221) 2025-03-19,21:01:05 | INFO | Train Epoch: 18 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.220, 145.788/s, 145.788/s/gpu LR: 0.000068 Logit Scale: 27.725 Contrastive_loss: 0.075665 (0.092907) Loss: 0.075665 (0.092907) 2025-03-19,21:01:27 | INFO | Train Epoch: 18 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.219, 146.927/s, 146.927/s/gpu LR: 0.000068 Logit Scale: 27.734 Contrastive_loss: 0.17444 (0.094338) Loss: 0.17444 (0.094338) 2025-03-19,21:01:48 | INFO | Train Epoch: 18 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.219, 145.146/s, 145.146/s/gpu LR: 0.000068 Logit Scale: 27.727 Contrastive_loss: 0.043674 (0.093464) Loss: 0.043674 (0.093464) 2025-03-19,21:02:10 | INFO | Train Epoch: 18 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.219, 148.967/s, 148.967/s/gpu LR: 0.000068 Logit Scale: 27.730 Contrastive_loss: 0.0075058 (0.092007) Loss: 0.0075058 (0.092007) 2025-03-19,21:02:32 | INFO | Train Epoch: 18 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 147.023/s, 147.023/s/gpu LR: 0.000068 Logit Scale: 27.752 Contrastive_loss: 0.040665 (0.091152) Loss: 0.040665 (0.091152) 2025-03-19,21:02:54 | INFO | Train Epoch: 18 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.219, 147.870/s, 147.870/s/gpu LR: 0.000068 Logit Scale: 27.766 Contrastive_loss: 0.075310 (0.090892) Loss: 0.075310 (0.090892) 2025-03-19,21:03:16 | INFO | Train Epoch: 18 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.223, 144.232/s, 144.232/s/gpu LR: 0.000068 Logit Scale: 27.777 Contrastive_loss: 0.033444 (0.089965) Loss: 0.033444 (0.089965) 2025-03-19,21:03:38 | INFO | Train Epoch: 18 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.223, 145.359/s, 145.359/s/gpu LR: 0.000068 Logit Scale: 27.766 Contrastive_loss: 0.0055486 (0.088625) Loss: 0.0055486 (0.088625) 2025-03-19,21:04:00 | INFO | Train Epoch: 18 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.220, 147.609/s, 147.609/s/gpu LR: 0.000068 Logit Scale: 27.770 Contrastive_loss: 0.058189 (0.088150) Loss: 0.058189 (0.088150) 2025-03-19,21:04:22 | INFO | Train Epoch: 18 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 149.654/s, 149.654/s/gpu LR: 0.000068 Logit Scale: 27.792 Contrastive_loss: 0.14394 (0.089008) Loss: 0.14394 (0.089008) 2025-03-19,21:04:43 | INFO | Train Epoch: 18 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 150.008/s, 150.008/s/gpu LR: 0.000068 Logit Scale: 27.796 Contrastive_loss: 0.16506 (0.090161) Loss: 0.16506 (0.090161) 2025-03-19,21:05:05 | INFO | Train Epoch: 18 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.722/s, 149.722/s/gpu LR: 0.000068 Logit Scale: 27.804 Contrastive_loss: 0.31500 (0.093516) Loss: 0.31500 (0.093516) 2025-03-19,21:05:26 | INFO | Train Epoch: 18 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 151.358/s, 151.358/s/gpu LR: 0.000068 Logit Scale: 27.801 Contrastive_loss: 0.18631 (0.094881) Loss: 0.18631 (0.094881) 2025-03-19,21:05:48 | INFO | Train Epoch: 18 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.142/s, 149.142/s/gpu LR: 0.000068 Logit Scale: 27.813 Contrastive_loss: 0.048323 (0.094206) Loss: 0.048323 (0.094206) 2025-03-19,21:06:09 | INFO | Train Epoch: 18 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.213, 149.584/s, 149.584/s/gpu LR: 0.000068 Logit Scale: 27.826 Contrastive_loss: 0.0049890 (0.092932) Loss: 0.0049890 (0.092932) 2025-03-19,21:06:31 | INFO | Train Epoch: 18 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 149.815/s, 149.815/s/gpu LR: 0.000068 Logit Scale: 27.825 Contrastive_loss: 0.015583 (0.091842) Loss: 0.015583 (0.091842) 2025-03-19,21:06:52 | INFO | Train Epoch: 18 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 151.423/s, 151.423/s/gpu LR: 0.000068 Logit Scale: 27.839 Contrastive_loss: 0.17805 (0.093039) Loss: 0.17805 (0.093039) 2025-03-19,21:07:14 | INFO | Train Epoch: 18 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 146.432/s, 146.432/s/gpu LR: 0.000068 Logit Scale: 27.826 Contrastive_loss: 0.11727 (0.093371) Loss: 0.11727 (0.093371) 2025-03-19,21:07:35 | INFO | Train Epoch: 18 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 147.956/s, 147.956/s/gpu LR: 0.000068 Logit Scale: 27.823 Contrastive_loss: 0.0036471 (0.092159) Loss: 0.0036471 (0.092159) 2025-03-19,21:07:57 | INFO | Train Epoch: 18 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 147.393/s, 147.393/s/gpu LR: 0.000068 Logit Scale: 27.817 Contrastive_loss: 0.18674 (0.093420) Loss: 0.18674 (0.093420) 2025-03-19,21:08:18 | INFO | Train Epoch: 18 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 144.731/s, 144.731/s/gpu LR: 0.000068 Logit Scale: 27.800 Contrastive_loss: 0.13934 (0.094024) Loss: 0.13934 (0.094024) 2025-03-19,21:08:40 | INFO | Train Epoch: 18 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 150.467/s, 150.467/s/gpu LR: 0.000068 Logit Scale: 27.811 Contrastive_loss: 0.059617 (0.093577) Loss: 0.059617 (0.093577) 2025-03-19,21:09:01 | INFO | Train Epoch: 18 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 150.806/s, 150.806/s/gpu LR: 0.000068 Logit Scale: 27.819 Contrastive_loss: 0.038632 (0.092873) Loss: 0.038632 (0.092873) 2025-03-19,21:09:23 | INFO | Train Epoch: 18 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.064/s, 150.064/s/gpu LR: 0.000067 Logit Scale: 27.828 Contrastive_loss: 0.069369 (0.092575) Loss: 0.069369 (0.092575) 2025-03-19,21:09:44 | INFO | Train Epoch: 18 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 144.797/s, 144.797/s/gpu LR: 0.000067 Logit Scale: 27.846 Contrastive_loss: 0.079001 (0.092406) Loss: 0.079001 (0.092406) 2025-03-19,21:10:06 | INFO | Train Epoch: 18 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 150.088/s, 150.088/s/gpu LR: 0.000067 Logit Scale: 27.866 Contrastive_loss: 0.14298 (0.093030) Loss: 0.14298 (0.093030) 2025-03-19,21:10:28 | INFO | Train Epoch: 18 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.213, 151.608/s, 151.608/s/gpu LR: 0.000067 Logit Scale: 27.868 Contrastive_loss: 0.070192 (0.092752) Loss: 0.070192 (0.092752) 2025-03-19,21:10:49 | INFO | Train Epoch: 18 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.213, 151.423/s, 151.423/s/gpu LR: 0.000067 Logit Scale: 27.875 Contrastive_loss: 0.10870 (0.092944) Loss: 0.10870 (0.092944) 2025-03-19,21:11:10 | INFO | Train Epoch: 18 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.213, 149.922/s, 149.922/s/gpu LR: 0.000067 Logit Scale: 27.855 Contrastive_loss: 0.13781 (0.093478) Loss: 0.13781 (0.093478) 2025-03-19,21:11:32 | INFO | Train Epoch: 18 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.213, 150.508/s, 150.508/s/gpu LR: 0.000067 Logit Scale: 27.875 Contrastive_loss: 0.25399 (0.095366) Loss: 0.25399 (0.095366) 2025-03-19,21:11:53 | INFO | Train Epoch: 18 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.212, 151.698/s, 151.698/s/gpu LR: 0.000067 Logit Scale: 27.855 Contrastive_loss: 0.15676 (0.096080) Loss: 0.15676 (0.096080) 2025-03-19,21:12:14 | INFO | Train Epoch: 18 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.473/s, 148.473/s/gpu LR: 0.000067 Logit Scale: 27.843 Contrastive_loss: 0.16381 (0.096859) Loss: 0.16381 (0.096859) 2025-03-19,21:12:36 | INFO | Train Epoch: 18 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.218, 148.882/s, 148.882/s/gpu LR: 0.000067 Logit Scale: 27.848 Contrastive_loss: 0.084892 (0.096723) Loss: 0.084892 (0.096723) 2025-03-19,21:12:58 | INFO | Train Epoch: 18 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 147.447/s, 147.447/s/gpu LR: 0.000067 Logit Scale: 27.870 Contrastive_loss: 0.028264 (0.095953) Loss: 0.028264 (0.095953) 2025-03-19,21:13:19 | INFO | Train Epoch: 18 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 149.120/s, 149.120/s/gpu LR: 0.000067 Logit Scale: 27.851 Contrastive_loss: 0.14535 (0.096502) Loss: 0.14535 (0.096502) 2025-03-19,21:13:41 | INFO | Train Epoch: 18 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 149.171/s, 149.171/s/gpu LR: 0.000067 Logit Scale: 27.835 Contrastive_loss: 0.091343 (0.096446) Loss: 0.091343 (0.096446) 2025-03-19,21:14:02 | INFO | Train Epoch: 18 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.840/s, 148.840/s/gpu LR: 0.000067 Logit Scale: 27.836 Contrastive_loss: 0.065705 (0.096111) Loss: 0.065705 (0.096111) 2025-03-19,21:14:24 | INFO | Train Epoch: 18 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 150.229/s, 150.229/s/gpu LR: 0.000067 Logit Scale: 27.826 Contrastive_loss: 0.15122 (0.096704) Loss: 0.15122 (0.096704) 2025-03-19,21:14:45 | INFO | Train Epoch: 18 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.347/s, 149.347/s/gpu LR: 0.000067 Logit Scale: 27.805 Contrastive_loss: 0.045298 (0.096157) Loss: 0.045298 (0.096157) 2025-03-19,21:15:07 | INFO | Train Epoch: 18 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.306/s, 149.306/s/gpu LR: 0.000067 Logit Scale: 27.828 Contrastive_loss: 0.23403 (0.097608) Loss: 0.23403 (0.097608) 2025-03-19,21:15:28 | INFO | Train Epoch: 18 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 148.190/s, 148.190/s/gpu LR: 0.000067 Logit Scale: 27.819 Contrastive_loss: 0.061867 (0.097236) Loss: 0.061867 (0.097236) 2025-03-19,21:15:50 | INFO | Train Epoch: 18 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.217, 149.946/s, 149.946/s/gpu LR: 0.000067 Logit Scale: 27.813 Contrastive_loss: 0.064393 (0.096897) Loss: 0.064393 (0.096897) 2025-03-19,21:16:11 | INFO | Train Epoch: 18 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 150.257/s, 150.257/s/gpu LR: 0.000067 Logit Scale: 27.782 Contrastive_loss: 0.0058640 (0.095969) Loss: 0.0058640 (0.095969) 2025-03-19,21:16:33 | INFO | Train Epoch: 18 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 148.016/s, 148.016/s/gpu LR: 0.000067 Logit Scale: 27.765 Contrastive_loss: 0.018972 (0.095191) Loss: 0.018972 (0.095191) 2025-03-19,21:16:55 | INFO | Train Epoch: 18 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 148.278/s, 148.278/s/gpu LR: 0.000067 Logit Scale: 27.764 Contrastive_loss: 0.10586 (0.095298) Loss: 0.10586 (0.095298) 2025-03-19,21:17:16 | INFO | Train Epoch: 18 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 150.505/s, 150.505/s/gpu LR: 0.000067 Logit Scale: 27.744 Contrastive_loss: 0.13698 (0.095710) Loss: 0.13698 (0.095710) 2025-03-19,21:17:37 | INFO | Train Epoch: 18 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.212, 150.398/s, 150.398/s/gpu LR: 0.000067 Logit Scale: 27.733 Contrastive_loss: 0.069769 (0.095456) Loss: 0.069769 (0.095456) 2025-03-19,21:17:58 | INFO | Train Epoch: 18 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 150.186/s, 150.186/s/gpu LR: 0.000066 Logit Scale: 27.742 Contrastive_loss: 0.24723 (0.096929) Loss: 0.24723 (0.096929) 2025-03-19,21:18:20 | INFO | Train Epoch: 18 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.218, 151.679/s, 151.679/s/gpu LR: 0.000066 Logit Scale: 27.745 Contrastive_loss: 0.13260 (0.097272) Loss: 0.13260 (0.097272) 2025-03-19,21:18:42 | INFO | Train Epoch: 18 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 150.337/s, 150.337/s/gpu LR: 0.000066 Logit Scale: 27.743 Contrastive_loss: 0.091822 (0.097221) Loss: 0.091822 (0.097221) 2025-03-19,21:19:04 | INFO | Train Epoch: 18 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.221, 145.438/s, 145.438/s/gpu LR: 0.000066 Logit Scale: 27.758 Contrastive_loss: 0.10833 (0.097325) Loss: 0.10833 (0.097325) 2025-03-19,21:19:26 | INFO | Train Epoch: 18 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.222, 143.868/s, 143.868/s/gpu LR: 0.000066 Logit Scale: 27.744 Contrastive_loss: 0.13360 (0.097664) Loss: 0.13360 (0.097664) 2025-03-19,21:19:47 | INFO | Train Epoch: 18 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 148.381/s, 148.381/s/gpu LR: 0.000066 Logit Scale: 27.746 Contrastive_loss: 0.070728 (0.097415) Loss: 0.070728 (0.097415) 2025-03-19,21:20:09 | INFO | Train Epoch: 18 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 149.913/s, 149.913/s/gpu LR: 0.000066 Logit Scale: 27.727 Contrastive_loss: 0.19571 (0.098317) Loss: 0.19571 (0.098317) 2025-03-19,21:20:31 | INFO | Train Epoch: 18 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 148.650/s, 148.650/s/gpu LR: 0.000066 Logit Scale: 27.745 Contrastive_loss: 0.10856 (0.098410) Loss: 0.10856 (0.098410) 2025-03-19,21:20:52 | INFO | Train Epoch: 18 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.342/s, 149.342/s/gpu LR: 0.000066 Logit Scale: 27.752 Contrastive_loss: 0.078341 (0.098229) Loss: 0.078341 (0.098229) 2025-03-19,21:21:13 | INFO | Train Epoch: 18 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.662/s, 149.662/s/gpu LR: 0.000066 Logit Scale: 27.736 Contrastive_loss: 0.018712 (0.097519) Loss: 0.018712 (0.097519) 2025-03-19,21:21:35 | INFO | Train Epoch: 18 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 146.588/s, 146.588/s/gpu LR: 0.000066 Logit Scale: 27.744 Contrastive_loss: 0.25825 (0.098941) Loss: 0.25825 (0.098941) 2025-03-19,21:21:57 | INFO | Train Epoch: 18 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.222, 82.1820/s, 82.1820/s/gpu LR: 0.000066 Logit Scale: 27.712 Contrastive_loss: 0.12170 (0.099141) Loss: 0.12170 (0.099141) 2025-03-19,21:22:19 | INFO | Train Epoch: 18 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.218, 150.350/s, 150.350/s/gpu LR: 0.000066 Logit Scale: 27.706 Contrastive_loss: 0.038593 (0.098615) Loss: 0.038593 (0.098615) 2025-03-19,21:22:41 | INFO | Train Epoch: 18 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 149.837/s, 149.837/s/gpu LR: 0.000066 Logit Scale: 27.716 Contrastive_loss: 0.14029 (0.098974) Loss: 0.14029 (0.098974) 2025-03-19,21:23:02 | INFO | Train Epoch: 18 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 148.521/s, 148.521/s/gpu LR: 0.000066 Logit Scale: 27.703 Contrastive_loss: 0.013492 (0.098243) Loss: 0.013492 (0.098243) 2025-03-19,21:23:24 | INFO | Train Epoch: 18 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 149.785/s, 149.785/s/gpu LR: 0.000066 Logit Scale: 27.698 Contrastive_loss: 0.039844 (0.097748) Loss: 0.039844 (0.097748) 2025-03-19,21:23:45 | INFO | Train Epoch: 18 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 149.978/s, 149.978/s/gpu LR: 0.000066 Logit Scale: 27.686 Contrastive_loss: 0.080065 (0.097600) Loss: 0.080065 (0.097600) 2025-03-19,21:24:06 | INFO | Train Epoch: 18 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 151.277/s, 151.277/s/gpu LR: 0.000066 Logit Scale: 27.687 Contrastive_loss: 0.013372 (0.096898) Loss: 0.013372 (0.096898) 2025-03-19,21:24:28 | INFO | Train Epoch: 18 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 150.374/s, 150.374/s/gpu LR: 0.000066 Logit Scale: 27.716 Contrastive_loss: 0.052602 (0.096532) Loss: 0.052602 (0.096532) 2025-03-19,21:24:49 | INFO | Train Epoch: 18 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 150.135/s, 150.135/s/gpu LR: 0.000066 Logit Scale: 27.710 Contrastive_loss: 0.024545 (0.095942) Loss: 0.024545 (0.095942) 2025-03-19,21:25:11 | INFO | Train Epoch: 18 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.218, 145.632/s, 145.632/s/gpu LR: 0.000066 Logit Scale: 27.715 Contrastive_loss: 0.061802 (0.095664) Loss: 0.061802 (0.095664) 2025-03-19,21:25:33 | INFO | Train Epoch: 18 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 148.299/s, 148.299/s/gpu LR: 0.000066 Logit Scale: 27.700 Contrastive_loss: 0.16418 (0.096217) Loss: 0.16418 (0.096217) 2025-03-19,21:25:54 | INFO | Train Epoch: 18 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 149.034/s, 149.034/s/gpu LR: 0.000066 Logit Scale: 27.698 Contrastive_loss: 0.090080 (0.096168) Loss: 0.090080 (0.096168) 2025-03-19,21:26:16 | INFO | Train Epoch: 18 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.212, 150.454/s, 150.454/s/gpu LR: 0.000066 Logit Scale: 27.696 Contrastive_loss: 0.018978 (0.095555) Loss: 0.018978 (0.095555) 2025-03-19,21:26:37 | INFO | Train Epoch: 18 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.213, 148.098/s, 148.098/s/gpu LR: 0.000065 Logit Scale: 27.734 Contrastive_loss: 0.13063 (0.095831) Loss: 0.13063 (0.095831) 2025-03-19,21:26:58 | INFO | Train Epoch: 18 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 147.816/s, 147.816/s/gpu LR: 0.000065 Logit Scale: 27.710 Contrastive_loss: 0.15513 (0.096294) Loss: 0.15513 (0.096294) 2025-03-19,21:27:20 | INFO | Train Epoch: 18 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 147.991/s, 147.991/s/gpu LR: 0.000065 Logit Scale: 27.717 Contrastive_loss: 0.010060 (0.095626) Loss: 0.010060 (0.095626) 2025-03-19,21:27:42 | INFO | Train Epoch: 18 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.217, 148.723/s, 148.723/s/gpu LR: 0.000065 Logit Scale: 27.698 Contrastive_loss: 0.025574 (0.095087) Loss: 0.025574 (0.095087) 2025-03-19,21:28:03 | INFO | Train Epoch: 18 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 147.868/s, 147.868/s/gpu LR: 0.000065 Logit Scale: 27.698 Contrastive_loss: 0.063182 (0.094844) Loss: 0.063182 (0.094844) 2025-03-19,21:28:25 | INFO | Train Epoch: 18 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 148.414/s, 148.414/s/gpu LR: 0.000065 Logit Scale: 27.677 Contrastive_loss: 0.11679 (0.095010) Loss: 0.11679 (0.095010) 2025-03-19,21:28:47 | INFO | Train Epoch: 18 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 147.905/s, 147.905/s/gpu LR: 0.000065 Logit Scale: 27.660 Contrastive_loss: 0.0018155 (0.094309) Loss: 0.0018155 (0.094309) 2025-03-19,21:29:08 | INFO | Train Epoch: 18 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 149.003/s, 149.003/s/gpu LR: 0.000065 Logit Scale: 27.679 Contrastive_loss: 0.20780 (0.095156) Loss: 0.20780 (0.095156) 2025-03-19,21:29:30 | INFO | Train Epoch: 18 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 148.721/s, 148.721/s/gpu LR: 0.000065 Logit Scale: 27.711 Contrastive_loss: 0.098424 (0.095180) Loss: 0.098424 (0.095180) 2025-03-19,21:29:51 | INFO | Train Epoch: 18 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 148.723/s, 148.723/s/gpu LR: 0.000065 Logit Scale: 27.718 Contrastive_loss: 0.12456 (0.095396) Loss: 0.12456 (0.095396) 2025-03-19,21:30:13 | INFO | Train Epoch: 18 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 148.636/s, 148.636/s/gpu LR: 0.000065 Logit Scale: 27.696 Contrastive_loss: 0.069999 (0.095211) Loss: 0.069999 (0.095211) 2025-03-19,21:30:34 | INFO | Train Epoch: 18 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 149.608/s, 149.608/s/gpu LR: 0.000065 Logit Scale: 27.699 Contrastive_loss: 0.064708 (0.094990) Loss: 0.064708 (0.094990) 2025-03-19,21:30:56 | INFO | Train Epoch: 18 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.566/s, 149.566/s/gpu LR: 0.000065 Logit Scale: 27.726 Contrastive_loss: 0.075426 (0.094849) Loss: 0.075426 (0.094849) 2025-03-19,21:31:17 | INFO | Train Epoch: 18 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 149.826/s, 149.826/s/gpu LR: 0.000065 Logit Scale: 27.715 Contrastive_loss: 0.011759 (0.094256) Loss: 0.011759 (0.094256) 2025-03-19,21:31:39 | INFO | Train Epoch: 18 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 151.294/s, 151.294/s/gpu LR: 0.000065 Logit Scale: 27.730 Contrastive_loss: 0.18289 (0.094884) Loss: 0.18289 (0.094884) 2025-03-19,21:32:00 | INFO | Train Epoch: 18 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 148.459/s, 148.459/s/gpu LR: 0.000065 Logit Scale: 27.713 Contrastive_loss: 0.15053 (0.095276) Loss: 0.15053 (0.095276) 2025-03-19,21:32:22 | INFO | Train Epoch: 18 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 150.193/s, 150.193/s/gpu LR: 0.000065 Logit Scale: 27.711 Contrastive_loss: 0.026635 (0.094796) Loss: 0.026635 (0.094796) 2025-03-19,21:32:43 | INFO | Train Epoch: 18 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.213, 151.646/s, 151.646/s/gpu LR: 0.000065 Logit Scale: 27.701 Contrastive_loss: 0.015100 (0.094243) Loss: 0.015100 (0.094243) 2025-03-19,21:33:05 | INFO | Train Epoch: 18 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 149.783/s, 149.783/s/gpu LR: 0.000065 Logit Scale: 27.690 Contrastive_loss: 0.081896 (0.094157) Loss: 0.081896 (0.094157) 2025-03-19,21:33:26 | INFO | Train Epoch: 18 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 150.575/s, 150.575/s/gpu LR: 0.000065 Logit Scale: 27.704 Contrastive_loss: 0.061796 (0.093936) Loss: 0.061796 (0.093936) 2025-03-19,21:33:47 | INFO | Train Epoch: 18 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 150.631/s, 150.631/s/gpu LR: 0.000065 Logit Scale: 27.710 Contrastive_loss: 0.043456 (0.093592) Loss: 0.043456 (0.093592) 2025-03-19,21:34:09 | INFO | Train Epoch: 18 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 149.334/s, 149.334/s/gpu LR: 0.000065 Logit Scale: 27.699 Contrastive_loss: 0.064207 (0.093394) Loss: 0.064207 (0.093394) 2025-03-19,21:34:30 | INFO | Train Epoch: 18 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.218, 148.111/s, 148.111/s/gpu LR: 0.000065 Logit Scale: 27.715 Contrastive_loss: 0.079039 (0.093297) Loss: 0.079039 (0.093297) 2025-03-19,21:34:52 | INFO | Train Epoch: 18 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 149.758/s, 149.758/s/gpu LR: 0.000065 Logit Scale: 27.712 Contrastive_loss: 0.17381 (0.093834) Loss: 0.17381 (0.093834) 2025-03-19,21:35:14 | INFO | Train Epoch: 18 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.219, 135.821/s, 135.821/s/gpu LR: 0.000064 Logit Scale: 27.741 Contrastive_loss: 0.048678 (0.093535) Loss: 0.048678 (0.093535) 2025-03-19,21:35:36 | INFO | Train Epoch: 18 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.221, 144.432/s, 144.432/s/gpu LR: 0.000064 Logit Scale: 27.726 Contrastive_loss: 0.053108 (0.093269) Loss: 0.053108 (0.093269) 2025-03-19,21:35:58 | INFO | Train Epoch: 18 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.219, 146.330/s, 146.330/s/gpu LR: 0.000064 Logit Scale: 27.751 Contrastive_loss: 0.12728 (0.093491) Loss: 0.12728 (0.093491) 2025-03-19,21:36:20 | INFO | Train Epoch: 18 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 147.127/s, 147.127/s/gpu LR: 0.000064 Logit Scale: 27.751 Contrastive_loss: 0.020101 (0.093015) Loss: 0.020101 (0.093015) 2025-03-19,21:36:41 | INFO | Train Epoch: 18 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 149.590/s, 149.590/s/gpu LR: 0.000064 Logit Scale: 27.748 Contrastive_loss: 0.12228 (0.093204) Loss: 0.12228 (0.093204) 2025-03-19,21:37:03 | INFO | Train Epoch: 18 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.431/s, 149.431/s/gpu LR: 0.000064 Logit Scale: 27.740 Contrastive_loss: 0.065196 (0.093024) Loss: 0.065196 (0.093024) 2025-03-19,21:37:24 | INFO | Train Epoch: 18 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.919/s, 149.919/s/gpu LR: 0.000064 Logit Scale: 27.750 Contrastive_loss: 0.11286 (0.093151) Loss: 0.11286 (0.093151) 2025-03-19,21:37:46 | INFO | Train Epoch: 18 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.013/s, 149.013/s/gpu LR: 0.000064 Logit Scale: 27.741 Contrastive_loss: 0.0071682 (0.092606) Loss: 0.0071682 (0.092606) 2025-03-19,21:38:07 | INFO | Train Epoch: 18 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 148.295/s, 148.295/s/gpu LR: 0.000064 Logit Scale: 27.755 Contrastive_loss: 0.10173 (0.092664) Loss: 0.10173 (0.092664) 2025-03-19,21:38:29 | INFO | Train Epoch: 18 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.605/s, 149.605/s/gpu LR: 0.000064 Logit Scale: 27.760 Contrastive_loss: 0.11899 (0.092828) Loss: 0.11899 (0.092828) 2025-03-19,21:38:50 | INFO | Train Epoch: 18 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 149.757/s, 149.757/s/gpu LR: 0.000064 Logit Scale: 27.737 Contrastive_loss: 0.10414 (0.092899) Loss: 0.10414 (0.092899) 2025-03-19,21:39:12 | INFO | Train Epoch: 18 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 149.624/s, 149.624/s/gpu LR: 0.000064 Logit Scale: 27.748 Contrastive_loss: 0.17174 (0.093385) Loss: 0.17174 (0.093385) 2025-03-19,21:39:33 | INFO | Train Epoch: 18 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 150.169/s, 150.169/s/gpu LR: 0.000064 Logit Scale: 27.717 Contrastive_loss: 0.10670 (0.093467) Loss: 0.10670 (0.093467) 2025-03-19,21:39:54 | INFO | Train Epoch: 18 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 148.888/s, 148.888/s/gpu LR: 0.000064 Logit Scale: 27.701 Contrastive_loss: 0.10220 (0.093520) Loss: 0.10220 (0.093520) 2025-03-19,21:40:16 | INFO | Train Epoch: 18 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 149.657/s, 149.657/s/gpu LR: 0.000064 Logit Scale: 27.699 Contrastive_loss: 0.0023101 (0.092967) Loss: 0.0023101 (0.092967) 2025-03-19,21:40:37 | INFO | Train Epoch: 18 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 149.590/s, 149.590/s/gpu LR: 0.000064 Logit Scale: 27.681 Contrastive_loss: 0.057328 (0.092753) Loss: 0.057328 (0.092753) 2025-03-19,21:40:59 | INFO | Train Epoch: 18 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 149.434/s, 149.434/s/gpu LR: 0.000064 Logit Scale: 27.682 Contrastive_loss: 0.069641 (0.092614) Loss: 0.069641 (0.092614) 2025-03-19,21:41:21 | INFO | Train Epoch: 18 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.214, 149.078/s, 149.078/s/gpu LR: 0.000064 Logit Scale: 27.696 Contrastive_loss: 0.17269 (0.093091) Loss: 0.17269 (0.093091) 2025-03-19,21:41:42 | INFO | Train Epoch: 18 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 148.651/s, 148.651/s/gpu LR: 0.000064 Logit Scale: 27.700 Contrastive_loss: 0.17087 (0.093551) Loss: 0.17087 (0.093551) 2025-03-19,21:42:03 | INFO | Train Epoch: 18 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 149.442/s, 149.442/s/gpu LR: 0.000064 Logit Scale: 27.695 Contrastive_loss: 0.096062 (0.093566) Loss: 0.096062 (0.093566) 2025-03-19,21:42:25 | INFO | Train Epoch: 18 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 151.060/s, 151.060/s/gpu LR: 0.000064 Logit Scale: 27.704 Contrastive_loss: 0.16141 (0.093963) Loss: 0.16141 (0.093963) 2025-03-19,21:42:46 | INFO | Train Epoch: 18 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 148.088/s, 148.088/s/gpu LR: 0.000064 Logit Scale: 27.710 Contrastive_loss: 0.10840 (0.094047) Loss: 0.10840 (0.094047) 2025-03-19,21:43:08 | INFO | Train Epoch: 18 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 149.988/s, 149.988/s/gpu LR: 0.000064 Logit Scale: 27.715 Contrastive_loss: 0.015585 (0.093593) Loss: 0.015585 (0.093593) 2025-03-19,21:43:30 | INFO | Train Epoch: 18 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 149.092/s, 149.092/s/gpu LR: 0.000064 Logit Scale: 27.719 Contrastive_loss: 0.20844 (0.094253) Loss: 0.20844 (0.094253) 2025-03-19,21:43:51 | INFO | Train Epoch: 18 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 172.378/s, 172.378/s/gpu LR: 0.000064 Logit Scale: 27.712 Contrastive_loss: 0.028637 (0.093878) Loss: 0.028637 (0.093878) 2025-03-19,21:44:13 | INFO | Train Epoch: 18 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 148.641/s, 148.641/s/gpu LR: 0.000063 Logit Scale: 27.709 Contrastive_loss: 0.042193 (0.093584) Loss: 0.042193 (0.093584) 2025-03-19,21:44:34 | INFO | Train Epoch: 18 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.449/s, 149.449/s/gpu LR: 0.000063 Logit Scale: 27.722 Contrastive_loss: 0.036275 (0.093261) Loss: 0.036275 (0.093261) 2025-03-19,21:44:56 | INFO | Train Epoch: 18 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 147.448/s, 147.448/s/gpu LR: 0.000063 Logit Scale: 27.706 Contrastive_loss: 0.018488 (0.092841) Loss: 0.018488 (0.092841) 2025-03-19,21:45:17 | INFO | Train Epoch: 18 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 148.937/s, 148.937/s/gpu LR: 0.000063 Logit Scale: 27.701 Contrastive_loss: 0.13254 (0.093062) Loss: 0.13254 (0.093062) 2025-03-19,21:45:39 | INFO | Train Epoch: 18 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 148.693/s, 148.693/s/gpu LR: 0.000063 Logit Scale: 27.696 Contrastive_loss: 0.13865 (0.093316) Loss: 0.13865 (0.093316) 2025-03-19,21:46:00 | INFO | Train Epoch: 18 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 149.056/s, 149.056/s/gpu LR: 0.000063 Logit Scale: 27.689 Contrastive_loss: 0.020704 (0.092915) Loss: 0.020704 (0.092915) 2025-03-19,21:46:22 | INFO | Train Epoch: 18 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.752/s, 148.752/s/gpu LR: 0.000063 Logit Scale: 27.734 Contrastive_loss: 0.084498 (0.092868) Loss: 0.084498 (0.092868) 2025-03-19,21:46:43 | INFO | Train Epoch: 18 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 150.452/s, 150.452/s/gpu LR: 0.000063 Logit Scale: 27.757 Contrastive_loss: 0.067662 (0.092731) Loss: 0.067662 (0.092731) 2025-03-19,21:47:05 | INFO | Train Epoch: 18 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 150.453/s, 150.453/s/gpu LR: 0.000063 Logit Scale: 27.757 Contrastive_loss: 0.032410 (0.092403) Loss: 0.032410 (0.092403) 2025-03-19,21:47:26 | INFO | Train Epoch: 18 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.213, 150.347/s, 150.347/s/gpu LR: 0.000063 Logit Scale: 27.751 Contrastive_loss: 0.053169 (0.092191) Loss: 0.053169 (0.092191) 2025-03-19,21:47:48 | INFO | Train Epoch: 18 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 148.380/s, 148.380/s/gpu LR: 0.000063 Logit Scale: 27.771 Contrastive_loss: 0.043419 (0.091928) Loss: 0.043419 (0.091928) 2025-03-19,21:48:09 | INFO | Train Epoch: 18 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 147.559/s, 147.559/s/gpu LR: 0.000063 Logit Scale: 27.788 Contrastive_loss: 0.10504 (0.091999) Loss: 0.10504 (0.091999) 2025-03-19,21:48:31 | INFO | Train Epoch: 18 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 148.503/s, 148.503/s/gpu LR: 0.000063 Logit Scale: 27.791 Contrastive_loss: 0.074193 (0.091904) Loss: 0.074193 (0.091904) 2025-03-19,21:48:52 | INFO | Train Epoch: 18 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 147.941/s, 147.941/s/gpu LR: 0.000063 Logit Scale: 27.789 Contrastive_loss: 0.24232 (0.092700) Loss: 0.24232 (0.092700) 2025-03-19,21:49:14 | INFO | Train Epoch: 18 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.218, 143.869/s, 143.869/s/gpu LR: 0.000063 Logit Scale: 27.807 Contrastive_loss: 0.19988 (0.093264) Loss: 0.19988 (0.093264) 2025-03-19,21:49:36 | INFO | Train Epoch: 18 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 147.281/s, 147.281/s/gpu LR: 0.000063 Logit Scale: 27.823 Contrastive_loss: 0.036100 (0.092964) Loss: 0.036100 (0.092964) 2025-03-19,21:49:57 | INFO | Train Epoch: 18 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 149.708/s, 149.708/s/gpu LR: 0.000063 Logit Scale: 27.816 Contrastive_loss: 0.013250 (0.092549) Loss: 0.013250 (0.092549) 2025-03-19,21:50:19 | INFO | Train Epoch: 18 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.220, 147.548/s, 147.548/s/gpu LR: 0.000063 Logit Scale: 27.817 Contrastive_loss: 0.091391 (0.092543) Loss: 0.091391 (0.092543) 2025-03-19,21:50:41 | INFO | Train Epoch: 18 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.213, 152.323/s, 152.323/s/gpu LR: 0.000063 Logit Scale: 27.800 Contrastive_loss: 0.048338 (0.092315) Loss: 0.048338 (0.092315) 2025-03-19,21:51:02 | INFO | Train Epoch: 18 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 150.795/s, 150.795/s/gpu LR: 0.000063 Logit Scale: 27.792 Contrastive_loss: 0.18774 (0.092805) Loss: 0.18774 (0.092805) 2025-03-19,21:51:24 | INFO | Train Epoch: 18 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.409/s, 148.409/s/gpu LR: 0.000063 Logit Scale: 27.759 Contrastive_loss: 0.11901 (0.092938) Loss: 0.11901 (0.092938) 2025-03-19,21:51:45 | INFO | Train Epoch: 18 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 147.312/s, 147.312/s/gpu LR: 0.000063 Logit Scale: 27.759 Contrastive_loss: 0.18118 (0.093386) Loss: 0.18118 (0.093386) 2025-03-19,21:52:07 | INFO | Train Epoch: 18 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 149.445/s, 149.445/s/gpu LR: 0.000063 Logit Scale: 27.741 Contrastive_loss: 0.088625 (0.093362) Loss: 0.088625 (0.093362) 2025-03-19,21:52:28 | INFO | Train Epoch: 18 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 146.688/s, 146.688/s/gpu LR: 0.000063 Logit Scale: 27.731 Contrastive_loss: 0.097532 (0.093383) Loss: 0.097532 (0.093383) 2025-03-19,21:52:50 | INFO | Train Epoch: 18 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.217, 145.378/s, 145.378/s/gpu LR: 0.000062 Logit Scale: 27.736 Contrastive_loss: 0.025186 (0.093042) Loss: 0.025186 (0.093042) 2025-03-19,21:53:12 | INFO | Train Epoch: 18 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 144.834/s, 144.834/s/gpu LR: 0.000062 Logit Scale: 27.755 Contrastive_loss: 0.099517 (0.093074) Loss: 0.099517 (0.093074) 2025-03-19,21:53:34 | INFO | Train Epoch: 18 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.222, 146.664/s, 146.664/s/gpu LR: 0.000062 Logit Scale: 27.749 Contrastive_loss: 0.071156 (0.092966) Loss: 0.071156 (0.092966) 2025-03-19,21:53:56 | INFO | Train Epoch: 18 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 146.019/s, 146.019/s/gpu LR: 0.000062 Logit Scale: 27.735 Contrastive_loss: 0.13252 (0.093161) Loss: 0.13252 (0.093161) 2025-03-19,21:54:18 | INFO | Train Epoch: 18 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.219, 145.943/s, 145.943/s/gpu LR: 0.000062 Logit Scale: 27.736 Contrastive_loss: 0.034048 (0.092871) Loss: 0.034048 (0.092871) 2025-03-19,21:54:40 | INFO | Train Epoch: 18 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.219, 144.684/s, 144.684/s/gpu LR: 0.000062 Logit Scale: 27.704 Contrastive_loss: 0.045883 (0.092642) Loss: 0.045883 (0.092642) 2025-03-19,21:55:02 | INFO | Train Epoch: 18 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.221, 148.116/s, 148.116/s/gpu LR: 0.000062 Logit Scale: 27.681 Contrastive_loss: 0.10438 (0.092699) Loss: 0.10438 (0.092699) 2025-03-19,21:55:23 | INFO | Train Epoch: 18 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 149.373/s, 149.373/s/gpu LR: 0.000062 Logit Scale: 27.683 Contrastive_loss: 0.050373 (0.092494) Loss: 0.050373 (0.092494) 2025-03-19,21:55:45 | INFO | Train Epoch: 18 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.537/s, 148.537/s/gpu LR: 0.000062 Logit Scale: 27.695 Contrastive_loss: 0.23195 (0.093165) Loss: 0.23195 (0.093165) 2025-03-19,21:56:06 | INFO | Train Epoch: 18 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 149.706/s, 149.706/s/gpu LR: 0.000062 Logit Scale: 27.710 Contrastive_loss: 0.0093565 (0.092764) Loss: 0.0093565 (0.092764) 2025-03-19,21:56:28 | INFO | Train Epoch: 18 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 149.699/s, 149.699/s/gpu LR: 0.000062 Logit Scale: 27.738 Contrastive_loss: 0.057614 (0.092596) Loss: 0.057614 (0.092596) 2025-03-19,21:56:49 | INFO | Train Epoch: 18 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 149.729/s, 149.729/s/gpu LR: 0.000062 Logit Scale: 27.741 Contrastive_loss: 0.11621 (0.092708) Loss: 0.11621 (0.092708) 2025-03-19,21:57:11 | INFO | Train Epoch: 18 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 149.557/s, 149.557/s/gpu LR: 0.000062 Logit Scale: 27.733 Contrastive_loss: 0.051022 (0.092512) Loss: 0.051022 (0.092512) 2025-03-19,21:57:32 | INFO | Train Epoch: 18 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.999/s, 149.999/s/gpu LR: 0.000062 Logit Scale: 27.714 Contrastive_loss: 0.084873 (0.092476) Loss: 0.084873 (0.092476) 2025-03-19,21:57:53 | INFO | Train Epoch: 18 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.882/s, 149.882/s/gpu LR: 0.000062 Logit Scale: 27.720 Contrastive_loss: 0.083773 (0.092435) Loss: 0.083773 (0.092435) 2025-03-19,21:58:15 | INFO | Train Epoch: 18 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.737/s, 149.737/s/gpu LR: 0.000062 Logit Scale: 27.702 Contrastive_loss: 0.049603 (0.092236) Loss: 0.049603 (0.092236) 2025-03-19,21:58:36 | INFO | Train Epoch: 18 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.508/s, 150.508/s/gpu LR: 0.000062 Logit Scale: 27.713 Contrastive_loss: 0.077694 (0.092169) Loss: 0.077694 (0.092169) 2025-03-19,21:58:58 | INFO | Train Epoch: 18 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.213, 151.484/s, 151.484/s/gpu LR: 0.000062 Logit Scale: 27.725 Contrastive_loss: 0.082393 (0.092124) Loss: 0.082393 (0.092124) 2025-03-19,21:59:19 | INFO | Train Epoch: 18 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.213, 150.426/s, 150.426/s/gpu LR: 0.000062 Logit Scale: 27.731 Contrastive_loss: 0.14187 (0.092352) Loss: 0.14187 (0.092352) 2025-03-19,21:59:40 | INFO | Train Epoch: 18 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.314/s, 149.314/s/gpu LR: 0.000062 Logit Scale: 27.753 Contrastive_loss: 0.021644 (0.092029) Loss: 0.021644 (0.092029) 2025-03-19,22:00:02 | INFO | Train Epoch: 18 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.714/s, 149.714/s/gpu LR: 0.000062 Logit Scale: 27.748 Contrastive_loss: 0.16926 (0.092380) Loss: 0.16926 (0.092380) 2025-03-19,22:00:23 | INFO | Train Epoch: 18 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 152.086/s, 152.086/s/gpu LR: 0.000062 Logit Scale: 27.766 Contrastive_loss: 0.064923 (0.092256) Loss: 0.064923 (0.092256) 2025-03-19,22:00:45 | INFO | Train Epoch: 18 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 149.115/s, 149.115/s/gpu LR: 0.000062 Logit Scale: 27.790 Contrastive_loss: 0.015437 (0.091910) Loss: 0.015437 (0.091910) 2025-03-19,22:01:06 | INFO | Train Epoch: 18 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.941/s, 149.941/s/gpu LR: 0.000062 Logit Scale: 27.778 Contrastive_loss: 0.11411 (0.092009) Loss: 0.11411 (0.092009) 2025-03-19,22:01:28 | INFO | Train Epoch: 18 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 149.831/s, 149.831/s/gpu LR: 0.000061 Logit Scale: 27.788 Contrastive_loss: 0.20808 (0.092527) Loss: 0.20808 (0.092527) 2025-03-19,22:01:49 | INFO | Train Epoch: 18 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.754/s, 148.754/s/gpu LR: 0.000061 Logit Scale: 27.797 Contrastive_loss: 0.22616 (0.093121) Loss: 0.22616 (0.093121) 2025-03-19,22:02:11 | INFO | Train Epoch: 18 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.730/s, 148.730/s/gpu LR: 0.000061 Logit Scale: 27.792 Contrastive_loss: 0.041930 (0.092895) Loss: 0.041930 (0.092895) 2025-03-19,22:02:32 | INFO | Train Epoch: 18 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.414/s, 149.414/s/gpu LR: 0.000061 Logit Scale: 27.778 Contrastive_loss: 0.039316 (0.092659) Loss: 0.039316 (0.092659) 2025-03-19,22:02:54 | INFO | Train Epoch: 18 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 149.196/s, 149.196/s/gpu LR: 0.000061 Logit Scale: 27.760 Contrastive_loss: 0.032314 (0.092394) Loss: 0.032314 (0.092394) 2025-03-19,22:03:15 | INFO | Train Epoch: 18 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 147.577/s, 147.577/s/gpu LR: 0.000061 Logit Scale: 27.778 Contrastive_loss: 0.14569 (0.092627) Loss: 0.14569 (0.092627) 2025-03-19,22:03:37 | INFO | Train Epoch: 18 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 148.833/s, 148.833/s/gpu LR: 0.000061 Logit Scale: 27.785 Contrastive_loss: 0.012037 (0.092276) Loss: 0.012037 (0.092276) 2025-03-19,22:03:58 | INFO | Train Epoch: 18 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 148.036/s, 148.036/s/gpu LR: 0.000061 Logit Scale: 27.810 Contrastive_loss: 0.14096 (0.092487) Loss: 0.14096 (0.092487) 2025-03-19,22:04:20 | INFO | Train Epoch: 18 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 151.506/s, 151.506/s/gpu LR: 0.000061 Logit Scale: 27.806 Contrastive_loss: 0.011889 (0.092140) Loss: 0.011889 (0.092140) 2025-03-19,22:04:41 | INFO | Train Epoch: 18 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.212, 151.003/s, 151.003/s/gpu LR: 0.000061 Logit Scale: 27.825 Contrastive_loss: 0.080324 (0.092089) Loss: 0.080324 (0.092089) 2025-03-19,22:05:02 | INFO | Train Epoch: 18 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.211, 151.409/s, 151.409/s/gpu LR: 0.000061 Logit Scale: 27.784 Contrastive_loss: 0.024711 (0.091801) Loss: 0.024711 (0.091801) 2025-03-19,22:05:23 | INFO | Train Epoch: 18 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.212, 151.528/s, 151.528/s/gpu LR: 0.000061 Logit Scale: 27.793 Contrastive_loss: 0.065077 (0.091687) Loss: 0.065077 (0.091687) 2025-03-19,22:05:44 | INFO | Train Epoch: 18 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 150.058/s, 150.058/s/gpu LR: 0.000061 Logit Scale: 27.792 Contrastive_loss: 0.046282 (0.091495) Loss: 0.046282 (0.091495) 2025-03-19,22:06:06 | INFO | Train Epoch: 18 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.929/s, 149.929/s/gpu LR: 0.000061 Logit Scale: 27.769 Contrastive_loss: 0.15667 (0.091770) Loss: 0.15667 (0.091770) 2025-03-19,22:06:27 | INFO | Train Epoch: 18 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 148.885/s, 148.885/s/gpu LR: 0.000061 Logit Scale: 27.771 Contrastive_loss: 0.0093109 (0.091424) Loss: 0.0093109 (0.091424) 2025-03-19,22:06:49 | INFO | Train Epoch: 18 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.541/s, 149.541/s/gpu LR: 0.000061 Logit Scale: 27.790 Contrastive_loss: 0.022148 (0.091134) Loss: 0.022148 (0.091134) 2025-03-19,22:07:10 | INFO | Train Epoch: 18 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.215, 149.147/s, 149.147/s/gpu LR: 0.000061 Logit Scale: 27.779 Contrastive_loss: 0.080848 (0.091091) Loss: 0.080848 (0.091091) 2025-03-19,22:07:18 | INFO | Train Epoch: 18 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.217, 149.230/s, 149.230/s/gpu LR: 0.000061 Logit Scale: 27.788 Contrastive_loss: 0.12284 (0.091223) Loss: 0.12284 (0.091223) 2025-03-19,22:07:18 | INFO | Eval Epoch: 19 [32 / 7443] Clip Loss: 3.198975 2025-03-19,22:07:24 | INFO | Eval Epoch: 19 [3232 / 7443] Clip Loss: 0.817051 2025-03-19,22:07:30 | INFO | Eval Epoch: 19 [6432 / 7443] Clip Loss: 0.612014 2025-03-19,22:07:33 | INFO | Eval Epoch: 19 image_to_text_mean_rank: 77.0107 image_to_text_median_rank: 6.0000 image_to_text_R@1: 0.1593 image_to_text_R@5: 0.4822 image_to_text_R@10: 0.6597 text_to_image_mean_rank: 48.9617 text_to_image_median_rank: 6.0000 text_to_image_R@1: 0.1575 text_to_image_R@5: 0.4831 text_to_image_R@10: 0.6605 clip_val_loss: 0.5690 epoch: 19.0000 num_samples: 7443.0000 2025-03-19,22:08:06 | INFO | Start epoch 19 2025-03-19,22:08:07 | INFO | Train Epoch: 19 [ 32/766009 (0%)] Data (t): 0.250 Batch (t): 0.445, 71.8387/s, 71.8387/s/gpu LR: 0.000061 Logit Scale: 27.788 Contrastive_loss: 0.0015021 (0.0015021) Loss: 0.0015021 (0.0015021) 2025-03-19,22:08:28 | INFO | Train Epoch: 19 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.215, 146.383/s, 146.383/s/gpu LR: 0.000061 Logit Scale: 27.810 Contrastive_loss: 0.068743 (0.035123) Loss: 0.068743 (0.035123) 2025-03-19,22:08:50 | INFO | Train Epoch: 19 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.218, 149.005/s, 149.005/s/gpu LR: 0.000061 Logit Scale: 27.815 Contrastive_loss: 0.0033893 (0.024545) Loss: 0.0033893 (0.024545) 2025-03-19,22:09:12 | INFO | Train Epoch: 19 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 148.837/s, 148.837/s/gpu LR: 0.000061 Logit Scale: 27.842 Contrastive_loss: 0.16341 (0.059262) Loss: 0.16341 (0.059262) 2025-03-19,22:09:33 | INFO | Train Epoch: 19 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 149.721/s, 149.721/s/gpu LR: 0.000061 Logit Scale: 27.847 Contrastive_loss: 0.034774 (0.054364) Loss: 0.034774 (0.054364) 2025-03-19,22:09:55 | INFO | Train Epoch: 19 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 148.990/s, 148.990/s/gpu LR: 0.000061 Logit Scale: 27.862 Contrastive_loss: 0.045247 (0.052845) Loss: 0.045247 (0.052845) 2025-03-19,22:10:16 | INFO | Train Epoch: 19 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 148.199/s, 148.199/s/gpu LR: 0.000061 Logit Scale: 27.891 Contrastive_loss: 0.064129 (0.054457) Loss: 0.064129 (0.054457) 2025-03-19,22:10:38 | INFO | Train Epoch: 19 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.825/s, 149.825/s/gpu LR: 0.000061 Logit Scale: 27.919 Contrastive_loss: 0.019008 (0.050026) Loss: 0.019008 (0.050026) 2025-03-19,22:10:59 | INFO | Train Epoch: 19 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 149.629/s, 149.629/s/gpu LR: 0.000060 Logit Scale: 27.918 Contrastive_loss: 0.0097750 (0.045553) Loss: 0.0097750 (0.045553) 2025-03-19,22:11:21 | INFO | Train Epoch: 19 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 148.904/s, 148.904/s/gpu LR: 0.000060 Logit Scale: 27.943 Contrastive_loss: 0.052096 (0.046208) Loss: 0.052096 (0.046208) 2025-03-19,22:11:42 | INFO | Train Epoch: 19 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 148.396/s, 148.396/s/gpu LR: 0.000060 Logit Scale: 27.945 Contrastive_loss: 0.17638 (0.058042) Loss: 0.17638 (0.058042) 2025-03-19,22:12:04 | INFO | Train Epoch: 19 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.487/s, 147.487/s/gpu LR: 0.000060 Logit Scale: 27.959 Contrastive_loss: 0.059235 (0.058141) Loss: 0.059235 (0.058141) 2025-03-19,22:12:26 | INFO | Train Epoch: 19 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.219, 145.222/s, 145.222/s/gpu LR: 0.000060 Logit Scale: 27.983 Contrastive_loss: 0.0031095 (0.053908) Loss: 0.0031095 (0.053908) 2025-03-19,22:12:47 | INFO | Train Epoch: 19 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 146.840/s, 146.840/s/gpu LR: 0.000060 Logit Scale: 27.976 Contrastive_loss: 0.018439 (0.051374) Loss: 0.018439 (0.051374) 2025-03-19,22:13:09 | INFO | Train Epoch: 19 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 150.000/s, 150.000/s/gpu LR: 0.000060 Logit Scale: 27.997 Contrastive_loss: 0.078895 (0.053209) Loss: 0.078895 (0.053209) 2025-03-19,22:13:30 | INFO | Train Epoch: 19 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 149.519/s, 149.519/s/gpu LR: 0.000060 Logit Scale: 27.984 Contrastive_loss: 0.0016588 (0.049987) Loss: 0.0016588 (0.049987) 2025-03-19,22:13:52 | INFO | Train Epoch: 19 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 150.146/s, 150.146/s/gpu LR: 0.000060 Logit Scale: 27.991 Contrastive_loss: 0.089487 (0.052311) Loss: 0.089487 (0.052311) 2025-03-19,22:14:13 | INFO | Train Epoch: 19 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.290/s, 149.290/s/gpu LR: 0.000060 Logit Scale: 28.003 Contrastive_loss: 0.063476 (0.052931) Loss: 0.063476 (0.052931) 2025-03-19,22:14:34 | INFO | Train Epoch: 19 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 150.212/s, 150.212/s/gpu LR: 0.000060 Logit Scale: 27.977 Contrastive_loss: 0.073223 (0.053999) Loss: 0.073223 (0.053999) 2025-03-19,22:14:56 | INFO | Train Epoch: 19 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 148.869/s, 148.869/s/gpu LR: 0.000060 Logit Scale: 27.957 Contrastive_loss: 0.17509 (0.060053) Loss: 0.17509 (0.060053) 2025-03-19,22:15:18 | INFO | Train Epoch: 19 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.594/s, 149.594/s/gpu LR: 0.000060 Logit Scale: 27.960 Contrastive_loss: 0.031351 (0.058687) Loss: 0.031351 (0.058687) 2025-03-19,22:15:39 | INFO | Train Epoch: 19 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 148.763/s, 148.763/s/gpu LR: 0.000060 Logit Scale: 27.966 Contrastive_loss: 0.061526 (0.058816) Loss: 0.061526 (0.058816) 2025-03-19,22:16:00 | INFO | Train Epoch: 19 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 149.445/s, 149.445/s/gpu LR: 0.000060 Logit Scale: 27.961 Contrastive_loss: 0.20191 (0.065037) Loss: 0.20191 (0.065037) 2025-03-19,22:16:22 | INFO | Train Epoch: 19 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 149.544/s, 149.544/s/gpu LR: 0.000060 Logit Scale: 27.934 Contrastive_loss: 0.041093 (0.064040) Loss: 0.041093 (0.064040) 2025-03-19,22:16:43 | INFO | Train Epoch: 19 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 149.910/s, 149.910/s/gpu LR: 0.000060 Logit Scale: 27.935 Contrastive_loss: 0.13522 (0.066887) Loss: 0.13522 (0.066887) 2025-03-19,22:17:05 | INFO | Train Epoch: 19 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 149.752/s, 149.752/s/gpu LR: 0.000060 Logit Scale: 27.927 Contrastive_loss: 0.0064267 (0.064562) Loss: 0.0064267 (0.064562) 2025-03-19,22:17:26 | INFO | Train Epoch: 19 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.215, 149.383/s, 149.383/s/gpu LR: 0.000060 Logit Scale: 27.928 Contrastive_loss: 0.011023 (0.062579) Loss: 0.011023 (0.062579) 2025-03-19,22:17:48 | INFO | Train Epoch: 19 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 150.475/s, 150.475/s/gpu LR: 0.000060 Logit Scale: 27.926 Contrastive_loss: 0.11512 (0.064455) Loss: 0.11512 (0.064455) 2025-03-19,22:18:09 | INFO | Train Epoch: 19 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.214, 151.308/s, 151.308/s/gpu LR: 0.000060 Logit Scale: 27.928 Contrastive_loss: 0.055084 (0.064132) Loss: 0.055084 (0.064132) 2025-03-19,22:18:31 | INFO | Train Epoch: 19 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.218, 150.970/s, 150.970/s/gpu LR: 0.000060 Logit Scale: 27.888 Contrastive_loss: 0.17073 (0.067685) Loss: 0.17073 (0.067685) 2025-03-19,22:18:53 | INFO | Train Epoch: 19 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 146.631/s, 146.631/s/gpu LR: 0.000060 Logit Scale: 27.882 Contrastive_loss: 0.20629 (0.072156) Loss: 0.20629 (0.072156) 2025-03-19,22:19:14 | INFO | Train Epoch: 19 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.218, 150.260/s, 150.260/s/gpu LR: 0.000060 Logit Scale: 27.881 Contrastive_loss: 0.066856 (0.071991) Loss: 0.066856 (0.071991) 2025-03-19,22:19:36 | INFO | Train Epoch: 19 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 150.020/s, 150.020/s/gpu LR: 0.000060 Logit Scale: 27.891 Contrastive_loss: 0.086070 (0.072417) Loss: 0.086070 (0.072417) 2025-03-19,22:19:57 | INFO | Train Epoch: 19 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 149.961/s, 149.961/s/gpu LR: 0.000059 Logit Scale: 27.910 Contrastive_loss: 0.0024099 (0.070358) Loss: 0.0024099 (0.070358) 2025-03-19,22:20:19 | INFO | Train Epoch: 19 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 148.058/s, 148.058/s/gpu LR: 0.000059 Logit Scale: 27.943 Contrastive_loss: 0.0084291 (0.068589) Loss: 0.0084291 (0.068589) 2025-03-19,22:20:40 | INFO | Train Epoch: 19 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 147.158/s, 147.158/s/gpu LR: 0.000059 Logit Scale: 27.962 Contrastive_loss: 0.0027757 (0.066761) Loss: 0.0027757 (0.066761) 2025-03-19,22:21:02 | INFO | Train Epoch: 19 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 145.249/s, 145.249/s/gpu LR: 0.000059 Logit Scale: 27.943 Contrastive_loss: 0.064800 (0.066708) Loss: 0.064800 (0.066708) 2025-03-19,22:21:24 | INFO | Train Epoch: 19 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 146.043/s, 146.043/s/gpu LR: 0.000059 Logit Scale: 27.942 Contrastive_loss: 0.081021 (0.067085) Loss: 0.081021 (0.067085) 2025-03-19,22:21:46 | INFO | Train Epoch: 19 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.222, 146.057/s, 146.057/s/gpu LR: 0.000059 Logit Scale: 27.973 Contrastive_loss: 0.087062 (0.067597) Loss: 0.087062 (0.067597) 2025-03-19,22:22:08 | INFO | Train Epoch: 19 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.220, 144.395/s, 144.395/s/gpu LR: 0.000059 Logit Scale: 27.977 Contrastive_loss: 0.018675 (0.066374) Loss: 0.018675 (0.066374) 2025-03-19,22:22:30 | INFO | Train Epoch: 19 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 148.243/s, 148.243/s/gpu LR: 0.000059 Logit Scale: 27.978 Contrastive_loss: 0.032227 (0.065541) Loss: 0.032227 (0.065541) 2025-03-19,22:22:52 | INFO | Train Epoch: 19 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.219, 146.972/s, 146.972/s/gpu LR: 0.000059 Logit Scale: 27.985 Contrastive_loss: 0.060725 (0.065426) Loss: 0.060725 (0.065426) 2025-03-19,22:23:13 | INFO | Train Epoch: 19 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 147.979/s, 147.979/s/gpu LR: 0.000059 Logit Scale: 27.984 Contrastive_loss: 0.14261 (0.067221) Loss: 0.14261 (0.067221) 2025-03-19,22:23:35 | INFO | Train Epoch: 19 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 151.087/s, 151.087/s/gpu LR: 0.000059 Logit Scale: 28.008 Contrastive_loss: 0.17583 (0.069690) Loss: 0.17583 (0.069690) 2025-03-19,22:23:56 | INFO | Train Epoch: 19 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.211, 151.814/s, 151.814/s/gpu LR: 0.000059 Logit Scale: 27.993 Contrastive_loss: 0.084679 (0.070023) Loss: 0.084679 (0.070023) 2025-03-19,22:24:17 | INFO | Train Epoch: 19 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 149.004/s, 149.004/s/gpu LR: 0.000059 Logit Scale: 28.003 Contrastive_loss: 0.16376 (0.072060) Loss: 0.16376 (0.072060) 2025-03-19,22:24:39 | INFO | Train Epoch: 19 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 147.645/s, 147.645/s/gpu LR: 0.000059 Logit Scale: 28.010 Contrastive_loss: 0.15743 (0.073877) Loss: 0.15743 (0.073877) 2025-03-19,22:25:00 | INFO | Train Epoch: 19 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 148.304/s, 148.304/s/gpu LR: 0.000059 Logit Scale: 28.027 Contrastive_loss: 0.24835 (0.077512) Loss: 0.24835 (0.077512) 2025-03-19,22:25:22 | INFO | Train Epoch: 19 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.259/s, 149.259/s/gpu LR: 0.000059 Logit Scale: 27.998 Contrastive_loss: 0.10045 (0.077980) Loss: 0.10045 (0.077980) 2025-03-19,22:25:43 | INFO | Train Epoch: 19 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 148.457/s, 148.457/s/gpu LR: 0.000059 Logit Scale: 28.006 Contrastive_loss: 0.047736 (0.077375) Loss: 0.047736 (0.077375) 2025-03-19,22:26:05 | INFO | Train Epoch: 19 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 148.833/s, 148.833/s/gpu LR: 0.000059 Logit Scale: 27.994 Contrastive_loss: 0.23143 (0.080396) Loss: 0.23143 (0.080396) 2025-03-19,22:26:27 | INFO | Train Epoch: 19 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 147.515/s, 147.515/s/gpu LR: 0.000059 Logit Scale: 27.996 Contrastive_loss: 0.17213 (0.082160) Loss: 0.17213 (0.082160) 2025-03-19,22:26:48 | INFO | Train Epoch: 19 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 148.696/s, 148.696/s/gpu LR: 0.000059 Logit Scale: 27.984 Contrastive_loss: 0.11435 (0.082767) Loss: 0.11435 (0.082767) 2025-03-19,22:27:10 | INFO | Train Epoch: 19 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 151.427/s, 151.427/s/gpu LR: 0.000059 Logit Scale: 28.009 Contrastive_loss: 0.21068 (0.085136) Loss: 0.21068 (0.085136) 2025-03-19,22:27:31 | INFO | Train Epoch: 19 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 145.941/s, 145.941/s/gpu LR: 0.000059 Logit Scale: 27.987 Contrastive_loss: 0.12843 (0.085923) Loss: 0.12843 (0.085923) 2025-03-19,22:27:53 | INFO | Train Epoch: 19 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.219, 147.146/s, 147.146/s/gpu LR: 0.000059 Logit Scale: 27.972 Contrastive_loss: 0.053393 (0.085342) Loss: 0.053393 (0.085342) 2025-03-19,22:28:15 | INFO | Train Epoch: 19 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.219, 143.574/s, 143.574/s/gpu LR: 0.000059 Logit Scale: 27.949 Contrastive_loss: 0.0024675 (0.083888) Loss: 0.0024675 (0.083888) 2025-03-19,22:28:37 | INFO | Train Epoch: 19 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 149.213/s, 149.213/s/gpu LR: 0.000059 Logit Scale: 27.957 Contrastive_loss: 0.076842 (0.083767) Loss: 0.076842 (0.083767) 2025-03-19,22:28:58 | INFO | Train Epoch: 19 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 148.798/s, 148.798/s/gpu LR: 0.000058 Logit Scale: 27.993 Contrastive_loss: 0.18074 (0.085410) Loss: 0.18074 (0.085410) 2025-03-19,22:29:20 | INFO | Train Epoch: 19 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 148.930/s, 148.930/s/gpu LR: 0.000058 Logit Scale: 27.990 Contrastive_loss: 0.052343 (0.084859) Loss: 0.052343 (0.084859) 2025-03-19,22:29:41 | INFO | Train Epoch: 19 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 148.247/s, 148.247/s/gpu LR: 0.000058 Logit Scale: 27.993 Contrastive_loss: 0.0085620 (0.083608) Loss: 0.0085620 (0.083608) 2025-03-19,22:30:03 | INFO | Train Epoch: 19 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 149.665/s, 149.665/s/gpu LR: 0.000058 Logit Scale: 27.981 Contrastive_loss: 0.027247 (0.082699) Loss: 0.027247 (0.082699) 2025-03-19,22:30:24 | INFO | Train Epoch: 19 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 149.010/s, 149.010/s/gpu LR: 0.000058 Logit Scale: 28.001 Contrastive_loss: 0.064892 (0.082417) Loss: 0.064892 (0.082417) 2025-03-19,22:30:46 | INFO | Train Epoch: 19 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 143.575/s, 143.575/s/gpu LR: 0.000058 Logit Scale: 27.997 Contrastive_loss: 0.051610 (0.081935) Loss: 0.051610 (0.081935) 2025-03-19,22:31:08 | INFO | Train Epoch: 19 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.219, 146.253/s, 146.253/s/gpu LR: 0.000058 Logit Scale: 28.018 Contrastive_loss: 0.13190 (0.082704) Loss: 0.13190 (0.082704) 2025-03-19,22:31:29 | INFO | Train Epoch: 19 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 149.579/s, 149.579/s/gpu LR: 0.000058 Logit Scale: 28.015 Contrastive_loss: 0.19669 (0.084431) Loss: 0.19669 (0.084431) 2025-03-19,22:31:51 | INFO | Train Epoch: 19 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.873/s, 148.873/s/gpu LR: 0.000058 Logit Scale: 28.006 Contrastive_loss: 0.19927 (0.086145) Loss: 0.19927 (0.086145) 2025-03-19,22:32:12 | INFO | Train Epoch: 19 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 147.900/s, 147.900/s/gpu LR: 0.000058 Logit Scale: 27.997 Contrastive_loss: 0.10044 (0.086355) Loss: 0.10044 (0.086355) 2025-03-19,22:32:34 | INFO | Train Epoch: 19 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 148.608/s, 148.608/s/gpu LR: 0.000058 Logit Scale: 28.007 Contrastive_loss: 0.0080555 (0.085221) Loss: 0.0080555 (0.085221) 2025-03-19,22:32:55 | INFO | Train Epoch: 19 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 150.098/s, 150.098/s/gpu LR: 0.000058 Logit Scale: 27.979 Contrastive_loss: 0.051646 (0.084741) Loss: 0.051646 (0.084741) 2025-03-19,22:33:17 | INFO | Train Epoch: 19 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 147.780/s, 147.780/s/gpu LR: 0.000058 Logit Scale: 27.997 Contrastive_loss: 0.036657 (0.084064) Loss: 0.036657 (0.084064) 2025-03-19,22:33:39 | INFO | Train Epoch: 19 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 148.163/s, 148.163/s/gpu LR: 0.000058 Logit Scale: 27.983 Contrastive_loss: 0.0060158 (0.082980) Loss: 0.0060158 (0.082980) 2025-03-19,22:34:00 | INFO | Train Epoch: 19 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 146.893/s, 146.893/s/gpu LR: 0.000058 Logit Scale: 28.009 Contrastive_loss: 0.050520 (0.082535) Loss: 0.050520 (0.082535) 2025-03-19,22:34:22 | INFO | Train Epoch: 19 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 151.270/s, 151.270/s/gpu LR: 0.000058 Logit Scale: 28.019 Contrastive_loss: 0.13623 (0.083261) Loss: 0.13623 (0.083261) 2025-03-19,22:34:44 | INFO | Train Epoch: 19 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 148.957/s, 148.957/s/gpu LR: 0.000058 Logit Scale: 28.006 Contrastive_loss: 0.061547 (0.082971) Loss: 0.061547 (0.082971) 2025-03-19,22:35:05 | INFO | Train Epoch: 19 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.962/s, 148.962/s/gpu LR: 0.000058 Logit Scale: 27.968 Contrastive_loss: 0.028403 (0.082253) Loss: 0.028403 (0.082253) 2025-03-19,22:35:27 | INFO | Train Epoch: 19 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 148.869/s, 148.869/s/gpu LR: 0.000058 Logit Scale: 27.968 Contrastive_loss: 0.18960 (0.083647) Loss: 0.18960 (0.083647) 2025-03-19,22:35:48 | INFO | Train Epoch: 19 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.213, 150.271/s, 150.271/s/gpu LR: 0.000058 Logit Scale: 27.977 Contrastive_loss: 0.10577 (0.083931) Loss: 0.10577 (0.083931) 2025-03-19,22:36:10 | INFO | Train Epoch: 19 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 150.646/s, 150.646/s/gpu LR: 0.000058 Logit Scale: 27.987 Contrastive_loss: 0.019641 (0.083117) Loss: 0.019641 (0.083117) 2025-03-19,22:36:31 | INFO | Train Epoch: 19 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.212, 151.564/s, 151.564/s/gpu LR: 0.000058 Logit Scale: 28.005 Contrastive_loss: 0.037294 (0.082544) Loss: 0.037294 (0.082544) 2025-03-19,22:36:52 | INFO | Train Epoch: 19 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 151.333/s, 151.333/s/gpu LR: 0.000058 Logit Scale: 28.008 Contrastive_loss: 0.084999 (0.082575) Loss: 0.084999 (0.082575) 2025-03-19,22:37:13 | INFO | Train Epoch: 19 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.212, 148.244/s, 148.244/s/gpu LR: 0.000058 Logit Scale: 27.993 Contrastive_loss: 0.0091274 (0.081679) Loss: 0.0091274 (0.081679) 2025-03-19,22:37:35 | INFO | Train Epoch: 19 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.218, 148.322/s, 148.322/s/gpu LR: 0.000057 Logit Scale: 27.993 Contrastive_loss: 0.041401 (0.081194) Loss: 0.041401 (0.081194) 2025-03-19,22:37:57 | INFO | Train Epoch: 19 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.217, 148.274/s, 148.274/s/gpu LR: 0.000057 Logit Scale: 27.984 Contrastive_loss: 0.0097064 (0.080343) Loss: 0.0097064 (0.080343) 2025-03-19,22:38:18 | INFO | Train Epoch: 19 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 148.219/s, 148.219/s/gpu LR: 0.000057 Logit Scale: 27.993 Contrastive_loss: 0.018240 (0.079612) Loss: 0.018240 (0.079612) 2025-03-19,22:38:40 | INFO | Train Epoch: 19 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.573/s, 149.573/s/gpu LR: 0.000057 Logit Scale: 28.005 Contrastive_loss: 0.039868 (0.079150) Loss: 0.039868 (0.079150) 2025-03-19,22:39:01 | INFO | Train Epoch: 19 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.546/s, 149.546/s/gpu LR: 0.000057 Logit Scale: 28.023 Contrastive_loss: 0.087850 (0.079250) Loss: 0.087850 (0.079250) 2025-03-19,22:39:23 | INFO | Train Epoch: 19 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.213, 151.714/s, 151.714/s/gpu LR: 0.000057 Logit Scale: 28.016 Contrastive_loss: 0.13664 (0.079902) Loss: 0.13664 (0.079902) 2025-03-19,22:39:44 | INFO | Train Epoch: 19 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.212, 151.312/s, 151.312/s/gpu LR: 0.000057 Logit Scale: 28.034 Contrastive_loss: 0.0089660 (0.079105) Loss: 0.0089660 (0.079105) 2025-03-19,22:40:05 | INFO | Train Epoch: 19 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.212, 148.347/s, 148.347/s/gpu LR: 0.000057 Logit Scale: 28.063 Contrastive_loss: 0.17779 (0.080201) Loss: 0.17779 (0.080201) 2025-03-19,22:40:27 | INFO | Train Epoch: 19 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 150.965/s, 150.965/s/gpu LR: 0.000057 Logit Scale: 28.056 Contrastive_loss: 0.14282 (0.080890) Loss: 0.14282 (0.080890) 2025-03-19,22:40:48 | INFO | Train Epoch: 19 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.794/s, 148.794/s/gpu LR: 0.000057 Logit Scale: 28.033 Contrastive_loss: 0.16337 (0.081786) Loss: 0.16337 (0.081786) 2025-03-19,22:41:10 | INFO | Train Epoch: 19 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.736/s, 148.736/s/gpu LR: 0.000057 Logit Scale: 28.033 Contrastive_loss: 0.048363 (0.081427) Loss: 0.048363 (0.081427) 2025-03-19,22:41:31 | INFO | Train Epoch: 19 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 150.361/s, 150.361/s/gpu LR: 0.000057 Logit Scale: 28.051 Contrastive_loss: 0.060389 (0.081203) Loss: 0.060389 (0.081203) 2025-03-19,22:41:52 | INFO | Train Epoch: 19 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.213, 149.269/s, 149.269/s/gpu LR: 0.000057 Logit Scale: 28.050 Contrastive_loss: 0.17521 (0.082192) Loss: 0.17521 (0.082192) 2025-03-19,22:42:14 | INFO | Train Epoch: 19 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 150.112/s, 150.112/s/gpu LR: 0.000057 Logit Scale: 28.034 Contrastive_loss: 0.033382 (0.081684) Loss: 0.033382 (0.081684) 2025-03-19,22:42:35 | INFO | Train Epoch: 19 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 147.583/s, 147.583/s/gpu LR: 0.000057 Logit Scale: 28.007 Contrastive_loss: 0.16173 (0.082509) Loss: 0.16173 (0.082509) 2025-03-19,22:42:57 | INFO | Train Epoch: 19 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 147.684/s, 147.684/s/gpu LR: 0.000057 Logit Scale: 28.025 Contrastive_loss: 0.066591 (0.082347) Loss: 0.066591 (0.082347) 2025-03-19,22:43:19 | INFO | Train Epoch: 19 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 148.530/s, 148.530/s/gpu LR: 0.000057 Logit Scale: 28.026 Contrastive_loss: 0.080694 (0.082330) Loss: 0.080694 (0.082330) 2025-03-19,22:43:40 | INFO | Train Epoch: 19 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 149.697/s, 149.697/s/gpu LR: 0.000057 Logit Scale: 28.027 Contrastive_loss: 0.045506 (0.081962) Loss: 0.045506 (0.081962) 2025-03-19,22:44:02 | INFO | Train Epoch: 19 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 146.330/s, 146.330/s/gpu LR: 0.000057 Logit Scale: 28.032 Contrastive_loss: 0.011800 (0.081267) Loss: 0.011800 (0.081267) 2025-03-19,22:44:23 | INFO | Train Epoch: 19 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.862/s, 149.862/s/gpu LR: 0.000057 Logit Scale: 28.008 Contrastive_loss: 0.16872 (0.082125) Loss: 0.16872 (0.082125) 2025-03-19,22:44:45 | INFO | Train Epoch: 19 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 148.008/s, 148.008/s/gpu LR: 0.000057 Logit Scale: 28.000 Contrastive_loss: 0.0055200 (0.081381) Loss: 0.0055200 (0.081381) 2025-03-19,22:45:06 | INFO | Train Epoch: 19 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 148.580/s, 148.580/s/gpu LR: 0.000057 Logit Scale: 28.009 Contrastive_loss: 0.33569 (0.083826) Loss: 0.33569 (0.083826) 2025-03-19,22:45:28 | INFO | Train Epoch: 19 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 149.331/s, 149.331/s/gpu LR: 0.000057 Logit Scale: 27.996 Contrastive_loss: 0.041538 (0.083423) Loss: 0.041538 (0.083423) 2025-03-19,22:45:49 | INFO | Train Epoch: 19 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 148.224/s, 148.224/s/gpu LR: 0.000057 Logit Scale: 28.001 Contrastive_loss: 0.10119 (0.083591) Loss: 0.10119 (0.083591) 2025-03-19,22:46:11 | INFO | Train Epoch: 19 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 146.936/s, 146.936/s/gpu LR: 0.000057 Logit Scale: 28.017 Contrastive_loss: 0.037999 (0.083165) Loss: 0.037999 (0.083165) 2025-03-19,22:46:32 | INFO | Train Epoch: 19 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 149.138/s, 149.138/s/gpu LR: 0.000056 Logit Scale: 27.996 Contrastive_loss: 0.064404 (0.082991) Loss: 0.064404 (0.082991) 2025-03-19,22:46:54 | INFO | Train Epoch: 19 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 150.469/s, 150.469/s/gpu LR: 0.000056 Logit Scale: 28.005 Contrastive_loss: 0.091990 (0.083074) Loss: 0.091990 (0.083074) 2025-03-19,22:47:15 | INFO | Train Epoch: 19 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.213, 150.203/s, 150.203/s/gpu LR: 0.000056 Logit Scale: 28.017 Contrastive_loss: 0.068223 (0.082939) Loss: 0.068223 (0.082939) 2025-03-19,22:47:37 | INFO | Train Epoch: 19 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 147.839/s, 147.839/s/gpu LR: 0.000056 Logit Scale: 28.017 Contrastive_loss: 0.015704 (0.082333) Loss: 0.015704 (0.082333) 2025-03-19,22:47:58 | INFO | Train Epoch: 19 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 150.494/s, 150.494/s/gpu LR: 0.000056 Logit Scale: 28.016 Contrastive_loss: 0.13365 (0.082791) Loss: 0.13365 (0.082791) 2025-03-19,22:48:20 | INFO | Train Epoch: 19 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 147.761/s, 147.761/s/gpu LR: 0.000056 Logit Scale: 28.034 Contrastive_loss: 0.0017179 (0.082074) Loss: 0.0017179 (0.082074) 2025-03-19,22:48:42 | INFO | Train Epoch: 19 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 146.927/s, 146.927/s/gpu LR: 0.000056 Logit Scale: 28.049 Contrastive_loss: 0.00089167 (0.081362) Loss: 0.00089167 (0.081362) 2025-03-19,22:49:04 | INFO | Train Epoch: 19 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.220, 147.193/s, 147.193/s/gpu LR: 0.000056 Logit Scale: 28.067 Contrastive_loss: 0.16139 (0.082058) Loss: 0.16139 (0.082058) 2025-03-19,22:49:26 | INFO | Train Epoch: 19 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.219, 146.465/s, 146.465/s/gpu LR: 0.000056 Logit Scale: 28.069 Contrastive_loss: 0.026537 (0.081579) Loss: 0.026537 (0.081579) 2025-03-19,22:49:48 | INFO | Train Epoch: 19 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.222, 146.146/s, 146.146/s/gpu LR: 0.000056 Logit Scale: 28.072 Contrastive_loss: 0.16968 (0.082332) Loss: 0.16968 (0.082332) 2025-03-19,22:50:10 | INFO | Train Epoch: 19 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.222, 147.410/s, 147.410/s/gpu LR: 0.000056 Logit Scale: 28.056 Contrastive_loss: 0.059608 (0.082139) Loss: 0.059608 (0.082139) 2025-03-19,22:50:32 | INFO | Train Epoch: 19 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 150.380/s, 150.380/s/gpu LR: 0.000056 Logit Scale: 28.055 Contrastive_loss: 0.011454 (0.081545) Loss: 0.011454 (0.081545) 2025-03-19,22:50:53 | INFO | Train Epoch: 19 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 147.942/s, 147.942/s/gpu LR: 0.000056 Logit Scale: 28.044 Contrastive_loss: 0.068731 (0.081439) Loss: 0.068731 (0.081439) 2025-03-19,22:51:15 | INFO | Train Epoch: 19 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.217, 148.351/s, 148.351/s/gpu LR: 0.000056 Logit Scale: 28.062 Contrastive_loss: 0.018210 (0.080916) Loss: 0.018210 (0.080916) 2025-03-19,22:51:36 | INFO | Train Epoch: 19 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.030/s, 149.030/s/gpu LR: 0.000056 Logit Scale: 28.059 Contrastive_loss: 0.073431 (0.080855) Loss: 0.073431 (0.080855) 2025-03-19,22:51:58 | INFO | Train Epoch: 19 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.707/s, 149.707/s/gpu LR: 0.000056 Logit Scale: 28.032 Contrastive_loss: 0.041634 (0.080536) Loss: 0.041634 (0.080536) 2025-03-19,22:52:19 | INFO | Train Epoch: 19 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 148.904/s, 148.904/s/gpu LR: 0.000056 Logit Scale: 28.012 Contrastive_loss: 0.056751 (0.080344) Loss: 0.056751 (0.080344) 2025-03-19,22:52:41 | INFO | Train Epoch: 19 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 149.226/s, 149.226/s/gpu LR: 0.000056 Logit Scale: 27.984 Contrastive_loss: 0.0040912 (0.079734) Loss: 0.0040912 (0.079734) 2025-03-19,22:53:02 | INFO | Train Epoch: 19 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 149.629/s, 149.629/s/gpu LR: 0.000056 Logit Scale: 27.978 Contrastive_loss: 0.030862 (0.079346) Loss: 0.030862 (0.079346) 2025-03-19,22:53:24 | INFO | Train Epoch: 19 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 147.421/s, 147.421/s/gpu LR: 0.000056 Logit Scale: 28.001 Contrastive_loss: 0.21969 (0.080451) Loss: 0.21969 (0.080451) 2025-03-19,22:53:46 | INFO | Train Epoch: 19 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.220, 147.089/s, 147.089/s/gpu LR: 0.000056 Logit Scale: 27.995 Contrastive_loss: 0.0032106 (0.079848) Loss: 0.0032106 (0.079848) 2025-03-19,22:54:08 | INFO | Train Epoch: 19 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.220, 146.676/s, 146.676/s/gpu LR: 0.000056 Logit Scale: 27.979 Contrastive_loss: 0.086634 (0.079900) Loss: 0.086634 (0.079900) 2025-03-19,22:54:30 | INFO | Train Epoch: 19 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.219, 151.513/s, 151.513/s/gpu LR: 0.000056 Logit Scale: 27.993 Contrastive_loss: 0.15199 (0.080455) Loss: 0.15199 (0.080455) 2025-03-19,22:54:51 | INFO | Train Epoch: 19 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.213, 149.715/s, 149.715/s/gpu LR: 0.000056 Logit Scale: 27.999 Contrastive_loss: 0.14305 (0.080933) Loss: 0.14305 (0.080933) 2025-03-19,22:55:12 | INFO | Train Epoch: 19 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.213, 151.148/s, 151.148/s/gpu LR: 0.000056 Logit Scale: 28.004 Contrastive_loss: 0.00048258 (0.080323) Loss: 0.00048258 (0.080323) 2025-03-19,22:55:34 | INFO | Train Epoch: 19 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.213, 145.514/s, 145.514/s/gpu LR: 0.000055 Logit Scale: 28.019 Contrastive_loss: 0.061419 (0.080181) Loss: 0.061419 (0.080181) 2025-03-19,22:55:56 | INFO | Train Epoch: 19 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.219, 145.416/s, 145.416/s/gpu LR: 0.000055 Logit Scale: 28.034 Contrastive_loss: 0.29037 (0.081750) Loss: 0.29037 (0.081750) 2025-03-19,22:56:18 | INFO | Train Epoch: 19 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.221, 144.765/s, 144.765/s/gpu LR: 0.000055 Logit Scale: 28.033 Contrastive_loss: 0.21801 (0.082759) Loss: 0.21801 (0.082759) 2025-03-19,22:56:40 | INFO | Train Epoch: 19 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.220, 140.420/s, 140.420/s/gpu LR: 0.000055 Logit Scale: 28.028 Contrastive_loss: 0.024849 (0.082333) Loss: 0.024849 (0.082333) 2025-03-19,22:57:02 | INFO | Train Epoch: 19 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.221, 145.708/s, 145.708/s/gpu LR: 0.000055 Logit Scale: 28.040 Contrastive_loss: 0.027697 (0.081934) Loss: 0.027697 (0.081934) 2025-03-19,22:57:24 | INFO | Train Epoch: 19 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.220, 147.612/s, 147.612/s/gpu LR: 0.000055 Logit Scale: 28.034 Contrastive_loss: 0.018587 (0.081475) Loss: 0.018587 (0.081475) 2025-03-19,22:57:46 | INFO | Train Epoch: 19 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 148.906/s, 148.906/s/gpu LR: 0.000055 Logit Scale: 28.018 Contrastive_loss: 0.14125 (0.081906) Loss: 0.14125 (0.081906) 2025-03-19,22:58:07 | INFO | Train Epoch: 19 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.218, 148.345/s, 148.345/s/gpu LR: 0.000055 Logit Scale: 28.016 Contrastive_loss: 0.0062190 (0.081365) Loss: 0.0062190 (0.081365) 2025-03-19,22:58:29 | INFO | Train Epoch: 19 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 149.588/s, 149.588/s/gpu LR: 0.000055 Logit Scale: 28.034 Contrastive_loss: 0.011205 (0.080867) Loss: 0.011205 (0.080867) 2025-03-19,22:58:50 | INFO | Train Epoch: 19 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.283/s, 149.283/s/gpu LR: 0.000055 Logit Scale: 28.037 Contrastive_loss: 0.12686 (0.081191) Loss: 0.12686 (0.081191) 2025-03-19,22:59:12 | INFO | Train Epoch: 19 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 149.398/s, 149.398/s/gpu LR: 0.000055 Logit Scale: 28.053 Contrastive_loss: 0.060814 (0.081049) Loss: 0.060814 (0.081049) 2025-03-19,22:59:34 | INFO | Train Epoch: 19 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 148.384/s, 148.384/s/gpu LR: 0.000055 Logit Scale: 28.033 Contrastive_loss: 0.32741 (0.082760) Loss: 0.32741 (0.082760) 2025-03-19,22:59:55 | INFO | Train Epoch: 19 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.627/s, 149.627/s/gpu LR: 0.000055 Logit Scale: 28.051 Contrastive_loss: 0.029917 (0.082395) Loss: 0.029917 (0.082395) 2025-03-19,23:00:17 | INFO | Train Epoch: 19 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 148.899/s, 148.899/s/gpu LR: 0.000055 Logit Scale: 28.054 Contrastive_loss: 0.043479 (0.082129) Loss: 0.043479 (0.082129) 2025-03-19,23:00:38 | INFO | Train Epoch: 19 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.133/s, 149.133/s/gpu LR: 0.000055 Logit Scale: 28.044 Contrastive_loss: 0.18503 (0.082829) Loss: 0.18503 (0.082829) 2025-03-19,23:01:00 | INFO | Train Epoch: 19 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.353/s, 148.353/s/gpu LR: 0.000055 Logit Scale: 28.045 Contrastive_loss: 0.044975 (0.082573) Loss: 0.044975 (0.082573) 2025-03-19,23:01:21 | INFO | Train Epoch: 19 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.217, 148.087/s, 148.087/s/gpu LR: 0.000055 Logit Scale: 28.045 Contrastive_loss: 0.12946 (0.082887) Loss: 0.12946 (0.082887) 2025-03-19,23:01:43 | INFO | Train Epoch: 19 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 148.752/s, 148.752/s/gpu LR: 0.000055 Logit Scale: 28.044 Contrastive_loss: 0.25420 (0.084030) Loss: 0.25420 (0.084030) 2025-03-19,23:02:05 | INFO | Train Epoch: 19 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.536/s, 148.536/s/gpu LR: 0.000055 Logit Scale: 28.050 Contrastive_loss: 0.068680 (0.083928) Loss: 0.068680 (0.083928) 2025-03-19,23:02:26 | INFO | Train Epoch: 19 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 143.231/s, 143.231/s/gpu LR: 0.000055 Logit Scale: 28.034 Contrastive_loss: 0.13962 (0.084294) Loss: 0.13962 (0.084294) 2025-03-19,23:02:48 | INFO | Train Epoch: 19 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 150.578/s, 150.578/s/gpu LR: 0.000055 Logit Scale: 28.064 Contrastive_loss: 0.15374 (0.084748) Loss: 0.15374 (0.084748) 2025-03-19,23:03:09 | INFO | Train Epoch: 19 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 149.650/s, 149.650/s/gpu LR: 0.000055 Logit Scale: 28.060 Contrastive_loss: 0.24139 (0.085765) Loss: 0.24139 (0.085765) 2025-03-19,23:03:30 | INFO | Train Epoch: 19 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 149.493/s, 149.493/s/gpu LR: 0.000055 Logit Scale: 28.051 Contrastive_loss: 0.057149 (0.085581) Loss: 0.057149 (0.085581) 2025-03-19,23:03:52 | INFO | Train Epoch: 19 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 146.830/s, 146.830/s/gpu LR: 0.000055 Logit Scale: 28.047 Contrastive_loss: 0.0056237 (0.085068) Loss: 0.0056237 (0.085068) 2025-03-19,23:04:14 | INFO | Train Epoch: 19 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.221, 148.266/s, 148.266/s/gpu LR: 0.000055 Logit Scale: 28.027 Contrastive_loss: 0.13497 (0.085386) Loss: 0.13497 (0.085386) 2025-03-19,23:04:36 | INFO | Train Epoch: 19 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.955/s, 149.955/s/gpu LR: 0.000055 Logit Scale: 28.019 Contrastive_loss: 0.10562 (0.085514) Loss: 0.10562 (0.085514) 2025-03-19,23:04:57 | INFO | Train Epoch: 19 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.213, 151.132/s, 151.132/s/gpu LR: 0.000054 Logit Scale: 28.028 Contrastive_loss: 0.038539 (0.085219) Loss: 0.038539 (0.085219) 2025-03-19,23:05:18 | INFO | Train Epoch: 19 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.129/s, 150.129/s/gpu LR: 0.000054 Logit Scale: 28.039 Contrastive_loss: 0.10676 (0.085353) Loss: 0.10676 (0.085353) 2025-03-19,23:05:40 | INFO | Train Epoch: 19 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 150.494/s, 150.494/s/gpu LR: 0.000054 Logit Scale: 28.047 Contrastive_loss: 0.011443 (0.084894) Loss: 0.011443 (0.084894) 2025-03-19,23:06:01 | INFO | Train Epoch: 19 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 146.652/s, 146.652/s/gpu LR: 0.000054 Logit Scale: 28.039 Contrastive_loss: 0.093961 (0.084950) Loss: 0.093961 (0.084950) 2025-03-19,23:06:23 | INFO | Train Epoch: 19 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 146.017/s, 146.017/s/gpu LR: 0.000054 Logit Scale: 28.022 Contrastive_loss: 0.046686 (0.084716) Loss: 0.046686 (0.084716) 2025-03-19,23:06:45 | INFO | Train Epoch: 19 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.219, 146.980/s, 146.980/s/gpu LR: 0.000054 Logit Scale: 28.006 Contrastive_loss: 0.0094671 (0.084257) Loss: 0.0094671 (0.084257) 2025-03-19,23:07:07 | INFO | Train Epoch: 19 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 146.297/s, 146.297/s/gpu LR: 0.000054 Logit Scale: 28.013 Contrastive_loss: 0.0011411 (0.083753) Loss: 0.0011411 (0.083753) 2025-03-19,23:07:29 | INFO | Train Epoch: 19 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 143.903/s, 143.903/s/gpu LR: 0.000054 Logit Scale: 28.009 Contrastive_loss: 0.030911 (0.083435) Loss: 0.030911 (0.083435) 2025-03-19,23:07:51 | INFO | Train Epoch: 19 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 145.672/s, 145.672/s/gpu LR: 0.000054 Logit Scale: 28.011 Contrastive_loss: 0.12278 (0.083670) Loss: 0.12278 (0.083670) 2025-03-19,23:08:13 | INFO | Train Epoch: 19 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.219, 147.623/s, 147.623/s/gpu LR: 0.000054 Logit Scale: 28.044 Contrastive_loss: 0.10504 (0.083798) Loss: 0.10504 (0.083798) 2025-03-19,23:08:35 | INFO | Train Epoch: 19 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.219, 145.281/s, 145.281/s/gpu LR: 0.000054 Logit Scale: 28.056 Contrastive_loss: 0.060924 (0.083662) Loss: 0.060924 (0.083662) 2025-03-19,23:08:57 | INFO | Train Epoch: 19 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 145.134/s, 145.134/s/gpu LR: 0.000054 Logit Scale: 28.067 Contrastive_loss: 0.17985 (0.084228) Loss: 0.17985 (0.084228) 2025-03-19,23:09:19 | INFO | Train Epoch: 19 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 147.156/s, 147.156/s/gpu LR: 0.000054 Logit Scale: 28.032 Contrastive_loss: 0.14935 (0.084609) Loss: 0.14935 (0.084609) 2025-03-19,23:09:41 | INFO | Train Epoch: 19 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 146.326/s, 146.326/s/gpu LR: 0.000054 Logit Scale: 28.034 Contrastive_loss: 0.073279 (0.084543) Loss: 0.073279 (0.084543) 2025-03-19,23:10:02 | INFO | Train Epoch: 19 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 150.370/s, 150.370/s/gpu LR: 0.000054 Logit Scale: 28.043 Contrastive_loss: 0.074914 (0.084487) Loss: 0.074914 (0.084487) 2025-03-19,23:10:23 | INFO | Train Epoch: 19 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.213, 144.020/s, 144.020/s/gpu LR: 0.000054 Logit Scale: 28.050 Contrastive_loss: 0.0075939 (0.084045) Loss: 0.0075939 (0.084045) 2025-03-19,23:10:45 | INFO | Train Epoch: 19 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 147.812/s, 147.812/s/gpu LR: 0.000054 Logit Scale: 28.025 Contrastive_loss: 0.21955 (0.084820) Loss: 0.21955 (0.084820) 2025-03-19,23:11:07 | INFO | Train Epoch: 19 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 145.243/s, 145.243/s/gpu LR: 0.000054 Logit Scale: 28.030 Contrastive_loss: 0.082999 (0.084809) Loss: 0.082999 (0.084809) 2025-03-19,23:11:29 | INFO | Train Epoch: 19 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.221, 145.253/s, 145.253/s/gpu LR: 0.000054 Logit Scale: 28.052 Contrastive_loss: 0.17045 (0.085293) Loss: 0.17045 (0.085293) 2025-03-19,23:11:51 | INFO | Train Epoch: 19 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 151.267/s, 151.267/s/gpu LR: 0.000054 Logit Scale: 28.062 Contrastive_loss: 0.10806 (0.085421) Loss: 0.10806 (0.085421) 2025-03-19,23:12:12 | INFO | Train Epoch: 19 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 151.133/s, 151.133/s/gpu LR: 0.000054 Logit Scale: 28.038 Contrastive_loss: 0.15445 (0.085807) Loss: 0.15445 (0.085807) 2025-03-19,23:12:34 | INFO | Train Epoch: 19 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.218, 144.384/s, 144.384/s/gpu LR: 0.000054 Logit Scale: 28.028 Contrastive_loss: 0.054685 (0.085634) Loss: 0.054685 (0.085634) 2025-03-19,23:12:56 | INFO | Train Epoch: 19 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.221, 145.683/s, 145.683/s/gpu LR: 0.000054 Logit Scale: 28.034 Contrastive_loss: 0.27354 (0.086672) Loss: 0.27354 (0.086672) 2025-03-19,23:13:18 | INFO | Train Epoch: 19 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.221, 145.171/s, 145.171/s/gpu LR: 0.000054 Logit Scale: 28.031 Contrastive_loss: 0.071236 (0.086587) Loss: 0.071236 (0.086587) 2025-03-19,23:13:40 | INFO | Train Epoch: 19 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.053/s, 148.053/s/gpu LR: 0.000054 Logit Scale: 28.023 Contrastive_loss: 0.063465 (0.086461) Loss: 0.063465 (0.086461) 2025-03-19,23:14:02 | INFO | Train Epoch: 19 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.217, 146.455/s, 146.455/s/gpu LR: 0.000053 Logit Scale: 28.035 Contrastive_loss: 0.032494 (0.086168) Loss: 0.032494 (0.086168) 2025-03-19,23:14:23 | INFO | Train Epoch: 19 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.644/s, 149.644/s/gpu LR: 0.000053 Logit Scale: 28.034 Contrastive_loss: 0.091830 (0.086198) Loss: 0.091830 (0.086198) 2025-03-19,23:14:45 | INFO | Train Epoch: 19 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.289/s, 149.289/s/gpu LR: 0.000053 Logit Scale: 28.026 Contrastive_loss: 0.072662 (0.086125) Loss: 0.072662 (0.086125) 2025-03-19,23:15:07 | INFO | Train Epoch: 19 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.217, 149.255/s, 149.255/s/gpu LR: 0.000053 Logit Scale: 28.049 Contrastive_loss: 0.072722 (0.086054) Loss: 0.072722 (0.086054) 2025-03-19,23:15:28 | INFO | Train Epoch: 19 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 151.146/s, 151.146/s/gpu LR: 0.000053 Logit Scale: 28.039 Contrastive_loss: 0.056154 (0.085895) Loss: 0.056154 (0.085895) 2025-03-19,23:15:49 | INFO | Train Epoch: 19 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 150.850/s, 150.850/s/gpu LR: 0.000053 Logit Scale: 28.047 Contrastive_loss: 0.085579 (0.085893) Loss: 0.085579 (0.085893) 2025-03-19,23:16:11 | INFO | Train Epoch: 19 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 148.540/s, 148.540/s/gpu LR: 0.000053 Logit Scale: 28.059 Contrastive_loss: 0.13312 (0.086142) Loss: 0.13312 (0.086142) 2025-03-19,23:16:32 | INFO | Train Epoch: 19 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 149.356/s, 149.356/s/gpu LR: 0.000053 Logit Scale: 28.038 Contrastive_loss: 0.031797 (0.085857) Loss: 0.031797 (0.085857) 2025-03-19,23:16:54 | INFO | Train Epoch: 19 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 148.018/s, 148.018/s/gpu LR: 0.000053 Logit Scale: 28.043 Contrastive_loss: 0.32019 (0.087077) Loss: 0.32019 (0.087077) 2025-03-19,23:17:15 | INFO | Train Epoch: 19 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 147.863/s, 147.863/s/gpu LR: 0.000053 Logit Scale: 28.054 Contrastive_loss: 0.014564 (0.086702) Loss: 0.014564 (0.086702) 2025-03-19,23:17:37 | INFO | Train Epoch: 19 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 148.490/s, 148.490/s/gpu LR: 0.000053 Logit Scale: 28.072 Contrastive_loss: 0.025398 (0.086386) Loss: 0.025398 (0.086386) 2025-03-19,23:17:58 | INFO | Train Epoch: 19 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 149.003/s, 149.003/s/gpu LR: 0.000053 Logit Scale: 28.089 Contrastive_loss: 0.022728 (0.086059) Loss: 0.022728 (0.086059) 2025-03-19,23:18:20 | INFO | Train Epoch: 19 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 149.667/s, 149.667/s/gpu LR: 0.000053 Logit Scale: 28.071 Contrastive_loss: 0.10652 (0.086164) Loss: 0.10652 (0.086164) 2025-03-19,23:18:42 | INFO | Train Epoch: 19 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 149.565/s, 149.565/s/gpu LR: 0.000053 Logit Scale: 28.092 Contrastive_loss: 0.28988 (0.087198) Loss: 0.28988 (0.087198) 2025-03-19,23:19:03 | INFO | Train Epoch: 19 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 148.761/s, 148.761/s/gpu LR: 0.000053 Logit Scale: 28.087 Contrastive_loss: 0.070197 (0.087112) Loss: 0.070197 (0.087112) 2025-03-19,23:19:24 | INFO | Train Epoch: 19 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.212, 151.479/s, 151.479/s/gpu LR: 0.000053 Logit Scale: 28.088 Contrastive_loss: 0.025237 (0.086801) Loss: 0.025237 (0.086801) 2025-03-19,23:19:46 | INFO | Train Epoch: 19 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 148.050/s, 148.050/s/gpu LR: 0.000053 Logit Scale: 28.084 Contrastive_loss: 0.062162 (0.086678) Loss: 0.062162 (0.086678) 2025-03-19,23:20:07 | INFO | Train Epoch: 19 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 148.651/s, 148.651/s/gpu LR: 0.000053 Logit Scale: 28.093 Contrastive_loss: 0.032300 (0.086407) Loss: 0.032300 (0.086407) 2025-03-19,23:20:29 | INFO | Train Epoch: 19 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 149.155/s, 149.155/s/gpu LR: 0.000053 Logit Scale: 28.101 Contrastive_loss: 0.042030 (0.086188) Loss: 0.042030 (0.086188) 2025-03-19,23:20:51 | INFO | Train Epoch: 19 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.217, 150.031/s, 150.031/s/gpu LR: 0.000053 Logit Scale: 28.106 Contrastive_loss: 0.22048 (0.086849) Loss: 0.22048 (0.086849) 2025-03-19,23:21:12 | INFO | Train Epoch: 19 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.213, 151.924/s, 151.924/s/gpu LR: 0.000053 Logit Scale: 28.105 Contrastive_loss: 0.24867 (0.087642) Loss: 0.24867 (0.087642) 2025-03-19,23:21:33 | INFO | Train Epoch: 19 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 150.706/s, 150.706/s/gpu LR: 0.000053 Logit Scale: 28.088 Contrastive_loss: 0.17108 (0.088049) Loss: 0.17108 (0.088049) 2025-03-19,23:21:55 | INFO | Train Epoch: 19 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 149.212/s, 149.212/s/gpu LR: 0.000053 Logit Scale: 28.109 Contrastive_loss: 0.11538 (0.088182) Loss: 0.11538 (0.088182) 2025-03-19,23:22:16 | INFO | Train Epoch: 19 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 149.558/s, 149.558/s/gpu LR: 0.000053 Logit Scale: 28.102 Contrastive_loss: 0.043467 (0.087966) Loss: 0.043467 (0.087966) 2025-03-19,23:22:38 | INFO | Train Epoch: 19 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.053/s, 148.053/s/gpu LR: 0.000053 Logit Scale: 28.104 Contrastive_loss: 0.0083929 (0.087583) Loss: 0.0083929 (0.087583) 2025-03-19,23:23:00 | INFO | Train Epoch: 19 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 148.282/s, 148.282/s/gpu LR: 0.000053 Logit Scale: 28.111 Contrastive_loss: 0.16037 (0.087932) Loss: 0.16037 (0.087932) 2025-03-19,23:23:21 | INFO | Train Epoch: 19 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 147.430/s, 147.430/s/gpu LR: 0.000052 Logit Scale: 28.091 Contrastive_loss: 0.098012 (0.087980) Loss: 0.098012 (0.087980) 2025-03-19,23:23:43 | INFO | Train Epoch: 19 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.979/s, 149.979/s/gpu LR: 0.000052 Logit Scale: 28.093 Contrastive_loss: 0.084269 (0.087962) Loss: 0.084269 (0.087962) 2025-03-19,23:24:04 | INFO | Train Epoch: 19 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 148.474/s, 148.474/s/gpu LR: 0.000052 Logit Scale: 28.093 Contrastive_loss: 0.018472 (0.087634) Loss: 0.018472 (0.087634) 2025-03-19,23:24:26 | INFO | Train Epoch: 19 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 150.903/s, 150.903/s/gpu LR: 0.000052 Logit Scale: 28.092 Contrastive_loss: 0.20400 (0.088181) Loss: 0.20400 (0.088181) 2025-03-19,23:24:47 | INFO | Train Epoch: 19 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.213, 151.676/s, 151.676/s/gpu LR: 0.000052 Logit Scale: 28.089 Contrastive_loss: 0.050753 (0.088006) Loss: 0.050753 (0.088006) 2025-03-19,23:25:08 | INFO | Train Epoch: 19 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.212, 151.182/s, 151.182/s/gpu LR: 0.000052 Logit Scale: 28.091 Contrastive_loss: 0.036169 (0.087765) Loss: 0.036169 (0.087765) 2025-03-19,23:25:30 | INFO | Train Epoch: 19 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 149.155/s, 149.155/s/gpu LR: 0.000052 Logit Scale: 28.107 Contrastive_loss: 0.077806 (0.087719) Loss: 0.077806 (0.087719) 2025-03-19,23:25:51 | INFO | Train Epoch: 19 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.217, 148.773/s, 148.773/s/gpu LR: 0.000052 Logit Scale: 28.105 Contrastive_loss: 0.11768 (0.087857) Loss: 0.11768 (0.087857) 2025-03-19,23:26:13 | INFO | Train Epoch: 19 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 150.997/s, 150.997/s/gpu LR: 0.000052 Logit Scale: 28.094 Contrastive_loss: 0.10800 (0.087949) Loss: 0.10800 (0.087949) 2025-03-19,23:26:34 | INFO | Train Epoch: 19 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.212, 149.466/s, 149.466/s/gpu LR: 0.000052 Logit Scale: 28.078 Contrastive_loss: 0.076657 (0.087897) Loss: 0.076657 (0.087897) 2025-03-19,23:26:55 | INFO | Train Epoch: 19 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 150.763/s, 150.763/s/gpu LR: 0.000052 Logit Scale: 28.071 Contrastive_loss: 0.023111 (0.087603) Loss: 0.023111 (0.087603) 2025-03-19,23:27:17 | INFO | Train Epoch: 19 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 150.195/s, 150.195/s/gpu LR: 0.000052 Logit Scale: 28.082 Contrastive_loss: 0.018989 (0.087293) Loss: 0.018989 (0.087293) 2025-03-19,23:27:39 | INFO | Train Epoch: 19 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 148.381/s, 148.381/s/gpu LR: 0.000052 Logit Scale: 28.092 Contrastive_loss: 0.14332 (0.087545) Loss: 0.14332 (0.087545) 2025-03-19,23:28:00 | INFO | Train Epoch: 19 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 148.963/s, 148.963/s/gpu LR: 0.000052 Logit Scale: 28.076 Contrastive_loss: 0.022966 (0.087255) Loss: 0.022966 (0.087255) 2025-03-19,23:28:22 | INFO | Train Epoch: 19 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 147.528/s, 147.528/s/gpu LR: 0.000052 Logit Scale: 28.089 Contrastive_loss: 0.050684 (0.087092) Loss: 0.050684 (0.087092) 2025-03-19,23:28:43 | INFO | Train Epoch: 19 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 149.649/s, 149.649/s/gpu LR: 0.000052 Logit Scale: 28.113 Contrastive_loss: 0.12914 (0.087279) Loss: 0.12914 (0.087279) 2025-03-19,23:29:05 | INFO | Train Epoch: 19 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 149.283/s, 149.283/s/gpu LR: 0.000052 Logit Scale: 28.103 Contrastive_loss: 0.0091723 (0.086933) Loss: 0.0091723 (0.086933) 2025-03-19,23:29:26 | INFO | Train Epoch: 19 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 149.269/s, 149.269/s/gpu LR: 0.000052 Logit Scale: 28.098 Contrastive_loss: 0.0012985 (0.086556) Loss: 0.0012985 (0.086556) 2025-03-19,23:29:48 | INFO | Train Epoch: 19 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 147.817/s, 147.817/s/gpu LR: 0.000052 Logit Scale: 28.110 Contrastive_loss: 0.074971 (0.086505) Loss: 0.074971 (0.086505) 2025-03-19,23:30:09 | INFO | Train Epoch: 19 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 147.881/s, 147.881/s/gpu LR: 0.000052 Logit Scale: 28.107 Contrastive_loss: 0.054076 (0.086364) Loss: 0.054076 (0.086364) 2025-03-19,23:30:31 | INFO | Train Epoch: 19 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 149.745/s, 149.745/s/gpu LR: 0.000052 Logit Scale: 28.106 Contrastive_loss: 0.019235 (0.086072) Loss: 0.019235 (0.086072) 2025-03-19,23:30:52 | INFO | Train Epoch: 19 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.217, 149.311/s, 149.311/s/gpu LR: 0.000052 Logit Scale: 28.119 Contrastive_loss: 0.10942 (0.086173) Loss: 0.10942 (0.086173) 2025-03-19,23:31:14 | INFO | Train Epoch: 19 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.212, 151.619/s, 151.619/s/gpu LR: 0.000052 Logit Scale: 28.121 Contrastive_loss: 0.0063970 (0.085829) Loss: 0.0063970 (0.085829) 2025-03-19,23:31:35 | INFO | Train Epoch: 19 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.212, 149.491/s, 149.491/s/gpu LR: 0.000052 Logit Scale: 28.097 Contrastive_loss: 0.0014736 (0.085467) Loss: 0.0014736 (0.085467) 2025-03-19,23:31:56 | INFO | Train Epoch: 19 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 150.259/s, 150.259/s/gpu LR: 0.000052 Logit Scale: 28.098 Contrastive_loss: 0.021135 (0.085192) Loss: 0.021135 (0.085192) 2025-03-19,23:32:18 | INFO | Train Epoch: 19 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.213, 150.787/s, 150.787/s/gpu LR: 0.000051 Logit Scale: 28.103 Contrastive_loss: 0.17606 (0.085579) Loss: 0.17606 (0.085579) 2025-03-19,23:32:39 | INFO | Train Epoch: 19 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 143.959/s, 143.959/s/gpu LR: 0.000051 Logit Scale: 28.093 Contrastive_loss: 0.018158 (0.085293) Loss: 0.018158 (0.085293) 2025-03-19,23:33:01 | INFO | Train Epoch: 19 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.220, 150.222/s, 150.222/s/gpu LR: 0.000051 Logit Scale: 28.106 Contrastive_loss: 0.057100 (0.085174) Loss: 0.057100 (0.085174) 2025-03-19,23:33:23 | INFO | Train Epoch: 19 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 146.812/s, 146.812/s/gpu LR: 0.000051 Logit Scale: 28.115 Contrastive_loss: 0.12498 (0.085341) Loss: 0.12498 (0.085341) 2025-03-19,23:33:45 | INFO | Train Epoch: 19 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.222, 145.601/s, 145.601/s/gpu LR: 0.000051 Logit Scale: 28.099 Contrastive_loss: 0.098973 (0.085398) Loss: 0.098973 (0.085398) 2025-03-19,23:34:07 | INFO | Train Epoch: 19 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.221, 146.653/s, 146.653/s/gpu LR: 0.000051 Logit Scale: 28.083 Contrastive_loss: 0.082726 (0.085387) Loss: 0.082726 (0.085387) 2025-03-19,23:34:15 | INFO | Train Epoch: 19 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.220, 141.167/s, 141.167/s/gpu LR: 0.000051 Logit Scale: 28.092 Contrastive_loss: 0.011725 (0.085082) Loss: 0.011725 (0.085082) 2025-03-19,23:34:16 | INFO | Eval Epoch: 20 [32 / 7443] Clip Loss: 3.142380 2025-03-19,23:34:21 | INFO | Eval Epoch: 20 [3232 / 7443] Clip Loss: 0.846006 2025-03-19,23:34:27 | INFO | Eval Epoch: 20 [6432 / 7443] Clip Loss: 0.628910 2025-03-19,23:34:30 | INFO | Eval Epoch: 20 image_to_text_mean_rank: 79.0081 image_to_text_median_rank: 6.0000 image_to_text_R@1: 0.1653 image_to_text_R@5: 0.4987 image_to_text_R@10: 0.6754 text_to_image_mean_rank: 49.1915 text_to_image_median_rank: 6.0000 text_to_image_R@1: 0.1635 text_to_image_R@5: 0.4909 text_to_image_R@10: 0.6659 clip_val_loss: 0.5861 epoch: 20.0000 num_samples: 7443.0000 2025-03-19,23:35:03 | INFO | Start epoch 20 2025-03-19,23:35:03 | INFO | Train Epoch: 20 [ 32/766009 (0%)] Data (t): 0.174 Batch (t): 0.384, 83.2976/s, 83.2976/s/gpu LR: 0.000051 Logit Scale: 28.092 Contrastive_loss: 0.092179 (0.092179) Loss: 0.092179 (0.092179) 2025-03-19,23:35:25 | INFO | Train Epoch: 20 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 151.620/s, 151.620/s/gpu LR: 0.000051 Logit Scale: 28.098 Contrastive_loss: 0.051057 (0.071618) Loss: 0.051057 (0.071618) 2025-03-19,23:35:46 | INFO | Train Epoch: 20 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 149.389/s, 149.389/s/gpu LR: 0.000051 Logit Scale: 28.091 Contrastive_loss: 0.040205 (0.061147) Loss: 0.040205 (0.061147) 2025-03-19,23:36:07 | INFO | Train Epoch: 20 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 150.068/s, 150.068/s/gpu LR: 0.000051 Logit Scale: 28.105 Contrastive_loss: 0.068875 (0.063079) Loss: 0.068875 (0.063079) 2025-03-19,23:36:29 | INFO | Train Epoch: 20 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.956/s, 149.956/s/gpu LR: 0.000051 Logit Scale: 28.128 Contrastive_loss: 0.10304 (0.071072) Loss: 0.10304 (0.071072) 2025-03-19,23:36:50 | INFO | Train Epoch: 20 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.213, 151.124/s, 151.124/s/gpu LR: 0.000051 Logit Scale: 28.144 Contrastive_loss: 0.061167 (0.069421) Loss: 0.061167 (0.069421) 2025-03-19,23:37:11 | INFO | Train Epoch: 20 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 150.030/s, 150.030/s/gpu LR: 0.000051 Logit Scale: 28.150 Contrastive_loss: 0.10660 (0.074732) Loss: 0.10660 (0.074732) 2025-03-19,23:37:33 | INFO | Train Epoch: 20 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 148.640/s, 148.640/s/gpu LR: 0.000051 Logit Scale: 28.147 Contrastive_loss: 0.11803 (0.080144) Loss: 0.11803 (0.080144) 2025-03-19,23:37:54 | INFO | Train Epoch: 20 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 150.212/s, 150.212/s/gpu LR: 0.000051 Logit Scale: 28.168 Contrastive_loss: 0.13215 (0.085922) Loss: 0.13215 (0.085922) 2025-03-19,23:38:16 | INFO | Train Epoch: 20 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 148.922/s, 148.922/s/gpu LR: 0.000051 Logit Scale: 28.183 Contrastive_loss: 0.063565 (0.083686) Loss: 0.063565 (0.083686) 2025-03-19,23:38:37 | INFO | Train Epoch: 20 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.328/s, 149.328/s/gpu LR: 0.000051 Logit Scale: 28.184 Contrastive_loss: 0.019770 (0.077876) Loss: 0.019770 (0.077876) 2025-03-19,23:38:59 | INFO | Train Epoch: 20 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 151.568/s, 151.568/s/gpu LR: 0.000051 Logit Scale: 28.195 Contrastive_loss: 0.069644 (0.077190) Loss: 0.069644 (0.077190) 2025-03-19,23:39:20 | INFO | Train Epoch: 20 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 149.613/s, 149.613/s/gpu LR: 0.000051 Logit Scale: 28.185 Contrastive_loss: 0.14487 (0.082396) Loss: 0.14487 (0.082396) 2025-03-19,23:39:41 | INFO | Train Epoch: 20 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 146.239/s, 146.239/s/gpu LR: 0.000051 Logit Scale: 28.180 Contrastive_loss: 0.17439 (0.088967) Loss: 0.17439 (0.088967) 2025-03-19,23:40:03 | INFO | Train Epoch: 20 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 149.628/s, 149.628/s/gpu LR: 0.000051 Logit Scale: 28.205 Contrastive_loss: 0.13710 (0.092176) Loss: 0.13710 (0.092176) 2025-03-19,23:40:24 | INFO | Train Epoch: 20 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 147.444/s, 147.444/s/gpu LR: 0.000051 Logit Scale: 28.192 Contrastive_loss: 0.063775 (0.090401) Loss: 0.063775 (0.090401) 2025-03-19,23:40:46 | INFO | Train Epoch: 20 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 146.406/s, 146.406/s/gpu LR: 0.000051 Logit Scale: 28.190 Contrastive_loss: 0.15156 (0.093998) Loss: 0.15156 (0.093998) 2025-03-19,23:41:08 | INFO | Train Epoch: 20 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.218, 146.576/s, 146.576/s/gpu LR: 0.000051 Logit Scale: 28.181 Contrastive_loss: 0.18549 (0.099081) Loss: 0.18549 (0.099081) 2025-03-19,23:41:30 | INFO | Train Epoch: 20 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.220, 146.094/s, 146.094/s/gpu LR: 0.000051 Logit Scale: 28.203 Contrastive_loss: 0.20786 (0.10481) Loss: 0.20786 (0.10481) 2025-03-19,23:41:52 | INFO | Train Epoch: 20 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.219, 148.782/s, 148.782/s/gpu LR: 0.000051 Logit Scale: 28.203 Contrastive_loss: 0.056382 (0.10238) Loss: 0.056382 (0.10238) 2025-03-19,23:42:13 | INFO | Train Epoch: 20 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 147.656/s, 147.656/s/gpu LR: 0.000051 Logit Scale: 28.203 Contrastive_loss: 0.065005 (0.10060) Loss: 0.065005 (0.10060) 2025-03-19,23:42:35 | INFO | Train Epoch: 20 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.218, 145.486/s, 145.486/s/gpu LR: 0.000050 Logit Scale: 28.209 Contrastive_loss: 0.098110 (0.10049) Loss: 0.098110 (0.10049) 2025-03-19,23:42:57 | INFO | Train Epoch: 20 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.220, 145.153/s, 145.153/s/gpu LR: 0.000050 Logit Scale: 28.214 Contrastive_loss: 0.036419 (0.097706) Loss: 0.036419 (0.097706) 2025-03-19,23:43:19 | INFO | Train Epoch: 20 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.218, 145.367/s, 145.367/s/gpu LR: 0.000050 Logit Scale: 28.219 Contrastive_loss: 0.00058800 (0.093659) Loss: 0.00058800 (0.093659) 2025-03-19,23:43:41 | INFO | Train Epoch: 20 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.223, 145.613/s, 145.613/s/gpu LR: 0.000050 Logit Scale: 28.209 Contrastive_loss: 0.010542 (0.090334) Loss: 0.010542 (0.090334) 2025-03-19,23:44:03 | INFO | Train Epoch: 20 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 145.364/s, 145.364/s/gpu LR: 0.000050 Logit Scale: 28.213 Contrastive_loss: 0.052777 (0.088890) Loss: 0.052777 (0.088890) 2025-03-19,23:44:25 | INFO | Train Epoch: 20 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 149.424/s, 149.424/s/gpu LR: 0.000050 Logit Scale: 28.202 Contrastive_loss: 0.050670 (0.087474) Loss: 0.050670 (0.087474) 2025-03-19,23:44:46 | INFO | Train Epoch: 20 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.214, 148.352/s, 148.352/s/gpu LR: 0.000050 Logit Scale: 28.208 Contrastive_loss: 0.0014550 (0.084402) Loss: 0.0014550 (0.084402) 2025-03-19,23:45:08 | INFO | Train Epoch: 20 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.214, 150.146/s, 150.146/s/gpu LR: 0.000050 Logit Scale: 28.224 Contrastive_loss: 0.025588 (0.082374) Loss: 0.025588 (0.082374) 2025-03-19,23:45:29 | INFO | Train Epoch: 20 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.212, 149.990/s, 149.990/s/gpu LR: 0.000050 Logit Scale: 28.221 Contrastive_loss: 0.19957 (0.086281) Loss: 0.19957 (0.086281) 2025-03-19,23:45:51 | INFO | Train Epoch: 20 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 150.546/s, 150.546/s/gpu LR: 0.000050 Logit Scale: 28.225 Contrastive_loss: 0.024275 (0.084281) Loss: 0.024275 (0.084281) 2025-03-19,23:46:12 | INFO | Train Epoch: 20 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 150.122/s, 150.122/s/gpu LR: 0.000050 Logit Scale: 28.233 Contrastive_loss: 0.0068756 (0.081862) Loss: 0.0068756 (0.081862) 2025-03-19,23:46:33 | INFO | Train Epoch: 20 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 150.526/s, 150.526/s/gpu LR: 0.000050 Logit Scale: 28.235 Contrastive_loss: 0.25571 (0.087130) Loss: 0.25571 (0.087130) 2025-03-19,23:46:55 | INFO | Train Epoch: 20 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.736/s, 149.736/s/gpu LR: 0.000050 Logit Scale: 28.210 Contrastive_loss: 0.24078 (0.091649) Loss: 0.24078 (0.091649) 2025-03-19,23:47:17 | INFO | Train Epoch: 20 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.221, 146.195/s, 146.195/s/gpu LR: 0.000050 Logit Scale: 28.217 Contrastive_loss: 0.10460 (0.092019) Loss: 0.10460 (0.092019) 2025-03-19,23:47:39 | INFO | Train Epoch: 20 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.223, 146.499/s, 146.499/s/gpu LR: 0.000050 Logit Scale: 28.208 Contrastive_loss: 0.028171 (0.090245) Loss: 0.028171 (0.090245) 2025-03-19,23:48:01 | INFO | Train Epoch: 20 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.222, 143.292/s, 143.292/s/gpu LR: 0.000050 Logit Scale: 28.221 Contrastive_loss: 0.010080 (0.088078) Loss: 0.010080 (0.088078) 2025-03-19,23:48:23 | INFO | Train Epoch: 20 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.221, 145.987/s, 145.987/s/gpu LR: 0.000050 Logit Scale: 28.232 Contrastive_loss: 0.14144 (0.089483) Loss: 0.14144 (0.089483) 2025-03-19,23:48:46 | INFO | Train Epoch: 20 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.222, 143.823/s, 143.823/s/gpu LR: 0.000050 Logit Scale: 28.256 Contrastive_loss: 0.076201 (0.089142) Loss: 0.076201 (0.089142) 2025-03-19,23:49:08 | INFO | Train Epoch: 20 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.221, 146.750/s, 146.750/s/gpu LR: 0.000050 Logit Scale: 28.260 Contrastive_loss: 0.019494 (0.087401) Loss: 0.019494 (0.087401) 2025-03-19,23:49:30 | INFO | Train Epoch: 20 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.221, 144.164/s, 144.164/s/gpu LR: 0.000050 Logit Scale: 28.258 Contrastive_loss: 0.052570 (0.086551) Loss: 0.052570 (0.086551) 2025-03-19,23:49:51 | INFO | Train Epoch: 20 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 149.216/s, 149.216/s/gpu LR: 0.000050 Logit Scale: 28.249 Contrastive_loss: 0.11590 (0.087250) Loss: 0.11590 (0.087250) 2025-03-19,23:50:13 | INFO | Train Epoch: 20 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 152.063/s, 152.063/s/gpu LR: 0.000050 Logit Scale: 28.271 Contrastive_loss: 0.042749 (0.086215) Loss: 0.042749 (0.086215) 2025-03-19,23:50:34 | INFO | Train Epoch: 20 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.213, 148.508/s, 148.508/s/gpu LR: 0.000050 Logit Scale: 28.293 Contrastive_loss: 0.15297 (0.087733) Loss: 0.15297 (0.087733) 2025-03-19,23:50:56 | INFO | Train Epoch: 20 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 149.485/s, 149.485/s/gpu LR: 0.000050 Logit Scale: 28.287 Contrastive_loss: 0.00074213 (0.085799) Loss: 0.00074213 (0.085799) 2025-03-19,23:51:17 | INFO | Train Epoch: 20 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 148.861/s, 148.861/s/gpu LR: 0.000050 Logit Scale: 28.260 Contrastive_loss: 0.14917 (0.087177) Loss: 0.14917 (0.087177) 2025-03-19,23:51:39 | INFO | Train Epoch: 20 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 151.145/s, 151.145/s/gpu LR: 0.000050 Logit Scale: 28.265 Contrastive_loss: 0.049731 (0.086380) Loss: 0.049731 (0.086380) 2025-03-19,23:52:00 | INFO | Train Epoch: 20 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.212, 151.520/s, 151.520/s/gpu LR: 0.000049 Logit Scale: 28.248 Contrastive_loss: 0.087835 (0.086411) Loss: 0.087835 (0.086411) 2025-03-19,23:52:21 | INFO | Train Epoch: 20 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.212, 148.980/s, 148.980/s/gpu LR: 0.000049 Logit Scale: 28.268 Contrastive_loss: 0.082807 (0.086337) Loss: 0.082807 (0.086337) 2025-03-19,23:52:43 | INFO | Train Epoch: 20 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 149.109/s, 149.109/s/gpu LR: 0.000049 Logit Scale: 28.257 Contrastive_loss: 0.051100 (0.085632) Loss: 0.051100 (0.085632) 2025-03-19,23:53:04 | INFO | Train Epoch: 20 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 149.931/s, 149.931/s/gpu LR: 0.000049 Logit Scale: 28.254 Contrastive_loss: 0.014512 (0.084238) Loss: 0.014512 (0.084238) 2025-03-19,23:53:26 | INFO | Train Epoch: 20 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 149.070/s, 149.070/s/gpu LR: 0.000049 Logit Scale: 28.273 Contrastive_loss: 0.037685 (0.083343) Loss: 0.037685 (0.083343) 2025-03-19,23:53:47 | INFO | Train Epoch: 20 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 149.748/s, 149.748/s/gpu LR: 0.000049 Logit Scale: 28.309 Contrastive_loss: 0.15705 (0.084733) Loss: 0.15705 (0.084733) 2025-03-19,23:54:09 | INFO | Train Epoch: 20 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.212, 150.775/s, 150.775/s/gpu LR: 0.000049 Logit Scale: 28.329 Contrastive_loss: 0.046754 (0.084030) Loss: 0.046754 (0.084030) 2025-03-19,23:54:30 | INFO | Train Epoch: 20 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 147.925/s, 147.925/s/gpu LR: 0.000049 Logit Scale: 28.325 Contrastive_loss: 0.057455 (0.083547) Loss: 0.057455 (0.083547) 2025-03-19,23:54:52 | INFO | Train Epoch: 20 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 149.833/s, 149.833/s/gpu LR: 0.000049 Logit Scale: 28.319 Contrastive_loss: 0.19651 (0.085564) Loss: 0.19651 (0.085564) 2025-03-19,23:55:13 | INFO | Train Epoch: 20 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.213, 150.973/s, 150.973/s/gpu LR: 0.000049 Logit Scale: 28.328 Contrastive_loss: 0.10461 (0.085898) Loss: 0.10461 (0.085898) 2025-03-19,23:55:34 | INFO | Train Epoch: 20 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.213, 149.415/s, 149.415/s/gpu LR: 0.000049 Logit Scale: 28.320 Contrastive_loss: 0.035542 (0.085030) Loss: 0.035542 (0.085030) 2025-03-19,23:55:56 | INFO | Train Epoch: 20 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 146.245/s, 146.245/s/gpu LR: 0.000049 Logit Scale: 28.347 Contrastive_loss: 0.017146 (0.083879) Loss: 0.017146 (0.083879) 2025-03-19,23:56:18 | INFO | Train Epoch: 20 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 151.641/s, 151.641/s/gpu LR: 0.000049 Logit Scale: 28.348 Contrastive_loss: 0.26808 (0.086949) Loss: 0.26808 (0.086949) 2025-03-19,23:56:39 | INFO | Train Epoch: 20 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 147.011/s, 147.011/s/gpu LR: 0.000049 Logit Scale: 28.356 Contrastive_loss: 0.044083 (0.086247) Loss: 0.044083 (0.086247) 2025-03-19,23:57:01 | INFO | Train Epoch: 20 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 150.444/s, 150.444/s/gpu LR: 0.000049 Logit Scale: 28.332 Contrastive_loss: 0.10193 (0.086500) Loss: 0.10193 (0.086500) 2025-03-19,23:57:22 | INFO | Train Epoch: 20 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.213, 151.326/s, 151.326/s/gpu LR: 0.000049 Logit Scale: 28.337 Contrastive_loss: 0.029209 (0.085590) Loss: 0.029209 (0.085590) 2025-03-19,23:57:44 | INFO | Train Epoch: 20 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.213, 151.539/s, 151.539/s/gpu LR: 0.000049 Logit Scale: 28.335 Contrastive_loss: 0.0084104 (0.084384) Loss: 0.0084104 (0.084384) 2025-03-19,23:58:05 | INFO | Train Epoch: 20 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.211, 150.949/s, 150.949/s/gpu LR: 0.000049 Logit Scale: 28.354 Contrastive_loss: 0.0064874 (0.083186) Loss: 0.0064874 (0.083186) 2025-03-19,23:58:26 | INFO | Train Epoch: 20 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.211, 149.636/s, 149.636/s/gpu LR: 0.000049 Logit Scale: 28.354 Contrastive_loss: 0.0065097 (0.082024) Loss: 0.0065097 (0.082024) 2025-03-19,23:58:47 | INFO | Train Epoch: 20 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.212, 150.232/s, 150.232/s/gpu LR: 0.000049 Logit Scale: 28.353 Contrastive_loss: 0.0047780 (0.080871) Loss: 0.0047780 (0.080871) 2025-03-19,23:59:08 | INFO | Train Epoch: 20 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 148.197/s, 148.197/s/gpu LR: 0.000049 Logit Scale: 28.351 Contrastive_loss: 0.029171 (0.080111) Loss: 0.029171 (0.080111) 2025-03-19,23:59:30 | INFO | Train Epoch: 20 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 146.376/s, 146.376/s/gpu LR: 0.000049 Logit Scale: 28.358 Contrastive_loss: 0.15066 (0.081133) Loss: 0.15066 (0.081133) 2025-03-19,23:59:52 | INFO | Train Epoch: 20 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.030/s, 149.030/s/gpu LR: 0.000049 Logit Scale: 28.369 Contrastive_loss: 0.0084709 (0.080095) Loss: 0.0084709 (0.080095) 2025-03-20,00:00:13 | INFO | Train Epoch: 20 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.736/s, 149.736/s/gpu LR: 0.000049 Logit Scale: 28.360 Contrastive_loss: 0.052512 (0.079707) Loss: 0.052512 (0.079707) 2025-03-20,00:00:34 | INFO | Train Epoch: 20 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 149.116/s, 149.116/s/gpu LR: 0.000049 Logit Scale: 28.364 Contrastive_loss: 0.087185 (0.079811) Loss: 0.087185 (0.079811) 2025-03-20,00:00:56 | INFO | Train Epoch: 20 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 148.656/s, 148.656/s/gpu LR: 0.000049 Logit Scale: 28.362 Contrastive_loss: 0.023443 (0.079038) Loss: 0.023443 (0.079038) 2025-03-20,00:01:17 | INFO | Train Epoch: 20 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.032/s, 149.032/s/gpu LR: 0.000048 Logit Scale: 28.347 Contrastive_loss: 0.042954 (0.078551) Loss: 0.042954 (0.078551) 2025-03-20,00:01:39 | INFO | Train Epoch: 20 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 149.183/s, 149.183/s/gpu LR: 0.000048 Logit Scale: 28.356 Contrastive_loss: 0.024094 (0.077825) Loss: 0.024094 (0.077825) 2025-03-20,00:02:00 | INFO | Train Epoch: 20 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.771/s, 149.771/s/gpu LR: 0.000048 Logit Scale: 28.339 Contrastive_loss: 0.095983 (0.078064) Loss: 0.095983 (0.078064) 2025-03-20,00:02:22 | INFO | Train Epoch: 20 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.217, 152.775/s, 152.775/s/gpu LR: 0.000048 Logit Scale: 28.358 Contrastive_loss: 0.028215 (0.077416) Loss: 0.028215 (0.077416) 2025-03-20,00:02:43 | INFO | Train Epoch: 20 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.212, 150.718/s, 150.718/s/gpu LR: 0.000048 Logit Scale: 28.360 Contrastive_loss: 0.036756 (0.076895) Loss: 0.036756 (0.076895) 2025-03-20,00:03:04 | INFO | Train Epoch: 20 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 150.298/s, 150.298/s/gpu LR: 0.000048 Logit Scale: 28.331 Contrastive_loss: 0.051762 (0.076577) Loss: 0.051762 (0.076577) 2025-03-20,00:03:26 | INFO | Train Epoch: 20 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 148.423/s, 148.423/s/gpu LR: 0.000048 Logit Scale: 28.335 Contrastive_loss: 0.0019407 (0.075644) Loss: 0.0019407 (0.075644) 2025-03-20,00:03:48 | INFO | Train Epoch: 20 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.217, 147.536/s, 147.536/s/gpu LR: 0.000048 Logit Scale: 28.350 Contrastive_loss: 0.046612 (0.075285) Loss: 0.046612 (0.075285) 2025-03-20,00:04:09 | INFO | Train Epoch: 20 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.217, 147.750/s, 147.750/s/gpu LR: 0.000048 Logit Scale: 28.332 Contrastive_loss: 0.14844 (0.076178) Loss: 0.14844 (0.076178) 2025-03-20,00:04:31 | INFO | Train Epoch: 20 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 148.300/s, 148.300/s/gpu LR: 0.000048 Logit Scale: 28.326 Contrastive_loss: 0.065060 (0.076044) Loss: 0.065060 (0.076044) 2025-03-20,00:04:52 | INFO | Train Epoch: 20 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 150.923/s, 150.923/s/gpu LR: 0.000048 Logit Scale: 28.335 Contrastive_loss: 0.23959 (0.077991) Loss: 0.23959 (0.077991) 2025-03-20,00:05:14 | INFO | Train Epoch: 20 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 149.680/s, 149.680/s/gpu LR: 0.000048 Logit Scale: 28.326 Contrastive_loss: 0.14045 (0.078726) Loss: 0.14045 (0.078726) 2025-03-20,00:05:35 | INFO | Train Epoch: 20 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 150.083/s, 150.083/s/gpu LR: 0.000048 Logit Scale: 28.345 Contrastive_loss: 0.013618 (0.077968) Loss: 0.013618 (0.077968) 2025-03-20,00:05:57 | INFO | Train Epoch: 20 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 151.018/s, 151.018/s/gpu LR: 0.000048 Logit Scale: 28.331 Contrastive_loss: 0.18091 (0.079152) Loss: 0.18091 (0.079152) 2025-03-20,00:06:18 | INFO | Train Epoch: 20 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.221/s, 149.221/s/gpu LR: 0.000048 Logit Scale: 28.326 Contrastive_loss: 0.027946 (0.078570) Loss: 0.027946 (0.078570) 2025-03-20,00:06:40 | INFO | Train Epoch: 20 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 149.075/s, 149.075/s/gpu LR: 0.000048 Logit Scale: 28.343 Contrastive_loss: 0.056356 (0.078320) Loss: 0.056356 (0.078320) 2025-03-20,00:07:01 | INFO | Train Epoch: 20 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 149.048/s, 149.048/s/gpu LR: 0.000048 Logit Scale: 28.328 Contrastive_loss: 0.0059617 (0.077516) Loss: 0.0059617 (0.077516) 2025-03-20,00:07:23 | INFO | Train Epoch: 20 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.530/s, 149.530/s/gpu LR: 0.000048 Logit Scale: 28.325 Contrastive_loss: 0.14371 (0.078244) Loss: 0.14371 (0.078244) 2025-03-20,00:07:44 | INFO | Train Epoch: 20 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.297/s, 148.297/s/gpu LR: 0.000048 Logit Scale: 28.336 Contrastive_loss: 0.016636 (0.077574) Loss: 0.016636 (0.077574) 2025-03-20,00:08:06 | INFO | Train Epoch: 20 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.062/s, 148.062/s/gpu LR: 0.000048 Logit Scale: 28.346 Contrastive_loss: 0.066024 (0.077450) Loss: 0.066024 (0.077450) 2025-03-20,00:08:27 | INFO | Train Epoch: 20 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.581/s, 149.581/s/gpu LR: 0.000048 Logit Scale: 28.328 Contrastive_loss: 0.29946 (0.079812) Loss: 0.29946 (0.079812) 2025-03-20,00:08:49 | INFO | Train Epoch: 20 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 148.032/s, 148.032/s/gpu LR: 0.000048 Logit Scale: 28.339 Contrastive_loss: 0.070106 (0.079709) Loss: 0.070106 (0.079709) 2025-03-20,00:09:10 | INFO | Train Epoch: 20 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 150.449/s, 150.449/s/gpu LR: 0.000048 Logit Scale: 28.364 Contrastive_loss: 0.058699 (0.079491) Loss: 0.058699 (0.079491) 2025-03-20,00:09:32 | INFO | Train Epoch: 20 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 147.857/s, 147.857/s/gpu LR: 0.000048 Logit Scale: 28.372 Contrastive_loss: 0.28354 (0.081594) Loss: 0.28354 (0.081594) 2025-03-20,00:09:53 | INFO | Train Epoch: 20 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 146.234/s, 146.234/s/gpu LR: 0.000048 Logit Scale: 28.356 Contrastive_loss: 0.061415 (0.081388) Loss: 0.061415 (0.081388) 2025-03-20,00:10:15 | INFO | Train Epoch: 20 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 148.766/s, 148.766/s/gpu LR: 0.000048 Logit Scale: 28.334 Contrastive_loss: 0.022618 (0.080795) Loss: 0.022618 (0.080795) 2025-03-20,00:10:37 | INFO | Train Epoch: 20 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 149.538/s, 149.538/s/gpu LR: 0.000047 Logit Scale: 28.360 Contrastive_loss: 0.0013342 (0.080000) Loss: 0.0013342 (0.080000) 2025-03-20,00:10:58 | INFO | Train Epoch: 20 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 150.268/s, 150.268/s/gpu LR: 0.000047 Logit Scale: 28.348 Contrastive_loss: 0.086425 (0.080064) Loss: 0.086425 (0.080064) 2025-03-20,00:11:19 | INFO | Train Epoch: 20 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 149.314/s, 149.314/s/gpu LR: 0.000047 Logit Scale: 28.343 Contrastive_loss: 0.093302 (0.080193) Loss: 0.093302 (0.080193) 2025-03-20,00:11:40 | INFO | Train Epoch: 20 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.212, 151.522/s, 151.522/s/gpu LR: 0.000047 Logit Scale: 28.358 Contrastive_loss: 0.0060809 (0.079474) Loss: 0.0060809 (0.079474) 2025-03-20,00:12:02 | INFO | Train Epoch: 20 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 148.427/s, 148.427/s/gpu LR: 0.000047 Logit Scale: 28.384 Contrastive_loss: 0.0014334 (0.078723) Loss: 0.0014334 (0.078723) 2025-03-20,00:12:23 | INFO | Train Epoch: 20 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 150.866/s, 150.866/s/gpu LR: 0.000047 Logit Scale: 28.382 Contrastive_loss: 0.022372 (0.078187) Loss: 0.022372 (0.078187) 2025-03-20,00:12:45 | INFO | Train Epoch: 20 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.213, 152.097/s, 152.097/s/gpu LR: 0.000047 Logit Scale: 28.393 Contrastive_loss: 0.52000 (0.082355) Loss: 0.52000 (0.082355) 2025-03-20,00:13:06 | INFO | Train Epoch: 20 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.212, 150.595/s, 150.595/s/gpu LR: 0.000047 Logit Scale: 28.377 Contrastive_loss: 0.040662 (0.081965) Loss: 0.040662 (0.081965) 2025-03-20,00:13:27 | INFO | Train Epoch: 20 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 149.472/s, 149.472/s/gpu LR: 0.000047 Logit Scale: 28.400 Contrastive_loss: 0.032664 (0.081509) Loss: 0.032664 (0.081509) 2025-03-20,00:13:49 | INFO | Train Epoch: 20 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 145.235/s, 145.235/s/gpu LR: 0.000047 Logit Scale: 28.402 Contrastive_loss: 0.0092398 (0.080846) Loss: 0.0092398 (0.080846) 2025-03-20,00:14:10 | INFO | Train Epoch: 20 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.410/s, 149.410/s/gpu LR: 0.000047 Logit Scale: 28.378 Contrastive_loss: 0.038573 (0.080461) Loss: 0.038573 (0.080461) 2025-03-20,00:14:32 | INFO | Train Epoch: 20 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.260/s, 149.260/s/gpu LR: 0.000047 Logit Scale: 28.360 Contrastive_loss: 0.0016475 (0.079751) Loss: 0.0016475 (0.079751) 2025-03-20,00:14:53 | INFO | Train Epoch: 20 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 150.142/s, 150.142/s/gpu LR: 0.000047 Logit Scale: 28.348 Contrastive_loss: 0.071951 (0.079682) Loss: 0.071951 (0.079682) 2025-03-20,00:15:15 | INFO | Train Epoch: 20 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 151.457/s, 151.457/s/gpu LR: 0.000047 Logit Scale: 28.356 Contrastive_loss: 0.00042275 (0.078980) Loss: 0.00042275 (0.078980) 2025-03-20,00:15:36 | INFO | Train Epoch: 20 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 145.618/s, 145.618/s/gpu LR: 0.000047 Logit Scale: 28.359 Contrastive_loss: 0.12278 (0.079364) Loss: 0.12278 (0.079364) 2025-03-20,00:15:58 | INFO | Train Epoch: 20 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.606/s, 149.606/s/gpu LR: 0.000047 Logit Scale: 28.369 Contrastive_loss: 0.052239 (0.079129) Loss: 0.052239 (0.079129) 2025-03-20,00:16:19 | INFO | Train Epoch: 20 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 150.332/s, 150.332/s/gpu LR: 0.000047 Logit Scale: 28.364 Contrastive_loss: 0.082911 (0.079161) Loss: 0.082911 (0.079161) 2025-03-20,00:16:41 | INFO | Train Epoch: 20 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.574/s, 149.574/s/gpu LR: 0.000047 Logit Scale: 28.361 Contrastive_loss: 0.0082021 (0.078555) Loss: 0.0082021 (0.078555) 2025-03-20,00:17:02 | INFO | Train Epoch: 20 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 151.014/s, 151.014/s/gpu LR: 0.000047 Logit Scale: 28.339 Contrastive_loss: 0.043792 (0.078260) Loss: 0.043792 (0.078260) 2025-03-20,00:17:24 | INFO | Train Epoch: 20 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 147.790/s, 147.790/s/gpu LR: 0.000047 Logit Scale: 28.348 Contrastive_loss: 0.095975 (0.078409) Loss: 0.095975 (0.078409) 2025-03-20,00:17:45 | INFO | Train Epoch: 20 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 148.308/s, 148.308/s/gpu LR: 0.000047 Logit Scale: 28.354 Contrastive_loss: 0.059963 (0.078255) Loss: 0.059963 (0.078255) 2025-03-20,00:18:07 | INFO | Train Epoch: 20 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 149.586/s, 149.586/s/gpu LR: 0.000047 Logit Scale: 28.373 Contrastive_loss: 0.13726 (0.078743) Loss: 0.13726 (0.078743) 2025-03-20,00:18:28 | INFO | Train Epoch: 20 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.815/s, 149.815/s/gpu LR: 0.000047 Logit Scale: 28.358 Contrastive_loss: 0.026315 (0.078313) Loss: 0.026315 (0.078313) 2025-03-20,00:18:50 | INFO | Train Epoch: 20 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.795/s, 149.795/s/gpu LR: 0.000047 Logit Scale: 28.346 Contrastive_loss: 0.033435 (0.077948) Loss: 0.033435 (0.077948) 2025-03-20,00:19:11 | INFO | Train Epoch: 20 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 147.722/s, 147.722/s/gpu LR: 0.000047 Logit Scale: 28.329 Contrastive_loss: 0.015309 (0.077443) Loss: 0.015309 (0.077443) 2025-03-20,00:19:33 | INFO | Train Epoch: 20 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.847/s, 148.847/s/gpu LR: 0.000047 Logit Scale: 28.338 Contrastive_loss: 0.075878 (0.077431) Loss: 0.075878 (0.077431) 2025-03-20,00:19:54 | INFO | Train Epoch: 20 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 150.536/s, 150.536/s/gpu LR: 0.000047 Logit Scale: 28.356 Contrastive_loss: 0.082354 (0.077470) Loss: 0.082354 (0.077470) 2025-03-20,00:20:16 | INFO | Train Epoch: 20 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.213, 151.991/s, 151.991/s/gpu LR: 0.000046 Logit Scale: 28.346 Contrastive_loss: 0.0074688 (0.076919) Loss: 0.0074688 (0.076919) 2025-03-20,00:20:37 | INFO | Train Epoch: 20 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.211, 151.248/s, 151.248/s/gpu LR: 0.000046 Logit Scale: 28.325 Contrastive_loss: 0.096527 (0.077072) Loss: 0.096527 (0.077072) 2025-03-20,00:20:58 | INFO | Train Epoch: 20 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.213, 148.836/s, 148.836/s/gpu LR: 0.000046 Logit Scale: 28.315 Contrastive_loss: 0.064224 (0.076972) Loss: 0.064224 (0.076972) 2025-03-20,00:21:20 | INFO | Train Epoch: 20 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 149.619/s, 149.619/s/gpu LR: 0.000046 Logit Scale: 28.306 Contrastive_loss: 0.011656 (0.076470) Loss: 0.011656 (0.076470) 2025-03-20,00:21:41 | INFO | Train Epoch: 20 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 149.723/s, 149.723/s/gpu LR: 0.000046 Logit Scale: 28.313 Contrastive_loss: 0.052385 (0.076286) Loss: 0.052385 (0.076286) 2025-03-20,00:22:02 | INFO | Train Epoch: 20 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 149.639/s, 149.639/s/gpu LR: 0.000046 Logit Scale: 28.317 Contrastive_loss: 0.19250 (0.077166) Loss: 0.19250 (0.077166) 2025-03-20,00:22:24 | INFO | Train Epoch: 20 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 149.869/s, 149.869/s/gpu LR: 0.000046 Logit Scale: 28.312 Contrastive_loss: 0.0049682 (0.076623) Loss: 0.0049682 (0.076623) 2025-03-20,00:22:45 | INFO | Train Epoch: 20 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 147.293/s, 147.293/s/gpu LR: 0.000046 Logit Scale: 28.319 Contrastive_loss: 0.12347 (0.076973) Loss: 0.12347 (0.076973) 2025-03-20,00:23:07 | INFO | Train Epoch: 20 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 151.972/s, 151.972/s/gpu LR: 0.000046 Logit Scale: 28.326 Contrastive_loss: 0.27627 (0.078449) Loss: 0.27627 (0.078449) 2025-03-20,00:23:28 | INFO | Train Epoch: 20 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 149.149/s, 149.149/s/gpu LR: 0.000046 Logit Scale: 28.327 Contrastive_loss: 0.049997 (0.078240) Loss: 0.049997 (0.078240) 2025-03-20,00:23:50 | INFO | Train Epoch: 20 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 150.797/s, 150.797/s/gpu LR: 0.000046 Logit Scale: 28.358 Contrastive_loss: 0.025948 (0.077858) Loss: 0.025948 (0.077858) 2025-03-20,00:24:12 | INFO | Train Epoch: 20 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.220, 149.851/s, 149.851/s/gpu LR: 0.000046 Logit Scale: 28.350 Contrastive_loss: 0.099150 (0.078013) Loss: 0.099150 (0.078013) 2025-03-20,00:24:33 | INFO | Train Epoch: 20 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 147.536/s, 147.536/s/gpu LR: 0.000046 Logit Scale: 28.359 Contrastive_loss: 0.084291 (0.078058) Loss: 0.084291 (0.078058) 2025-03-20,00:24:55 | INFO | Train Epoch: 20 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.219, 145.337/s, 145.337/s/gpu LR: 0.000046 Logit Scale: 28.347 Contrastive_loss: 0.14005 (0.078501) Loss: 0.14005 (0.078501) 2025-03-20,00:25:17 | INFO | Train Epoch: 20 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.220, 142.301/s, 142.301/s/gpu LR: 0.000046 Logit Scale: 28.303 Contrastive_loss: 0.053663 (0.078324) Loss: 0.053663 (0.078324) 2025-03-20,00:25:39 | INFO | Train Epoch: 20 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.222, 145.926/s, 145.926/s/gpu LR: 0.000046 Logit Scale: 28.320 Contrastive_loss: 0.13407 (0.078717) Loss: 0.13407 (0.078717) 2025-03-20,00:26:01 | INFO | Train Epoch: 20 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.218, 148.682/s, 148.682/s/gpu LR: 0.000046 Logit Scale: 28.316 Contrastive_loss: 0.11001 (0.078936) Loss: 0.11001 (0.078936) 2025-03-20,00:26:23 | INFO | Train Epoch: 20 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 148.524/s, 148.524/s/gpu LR: 0.000046 Logit Scale: 28.311 Contrastive_loss: 0.020678 (0.078531) Loss: 0.020678 (0.078531) 2025-03-20,00:26:44 | INFO | Train Epoch: 20 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 150.061/s, 150.061/s/gpu LR: 0.000046 Logit Scale: 28.317 Contrastive_loss: 0.016923 (0.078106) Loss: 0.016923 (0.078106) 2025-03-20,00:27:06 | INFO | Train Epoch: 20 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.960/s, 149.960/s/gpu LR: 0.000046 Logit Scale: 28.312 Contrastive_loss: 0.090375 (0.078190) Loss: 0.090375 (0.078190) 2025-03-20,00:27:28 | INFO | Train Epoch: 20 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.218, 147.485/s, 147.485/s/gpu LR: 0.000046 Logit Scale: 28.321 Contrastive_loss: 0.0088298 (0.077719) Loss: 0.0088298 (0.077719) 2025-03-20,00:27:50 | INFO | Train Epoch: 20 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.219, 147.500/s, 147.500/s/gpu LR: 0.000046 Logit Scale: 28.313 Contrastive_loss: 0.099294 (0.077864) Loss: 0.099294 (0.077864) 2025-03-20,00:28:11 | INFO | Train Epoch: 20 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 149.119/s, 149.119/s/gpu LR: 0.000046 Logit Scale: 28.309 Contrastive_loss: 0.048907 (0.077670) Loss: 0.048907 (0.077670) 2025-03-20,00:28:32 | INFO | Train Epoch: 20 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 151.087/s, 151.087/s/gpu LR: 0.000046 Logit Scale: 28.310 Contrastive_loss: 0.041170 (0.077427) Loss: 0.041170 (0.077427) 2025-03-20,00:28:54 | INFO | Train Epoch: 20 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 149.931/s, 149.931/s/gpu LR: 0.000046 Logit Scale: 28.325 Contrastive_loss: 0.0045927 (0.076944) Loss: 0.0045927 (0.076944) 2025-03-20,00:29:15 | INFO | Train Epoch: 20 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.898/s, 149.898/s/gpu LR: 0.000046 Logit Scale: 28.334 Contrastive_loss: 0.00099520 (0.076445) Loss: 0.00099520 (0.076445) 2025-03-20,00:29:36 | INFO | Train Epoch: 20 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.213, 150.660/s, 150.660/s/gpu LR: 0.000046 Logit Scale: 28.330 Contrastive_loss: 0.057540 (0.076321) Loss: 0.057540 (0.076321) 2025-03-20,00:29:58 | INFO | Train Epoch: 20 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 144.745/s, 144.745/s/gpu LR: 0.000045 Logit Scale: 28.326 Contrastive_loss: 0.16149 (0.076874) Loss: 0.16149 (0.076874) 2025-03-20,00:30:20 | INFO | Train Epoch: 20 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.218, 147.040/s, 147.040/s/gpu LR: 0.000045 Logit Scale: 28.331 Contrastive_loss: 0.12076 (0.077157) Loss: 0.12076 (0.077157) 2025-03-20,00:30:42 | INFO | Train Epoch: 20 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.220, 149.683/s, 149.683/s/gpu LR: 0.000045 Logit Scale: 28.335 Contrastive_loss: 0.031824 (0.076867) Loss: 0.031824 (0.076867) 2025-03-20,00:31:04 | INFO | Train Epoch: 20 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.218, 147.930/s, 147.930/s/gpu LR: 0.000045 Logit Scale: 28.335 Contrastive_loss: 0.061313 (0.076768) Loss: 0.061313 (0.076768) 2025-03-20,00:31:25 | INFO | Train Epoch: 20 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.218, 145.583/s, 145.583/s/gpu LR: 0.000045 Logit Scale: 28.331 Contrastive_loss: 0.0044099 (0.076310) Loss: 0.0044099 (0.076310) 2025-03-20,00:31:47 | INFO | Train Epoch: 20 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 145.805/s, 145.805/s/gpu LR: 0.000045 Logit Scale: 28.334 Contrastive_loss: 0.014620 (0.075922) Loss: 0.014620 (0.075922) 2025-03-20,00:32:09 | INFO | Train Epoch: 20 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.218, 142.731/s, 142.731/s/gpu LR: 0.000045 Logit Scale: 28.331 Contrastive_loss: 0.0061640 (0.075486) Loss: 0.0061640 (0.075486) 2025-03-20,00:32:31 | INFO | Train Epoch: 20 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.221, 142.153/s, 142.153/s/gpu LR: 0.000045 Logit Scale: 28.329 Contrastive_loss: 0.20033 (0.076261) Loss: 0.20033 (0.076261) 2025-03-20,00:32:53 | INFO | Train Epoch: 20 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.222, 143.227/s, 143.227/s/gpu LR: 0.000045 Logit Scale: 28.333 Contrastive_loss: 0.017369 (0.075898) Loss: 0.017369 (0.075898) 2025-03-20,00:33:15 | INFO | Train Epoch: 20 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.220, 147.144/s, 147.144/s/gpu LR: 0.000045 Logit Scale: 28.345 Contrastive_loss: 0.025212 (0.075587) Loss: 0.025212 (0.075587) 2025-03-20,00:33:38 | INFO | Train Epoch: 20 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.222, 145.588/s, 145.588/s/gpu LR: 0.000045 Logit Scale: 28.340 Contrastive_loss: 0.010962 (0.075193) Loss: 0.010962 (0.075193) 2025-03-20,00:34:00 | INFO | Train Epoch: 20 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 147.311/s, 147.311/s/gpu LR: 0.000045 Logit Scale: 28.321 Contrastive_loss: 0.086799 (0.075263) Loss: 0.086799 (0.075263) 2025-03-20,00:34:21 | INFO | Train Epoch: 20 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 147.118/s, 147.118/s/gpu LR: 0.000045 Logit Scale: 28.310 Contrastive_loss: 0.058564 (0.075162) Loss: 0.058564 (0.075162) 2025-03-20,00:34:43 | INFO | Train Epoch: 20 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 145.759/s, 145.759/s/gpu LR: 0.000045 Logit Scale: 28.320 Contrastive_loss: 0.25912 (0.076264) Loss: 0.25912 (0.076264) 2025-03-20,00:35:05 | INFO | Train Epoch: 20 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 147.978/s, 147.978/s/gpu LR: 0.000045 Logit Scale: 28.338 Contrastive_loss: 0.058731 (0.076159) Loss: 0.058731 (0.076159) 2025-03-20,00:35:26 | INFO | Train Epoch: 20 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 150.069/s, 150.069/s/gpu LR: 0.000045 Logit Scale: 28.350 Contrastive_loss: 0.077348 (0.076167) Loss: 0.077348 (0.076167) 2025-03-20,00:35:48 | INFO | Train Epoch: 20 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 145.165/s, 145.165/s/gpu LR: 0.000045 Logit Scale: 28.341 Contrastive_loss: 0.052799 (0.076029) Loss: 0.052799 (0.076029) 2025-03-20,00:36:10 | INFO | Train Epoch: 20 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 146.058/s, 146.058/s/gpu LR: 0.000045 Logit Scale: 28.331 Contrastive_loss: 0.043178 (0.075837) Loss: 0.043178 (0.075837) 2025-03-20,00:36:32 | INFO | Train Epoch: 20 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 147.721/s, 147.721/s/gpu LR: 0.000045 Logit Scale: 28.332 Contrastive_loss: 0.21304 (0.076635) Loss: 0.21304 (0.076635) 2025-03-20,00:36:54 | INFO | Train Epoch: 20 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.220, 147.106/s, 147.106/s/gpu LR: 0.000045 Logit Scale: 28.339 Contrastive_loss: 0.14895 (0.077053) Loss: 0.14895 (0.077053) 2025-03-20,00:37:15 | INFO | Train Epoch: 20 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 146.598/s, 146.598/s/gpu LR: 0.000045 Logit Scale: 28.348 Contrastive_loss: 0.19515 (0.077731) Loss: 0.19515 (0.077731) 2025-03-20,00:37:37 | INFO | Train Epoch: 20 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.220, 146.120/s, 146.120/s/gpu LR: 0.000045 Logit Scale: 28.336 Contrastive_loss: 0.081723 (0.077754) Loss: 0.081723 (0.077754) 2025-03-20,00:37:59 | INFO | Train Epoch: 20 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 145.643/s, 145.643/s/gpu LR: 0.000045 Logit Scale: 28.337 Contrastive_loss: 0.0016247 (0.077322) Loss: 0.0016247 (0.077322) 2025-03-20,00:38:21 | INFO | Train Epoch: 20 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 149.853/s, 149.853/s/gpu LR: 0.000045 Logit Scale: 28.348 Contrastive_loss: 0.0095629 (0.076939) Loss: 0.0095629 (0.076939) 2025-03-20,00:38:43 | INFO | Train Epoch: 20 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 149.779/s, 149.779/s/gpu LR: 0.000045 Logit Scale: 28.341 Contrastive_loss: 0.20802 (0.077675) Loss: 0.20802 (0.077675) 2025-03-20,00:39:04 | INFO | Train Epoch: 20 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 149.068/s, 149.068/s/gpu LR: 0.000045 Logit Scale: 28.331 Contrastive_loss: 0.14247 (0.078037) Loss: 0.14247 (0.078037) 2025-03-20,00:39:25 | INFO | Train Epoch: 20 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 148.948/s, 148.948/s/gpu LR: 0.000045 Logit Scale: 28.343 Contrastive_loss: 0.063567 (0.077957) Loss: 0.063567 (0.077957) 2025-03-20,00:39:47 | INFO | Train Epoch: 20 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 150.047/s, 150.047/s/gpu LR: 0.000044 Logit Scale: 28.335 Contrastive_loss: 0.0051383 (0.077555) Loss: 0.0051383 (0.077555) 2025-03-20,00:40:09 | INFO | Train Epoch: 20 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.221, 145.916/s, 145.916/s/gpu LR: 0.000044 Logit Scale: 28.332 Contrastive_loss: 0.095259 (0.077652) Loss: 0.095259 (0.077652) 2025-03-20,00:40:31 | INFO | Train Epoch: 20 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 148.113/s, 148.113/s/gpu LR: 0.000044 Logit Scale: 28.330 Contrastive_loss: 0.14596 (0.078025) Loss: 0.14596 (0.078025) 2025-03-20,00:40:52 | INFO | Train Epoch: 20 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 149.361/s, 149.361/s/gpu LR: 0.000044 Logit Scale: 28.351 Contrastive_loss: 0.083359 (0.078054) Loss: 0.083359 (0.078054) 2025-03-20,00:41:14 | INFO | Train Epoch: 20 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.774/s, 149.774/s/gpu LR: 0.000044 Logit Scale: 28.351 Contrastive_loss: 0.013135 (0.077703) Loss: 0.013135 (0.077703) 2025-03-20,00:41:35 | INFO | Train Epoch: 20 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 146.937/s, 146.937/s/gpu LR: 0.000044 Logit Scale: 28.345 Contrastive_loss: 0.025656 (0.077423) Loss: 0.025656 (0.077423) 2025-03-20,00:41:57 | INFO | Train Epoch: 20 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 149.954/s, 149.954/s/gpu LR: 0.000044 Logit Scale: 28.350 Contrastive_loss: 0.11788 (0.077640) Loss: 0.11788 (0.077640) 2025-03-20,00:42:18 | INFO | Train Epoch: 20 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 149.155/s, 149.155/s/gpu LR: 0.000044 Logit Scale: 28.355 Contrastive_loss: 0.070953 (0.077604) Loss: 0.070953 (0.077604) 2025-03-20,00:42:40 | INFO | Train Epoch: 20 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.437/s, 149.437/s/gpu LR: 0.000044 Logit Scale: 28.365 Contrastive_loss: 0.10914 (0.077771) Loss: 0.10914 (0.077771) 2025-03-20,00:43:02 | INFO | Train Epoch: 20 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 149.844/s, 149.844/s/gpu LR: 0.000044 Logit Scale: 28.358 Contrastive_loss: 0.057515 (0.077664) Loss: 0.057515 (0.077664) 2025-03-20,00:43:23 | INFO | Train Epoch: 20 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.218, 148.824/s, 148.824/s/gpu LR: 0.000044 Logit Scale: 28.355 Contrastive_loss: 0.070758 (0.077628) Loss: 0.070758 (0.077628) 2025-03-20,00:43:45 | INFO | Train Epoch: 20 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 149.554/s, 149.554/s/gpu LR: 0.000044 Logit Scale: 28.373 Contrastive_loss: 0.014368 (0.077299) Loss: 0.014368 (0.077299) 2025-03-20,00:44:06 | INFO | Train Epoch: 20 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 148.487/s, 148.487/s/gpu LR: 0.000044 Logit Scale: 28.380 Contrastive_loss: 0.012129 (0.076961) Loss: 0.012129 (0.076961) 2025-03-20,00:44:28 | INFO | Train Epoch: 20 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.681/s, 149.681/s/gpu LR: 0.000044 Logit Scale: 28.372 Contrastive_loss: 0.018641 (0.076660) Loss: 0.018641 (0.076660) 2025-03-20,00:44:49 | INFO | Train Epoch: 20 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 149.732/s, 149.732/s/gpu LR: 0.000044 Logit Scale: 28.372 Contrastive_loss: 0.055940 (0.076554) Loss: 0.055940 (0.076554) 2025-03-20,00:45:11 | INFO | Train Epoch: 20 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.095/s, 149.095/s/gpu LR: 0.000044 Logit Scale: 28.387 Contrastive_loss: 0.045412 (0.076395) Loss: 0.045412 (0.076395) 2025-03-20,00:45:32 | INFO | Train Epoch: 20 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 149.033/s, 149.033/s/gpu LR: 0.000044 Logit Scale: 28.387 Contrastive_loss: 0.082148 (0.076424) Loss: 0.082148 (0.076424) 2025-03-20,00:45:54 | INFO | Train Epoch: 20 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 149.591/s, 149.591/s/gpu LR: 0.000044 Logit Scale: 28.371 Contrastive_loss: 0.0045942 (0.076062) Loss: 0.0045942 (0.076062) 2025-03-20,00:46:15 | INFO | Train Epoch: 20 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 147.284/s, 147.284/s/gpu LR: 0.000044 Logit Scale: 28.364 Contrastive_loss: 0.063468 (0.075998) Loss: 0.063468 (0.075998) 2025-03-20,00:46:37 | INFO | Train Epoch: 20 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 147.296/s, 147.296/s/gpu LR: 0.000044 Logit Scale: 28.354 Contrastive_loss: 0.0055925 (0.075646) Loss: 0.0055925 (0.075646) 2025-03-20,00:46:59 | INFO | Train Epoch: 20 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.845/s, 148.845/s/gpu LR: 0.000044 Logit Scale: 28.365 Contrastive_loss: 0.0060877 (0.075300) Loss: 0.0060877 (0.075300) 2025-03-20,00:47:20 | INFO | Train Epoch: 20 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.174/s, 148.174/s/gpu LR: 0.000044 Logit Scale: 28.369 Contrastive_loss: 0.082977 (0.075338) Loss: 0.082977 (0.075338) 2025-03-20,00:47:42 | INFO | Train Epoch: 20 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 149.003/s, 149.003/s/gpu LR: 0.000044 Logit Scale: 28.373 Contrastive_loss: 0.17944 (0.075851) Loss: 0.17944 (0.075851) 2025-03-20,00:48:03 | INFO | Train Epoch: 20 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 149.240/s, 149.240/s/gpu LR: 0.000044 Logit Scale: 28.380 Contrastive_loss: 0.064115 (0.075794) Loss: 0.064115 (0.075794) 2025-03-20,00:48:25 | INFO | Train Epoch: 20 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 148.270/s, 148.270/s/gpu LR: 0.000044 Logit Scale: 28.377 Contrastive_loss: 0.018155 (0.075512) Loss: 0.018155 (0.075512) 2025-03-20,00:48:47 | INFO | Train Epoch: 20 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.741/s, 148.741/s/gpu LR: 0.000044 Logit Scale: 28.383 Contrastive_loss: 0.10442 (0.075653) Loss: 0.10442 (0.075653) 2025-03-20,00:49:08 | INFO | Train Epoch: 20 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.977/s, 148.977/s/gpu LR: 0.000044 Logit Scale: 28.382 Contrastive_loss: 0.068504 (0.075618) Loss: 0.068504 (0.075618) 2025-03-20,00:49:30 | INFO | Train Epoch: 20 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 147.170/s, 147.170/s/gpu LR: 0.000043 Logit Scale: 28.394 Contrastive_loss: 0.16276 (0.076037) Loss: 0.16276 (0.076037) 2025-03-20,00:49:52 | INFO | Train Epoch: 20 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.218, 148.513/s, 148.513/s/gpu LR: 0.000043 Logit Scale: 28.367 Contrastive_loss: 0.0065641 (0.075705) Loss: 0.0065641 (0.075705) 2025-03-20,00:50:13 | INFO | Train Epoch: 20 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 148.098/s, 148.098/s/gpu LR: 0.000043 Logit Scale: 28.366 Contrastive_loss: 0.026214 (0.075469) Loss: 0.026214 (0.075469) 2025-03-20,00:50:35 | INFO | Train Epoch: 20 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 148.883/s, 148.883/s/gpu LR: 0.000043 Logit Scale: 28.373 Contrastive_loss: 0.016648 (0.075190) Loss: 0.016648 (0.075190) 2025-03-20,00:50:57 | INFO | Train Epoch: 20 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 146.891/s, 146.891/s/gpu LR: 0.000043 Logit Scale: 28.375 Contrastive_loss: 0.13076 (0.075452) Loss: 0.13076 (0.075452) 2025-03-20,00:51:18 | INFO | Train Epoch: 20 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 149.601/s, 149.601/s/gpu LR: 0.000043 Logit Scale: 28.388 Contrastive_loss: 0.063411 (0.075396) Loss: 0.063411 (0.075396) 2025-03-20,00:51:40 | INFO | Train Epoch: 20 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 148.827/s, 148.827/s/gpu LR: 0.000043 Logit Scale: 28.370 Contrastive_loss: 0.087753 (0.075454) Loss: 0.087753 (0.075454) 2025-03-20,00:52:01 | INFO | Train Epoch: 20 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 149.425/s, 149.425/s/gpu LR: 0.000043 Logit Scale: 28.381 Contrastive_loss: 0.021316 (0.075202) Loss: 0.021316 (0.075202) 2025-03-20,00:52:22 | INFO | Train Epoch: 20 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.040/s, 150.040/s/gpu LR: 0.000043 Logit Scale: 28.382 Contrastive_loss: 0.11594 (0.075391) Loss: 0.11594 (0.075391) 2025-03-20,00:52:44 | INFO | Train Epoch: 20 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 149.898/s, 149.898/s/gpu LR: 0.000043 Logit Scale: 28.371 Contrastive_loss: 0.022472 (0.075147) Loss: 0.022472 (0.075147) 2025-03-20,00:53:05 | INFO | Train Epoch: 20 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.362/s, 148.362/s/gpu LR: 0.000043 Logit Scale: 28.385 Contrastive_loss: 0.19169 (0.075681) Loss: 0.19169 (0.075681) 2025-03-20,00:53:27 | INFO | Train Epoch: 20 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 147.406/s, 147.406/s/gpu LR: 0.000043 Logit Scale: 28.391 Contrastive_loss: 0.11490 (0.075860) Loss: 0.11490 (0.075860) 2025-03-20,00:53:49 | INFO | Train Epoch: 20 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 147.323/s, 147.323/s/gpu LR: 0.000043 Logit Scale: 28.374 Contrastive_loss: 0.12343 (0.076077) Loss: 0.12343 (0.076077) 2025-03-20,00:54:10 | INFO | Train Epoch: 20 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 148.629/s, 148.629/s/gpu LR: 0.000043 Logit Scale: 28.383 Contrastive_loss: 0.011861 (0.075786) Loss: 0.011861 (0.075786) 2025-03-20,00:54:32 | INFO | Train Epoch: 20 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 148.268/s, 148.268/s/gpu LR: 0.000043 Logit Scale: 28.377 Contrastive_loss: 0.020751 (0.075538) Loss: 0.020751 (0.075538) 2025-03-20,00:54:53 | INFO | Train Epoch: 20 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 148.727/s, 148.727/s/gpu LR: 0.000043 Logit Scale: 28.379 Contrastive_loss: 0.068331 (0.075506) Loss: 0.068331 (0.075506) 2025-03-20,00:55:15 | INFO | Train Epoch: 20 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 148.076/s, 148.076/s/gpu LR: 0.000043 Logit Scale: 28.386 Contrastive_loss: 0.050144 (0.075393) Loss: 0.050144 (0.075393) 2025-03-20,00:55:37 | INFO | Train Epoch: 20 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 147.891/s, 147.891/s/gpu LR: 0.000043 Logit Scale: 28.359 Contrastive_loss: 0.025399 (0.075170) Loss: 0.025399 (0.075170) 2025-03-20,00:55:59 | INFO | Train Epoch: 20 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 148.095/s, 148.095/s/gpu LR: 0.000043 Logit Scale: 28.380 Contrastive_loss: 0.096832 (0.075266) Loss: 0.096832 (0.075266) 2025-03-20,00:56:20 | INFO | Train Epoch: 20 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.697/s, 148.697/s/gpu LR: 0.000043 Logit Scale: 28.377 Contrastive_loss: 0.12142 (0.075470) Loss: 0.12142 (0.075470) 2025-03-20,00:56:42 | INFO | Train Epoch: 20 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 149.932/s, 149.932/s/gpu LR: 0.000043 Logit Scale: 28.383 Contrastive_loss: 0.12056 (0.075667) Loss: 0.12056 (0.075667) 2025-03-20,00:57:03 | INFO | Train Epoch: 20 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 149.065/s, 149.065/s/gpu LR: 0.000043 Logit Scale: 28.366 Contrastive_loss: 0.12675 (0.075890) Loss: 0.12675 (0.075890) 2025-03-20,00:57:25 | INFO | Train Epoch: 20 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 147.156/s, 147.156/s/gpu LR: 0.000043 Logit Scale: 28.369 Contrastive_loss: 0.16200 (0.076265) Loss: 0.16200 (0.076265) 2025-03-20,00:57:46 | INFO | Train Epoch: 20 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 149.152/s, 149.152/s/gpu LR: 0.000043 Logit Scale: 28.358 Contrastive_loss: 0.11330 (0.076425) Loss: 0.11330 (0.076425) 2025-03-20,00:58:08 | INFO | Train Epoch: 20 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 148.568/s, 148.568/s/gpu LR: 0.000043 Logit Scale: 28.348 Contrastive_loss: 0.066333 (0.076382) Loss: 0.066333 (0.076382) 2025-03-20,00:58:29 | INFO | Train Epoch: 20 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.409/s, 149.409/s/gpu LR: 0.000043 Logit Scale: 28.337 Contrastive_loss: 0.055251 (0.076291) Loss: 0.055251 (0.076291) 2025-03-20,00:58:51 | INFO | Train Epoch: 20 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 148.469/s, 148.469/s/gpu LR: 0.000043 Logit Scale: 28.349 Contrastive_loss: 0.047508 (0.076168) Loss: 0.047508 (0.076168) 2025-03-20,00:59:13 | INFO | Train Epoch: 20 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 151.287/s, 151.287/s/gpu LR: 0.000042 Logit Scale: 28.360 Contrastive_loss: 0.041386 (0.076020) Loss: 0.041386 (0.076020) 2025-03-20,00:59:34 | INFO | Train Epoch: 20 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.212, 150.855/s, 150.855/s/gpu LR: 0.000042 Logit Scale: 28.357 Contrastive_loss: 0.11426 (0.076182) Loss: 0.11426 (0.076182) 2025-03-20,00:59:55 | INFO | Train Epoch: 20 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.212, 151.322/s, 151.322/s/gpu LR: 0.000042 Logit Scale: 28.356 Contrastive_loss: 0.062317 (0.076123) Loss: 0.062317 (0.076123) 2025-03-20,01:00:16 | INFO | Train Epoch: 20 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 148.102/s, 148.102/s/gpu LR: 0.000042 Logit Scale: 28.362 Contrastive_loss: 0.050858 (0.076017) Loss: 0.050858 (0.076017) 2025-03-20,01:00:38 | INFO | Train Epoch: 20 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 149.600/s, 149.600/s/gpu LR: 0.000042 Logit Scale: 28.379 Contrastive_loss: 0.0057115 (0.075723) Loss: 0.0057115 (0.075723) 2025-03-20,01:00:59 | INFO | Train Epoch: 20 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.214, 149.633/s, 149.633/s/gpu LR: 0.000042 Logit Scale: 28.366 Contrastive_loss: 0.14420 (0.076008) Loss: 0.14420 (0.076008) 2025-03-20,01:01:07 | INFO | Train Epoch: 20 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.214, 152.424/s, 152.424/s/gpu LR: 0.000042 Logit Scale: 28.362 Contrastive_loss: 0.092861 (0.076078) Loss: 0.092861 (0.076078) 2025-03-20,01:01:07 | INFO | Eval Epoch: 21 [32 / 7443] Clip Loss: 3.417885 2025-03-20,01:01:13 | INFO | Eval Epoch: 21 [3232 / 7443] Clip Loss: 0.833722 2025-03-20,01:01:19 | INFO | Eval Epoch: 21 [6432 / 7443] Clip Loss: 0.637082 2025-03-20,01:01:22 | INFO | Eval Epoch: 21 image_to_text_mean_rank: 83.1464 image_to_text_median_rank: 5.0000 image_to_text_R@1: 0.1710 image_to_text_R@5: 0.5084 image_to_text_R@10: 0.6786 text_to_image_mean_rank: 50.8099 text_to_image_median_rank: 6.0000 text_to_image_R@1: 0.1690 text_to_image_R@5: 0.4944 text_to_image_R@10: 0.6707 clip_val_loss: 0.5921 epoch: 21.0000 num_samples: 7443.0000 2025-03-20,01:01:55 | INFO | Start epoch 21 2025-03-20,01:01:55 | INFO | Train Epoch: 21 [ 32/766009 (0%)] Data (t): 0.178 Batch (t): 0.382, 83.8498/s, 83.8498/s/gpu LR: 0.000042 Logit Scale: 28.362 Contrastive_loss: 0.057705 (0.057705) Loss: 0.057705 (0.057705) 2025-03-20,01:02:17 | INFO | Train Epoch: 21 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.216, 149.193/s, 149.193/s/gpu LR: 0.000042 Logit Scale: 28.376 Contrastive_loss: 0.038715 (0.048210) Loss: 0.038715 (0.048210) 2025-03-20,01:02:38 | INFO | Train Epoch: 21 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 148.861/s, 148.861/s/gpu LR: 0.000042 Logit Scale: 28.386 Contrastive_loss: 0.023221 (0.039880) Loss: 0.023221 (0.039880) 2025-03-20,01:03:00 | INFO | Train Epoch: 21 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 149.304/s, 149.304/s/gpu LR: 0.000042 Logit Scale: 28.406 Contrastive_loss: 0.10263 (0.055567) Loss: 0.10263 (0.055567) 2025-03-20,01:03:21 | INFO | Train Epoch: 21 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 149.105/s, 149.105/s/gpu LR: 0.000042 Logit Scale: 28.410 Contrastive_loss: 0.058422 (0.056138) Loss: 0.058422 (0.056138) 2025-03-20,01:03:43 | INFO | Train Epoch: 21 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 148.909/s, 148.909/s/gpu LR: 0.000042 Logit Scale: 28.403 Contrastive_loss: 0.015785 (0.049412) Loss: 0.015785 (0.049412) 2025-03-20,01:04:05 | INFO | Train Epoch: 21 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.036/s, 149.036/s/gpu LR: 0.000042 Logit Scale: 28.422 Contrastive_loss: 0.012671 (0.044163) Loss: 0.012671 (0.044163) 2025-03-20,01:04:26 | INFO | Train Epoch: 21 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.025/s, 149.025/s/gpu LR: 0.000042 Logit Scale: 28.430 Contrastive_loss: 0.13595 (0.055637) Loss: 0.13595 (0.055637) 2025-03-20,01:04:48 | INFO | Train Epoch: 21 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.690/s, 149.690/s/gpu LR: 0.000042 Logit Scale: 28.434 Contrastive_loss: 0.077397 (0.058054) Loss: 0.077397 (0.058054) 2025-03-20,01:05:09 | INFO | Train Epoch: 21 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 148.977/s, 148.977/s/gpu LR: 0.000042 Logit Scale: 28.442 Contrastive_loss: 0.0063471 (0.052884) Loss: 0.0063471 (0.052884) 2025-03-20,01:05:31 | INFO | Train Epoch: 21 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 149.342/s, 149.342/s/gpu LR: 0.000042 Logit Scale: 28.455 Contrastive_loss: 0.019869 (0.049882) Loss: 0.019869 (0.049882) 2025-03-20,01:05:52 | INFO | Train Epoch: 21 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 148.698/s, 148.698/s/gpu LR: 0.000042 Logit Scale: 28.459 Contrastive_loss: 0.0057667 (0.046206) Loss: 0.0057667 (0.046206) 2025-03-20,01:06:14 | INFO | Train Epoch: 21 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 150.183/s, 150.183/s/gpu LR: 0.000042 Logit Scale: 28.469 Contrastive_loss: 0.076882 (0.048566) Loss: 0.076882 (0.048566) 2025-03-20,01:06:35 | INFO | Train Epoch: 21 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.009/s, 149.009/s/gpu LR: 0.000042 Logit Scale: 28.482 Contrastive_loss: 0.052405 (0.048840) Loss: 0.052405 (0.048840) 2025-03-20,01:06:57 | INFO | Train Epoch: 21 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.184/s, 148.184/s/gpu LR: 0.000042 Logit Scale: 28.469 Contrastive_loss: 0.0068338 (0.046040) Loss: 0.0068338 (0.046040) 2025-03-20,01:07:18 | INFO | Train Epoch: 21 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.465/s, 148.465/s/gpu LR: 0.000042 Logit Scale: 28.465 Contrastive_loss: 0.060439 (0.046940) Loss: 0.060439 (0.046940) 2025-03-20,01:07:40 | INFO | Train Epoch: 21 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 149.804/s, 149.804/s/gpu LR: 0.000042 Logit Scale: 28.477 Contrastive_loss: 0.00054776 (0.044211) Loss: 0.00054776 (0.044211) 2025-03-20,01:08:01 | INFO | Train Epoch: 21 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 150.084/s, 150.084/s/gpu LR: 0.000042 Logit Scale: 28.459 Contrastive_loss: 0.091829 (0.046856) Loss: 0.091829 (0.046856) 2025-03-20,01:08:23 | INFO | Train Epoch: 21 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.120/s, 148.120/s/gpu LR: 0.000042 Logit Scale: 28.442 Contrastive_loss: 0.0019774 (0.044494) Loss: 0.0019774 (0.044494) 2025-03-20,01:08:44 | INFO | Train Epoch: 21 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.184/s, 148.184/s/gpu LR: 0.000042 Logit Scale: 28.456 Contrastive_loss: 0.071887 (0.045864) Loss: 0.071887 (0.045864) 2025-03-20,01:09:06 | INFO | Train Epoch: 21 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.942/s, 149.942/s/gpu LR: 0.000042 Logit Scale: 28.457 Contrastive_loss: 0.050081 (0.046064) Loss: 0.050081 (0.046064) 2025-03-20,01:09:27 | INFO | Train Epoch: 21 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 149.578/s, 149.578/s/gpu LR: 0.000042 Logit Scale: 28.446 Contrastive_loss: 0.22103 (0.054017) Loss: 0.22103 (0.054017) 2025-03-20,01:09:49 | INFO | Train Epoch: 21 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 149.398/s, 149.398/s/gpu LR: 0.000042 Logit Scale: 28.449 Contrastive_loss: 0.14784 (0.058097) Loss: 0.14784 (0.058097) 2025-03-20,01:10:10 | INFO | Train Epoch: 21 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 148.149/s, 148.149/s/gpu LR: 0.000041 Logit Scale: 28.449 Contrastive_loss: 0.054865 (0.057962) Loss: 0.054865 (0.057962) 2025-03-20,01:10:32 | INFO | Train Epoch: 21 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 147.551/s, 147.551/s/gpu LR: 0.000041 Logit Scale: 28.453 Contrastive_loss: 0.029745 (0.056833) Loss: 0.029745 (0.056833) 2025-03-20,01:10:54 | INFO | Train Epoch: 21 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.218, 147.928/s, 147.928/s/gpu LR: 0.000041 Logit Scale: 28.459 Contrastive_loss: 0.077598 (0.057632) Loss: 0.077598 (0.057632) 2025-03-20,01:11:15 | INFO | Train Epoch: 21 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 146.569/s, 146.569/s/gpu LR: 0.000041 Logit Scale: 28.466 Contrastive_loss: 0.021031 (0.056276) Loss: 0.021031 (0.056276) 2025-03-20,01:11:37 | INFO | Train Epoch: 21 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.218, 148.849/s, 148.849/s/gpu LR: 0.000041 Logit Scale: 28.423 Contrastive_loss: 0.053172 (0.056165) Loss: 0.053172 (0.056165) 2025-03-20,01:11:59 | INFO | Train Epoch: 21 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 142.818/s, 142.818/s/gpu LR: 0.000041 Logit Scale: 28.432 Contrastive_loss: 0.066698 (0.056529) Loss: 0.066698 (0.056529) 2025-03-20,01:12:20 | INFO | Train Epoch: 21 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 148.029/s, 148.029/s/gpu LR: 0.000041 Logit Scale: 28.442 Contrastive_loss: 0.010991 (0.055011) Loss: 0.010991 (0.055011) 2025-03-20,01:12:42 | INFO | Train Epoch: 21 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.218, 147.474/s, 147.474/s/gpu LR: 0.000041 Logit Scale: 28.456 Contrastive_loss: 0.030344 (0.054215) Loss: 0.030344 (0.054215) 2025-03-20,01:13:04 | INFO | Train Epoch: 21 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.648/s, 148.648/s/gpu LR: 0.000041 Logit Scale: 28.463 Contrastive_loss: 0.0051552 (0.052682) Loss: 0.0051552 (0.052682) 2025-03-20,01:13:25 | INFO | Train Epoch: 21 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 149.587/s, 149.587/s/gpu LR: 0.000041 Logit Scale: 28.459 Contrastive_loss: 0.10104 (0.054147) Loss: 0.10104 (0.054147) 2025-03-20,01:13:47 | INFO | Train Epoch: 21 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 149.026/s, 149.026/s/gpu LR: 0.000041 Logit Scale: 28.460 Contrastive_loss: 0.015903 (0.053022) Loss: 0.015903 (0.053022) 2025-03-20,01:14:08 | INFO | Train Epoch: 21 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 149.850/s, 149.850/s/gpu LR: 0.000041 Logit Scale: 28.460 Contrastive_loss: 0.099143 (0.054340) Loss: 0.099143 (0.054340) 2025-03-20,01:14:29 | INFO | Train Epoch: 21 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 146.916/s, 146.916/s/gpu LR: 0.000041 Logit Scale: 28.462 Contrastive_loss: 0.059693 (0.054489) Loss: 0.059693 (0.054489) 2025-03-20,01:14:51 | INFO | Train Epoch: 21 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 148.280/s, 148.280/s/gpu LR: 0.000041 Logit Scale: 28.449 Contrastive_loss: 0.091648 (0.055493) Loss: 0.091648 (0.055493) 2025-03-20,01:15:13 | INFO | Train Epoch: 21 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.756/s, 149.756/s/gpu LR: 0.000041 Logit Scale: 28.464 Contrastive_loss: 0.024165 (0.054669) Loss: 0.024165 (0.054669) 2025-03-20,01:15:34 | INFO | Train Epoch: 21 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.214, 150.073/s, 150.073/s/gpu LR: 0.000041 Logit Scale: 28.485 Contrastive_loss: 0.00067789 (0.053284) Loss: 0.00067789 (0.053284) 2025-03-20,01:15:56 | INFO | Train Epoch: 21 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 148.525/s, 148.525/s/gpu LR: 0.000041 Logit Scale: 28.482 Contrastive_loss: 0.11495 (0.054826) Loss: 0.11495 (0.054826) 2025-03-20,01:16:17 | INFO | Train Epoch: 21 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 149.137/s, 149.137/s/gpu LR: 0.000041 Logit Scale: 28.483 Contrastive_loss: 0.064349 (0.055058) Loss: 0.064349 (0.055058) 2025-03-20,01:16:39 | INFO | Train Epoch: 21 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 148.376/s, 148.376/s/gpu LR: 0.000041 Logit Scale: 28.500 Contrastive_loss: 0.055833 (0.055077) Loss: 0.055833 (0.055077) 2025-03-20,01:17:00 | INFO | Train Epoch: 21 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 148.788/s, 148.788/s/gpu LR: 0.000041 Logit Scale: 28.519 Contrastive_loss: 0.090805 (0.055908) Loss: 0.090805 (0.055908) 2025-03-20,01:17:22 | INFO | Train Epoch: 21 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.590/s, 149.590/s/gpu LR: 0.000041 Logit Scale: 28.526 Contrastive_loss: 0.040061 (0.055547) Loss: 0.040061 (0.055547) 2025-03-20,01:17:43 | INFO | Train Epoch: 21 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.245/s, 149.245/s/gpu LR: 0.000041 Logit Scale: 28.515 Contrastive_loss: 0.0027814 (0.054375) Loss: 0.0027814 (0.054375) 2025-03-20,01:18:05 | INFO | Train Epoch: 21 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.217, 149.447/s, 149.447/s/gpu LR: 0.000041 Logit Scale: 28.532 Contrastive_loss: 0.083689 (0.055012) Loss: 0.083689 (0.055012) 2025-03-20,01:18:26 | INFO | Train Epoch: 21 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 149.238/s, 149.238/s/gpu LR: 0.000041 Logit Scale: 28.540 Contrastive_loss: 0.091814 (0.055795) Loss: 0.091814 (0.055795) 2025-03-20,01:18:48 | INFO | Train Epoch: 21 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.925/s, 149.925/s/gpu LR: 0.000041 Logit Scale: 28.542 Contrastive_loss: 0.012291 (0.054889) Loss: 0.012291 (0.054889) 2025-03-20,01:19:09 | INFO | Train Epoch: 21 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 148.678/s, 148.678/s/gpu LR: 0.000041 Logit Scale: 28.532 Contrastive_loss: 0.097430 (0.055757) Loss: 0.097430 (0.055757) 2025-03-20,01:19:31 | INFO | Train Epoch: 21 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.254/s, 149.254/s/gpu LR: 0.000041 Logit Scale: 28.535 Contrastive_loss: 0.0013904 (0.054670) Loss: 0.0013904 (0.054670) 2025-03-20,01:19:53 | INFO | Train Epoch: 21 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 152.170/s, 152.170/s/gpu LR: 0.000041 Logit Scale: 28.523 Contrastive_loss: 0.087814 (0.055320) Loss: 0.087814 (0.055320) 2025-03-20,01:20:14 | INFO | Train Epoch: 21 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.212, 151.867/s, 151.867/s/gpu LR: 0.000040 Logit Scale: 28.512 Contrastive_loss: 0.0029267 (0.054312) Loss: 0.0029267 (0.054312) 2025-03-20,01:20:35 | INFO | Train Epoch: 21 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 150.995/s, 150.995/s/gpu LR: 0.000040 Logit Scale: 28.520 Contrastive_loss: 0.11907 (0.055534) Loss: 0.11907 (0.055534) 2025-03-20,01:20:57 | INFO | Train Epoch: 21 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 148.841/s, 148.841/s/gpu LR: 0.000040 Logit Scale: 28.512 Contrastive_loss: 0.020021 (0.054876) Loss: 0.020021 (0.054876) 2025-03-20,01:21:18 | INFO | Train Epoch: 21 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.886/s, 149.886/s/gpu LR: 0.000040 Logit Scale: 28.520 Contrastive_loss: 0.0053680 (0.053976) Loss: 0.0053680 (0.053976) 2025-03-20,01:21:39 | INFO | Train Epoch: 21 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 148.700/s, 148.700/s/gpu LR: 0.000040 Logit Scale: 28.488 Contrastive_loss: 0.11601 (0.055084) Loss: 0.11601 (0.055084) 2025-03-20,01:22:01 | INFO | Train Epoch: 21 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 146.897/s, 146.897/s/gpu LR: 0.000040 Logit Scale: 28.499 Contrastive_loss: 0.059585 (0.055163) Loss: 0.059585 (0.055163) 2025-03-20,01:22:23 | INFO | Train Epoch: 21 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 150.713/s, 150.713/s/gpu LR: 0.000040 Logit Scale: 28.512 Contrastive_loss: 0.068439 (0.055392) Loss: 0.068439 (0.055392) 2025-03-20,01:22:44 | INFO | Train Epoch: 21 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.213, 150.526/s, 150.526/s/gpu LR: 0.000040 Logit Scale: 28.511 Contrastive_loss: 0.039643 (0.055125) Loss: 0.039643 (0.055125) 2025-03-20,01:23:05 | INFO | Train Epoch: 21 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 151.223/s, 151.223/s/gpu LR: 0.000040 Logit Scale: 28.508 Contrastive_loss: 0.0044917 (0.054281) Loss: 0.0044917 (0.054281) 2025-03-20,01:23:27 | INFO | Train Epoch: 21 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 145.006/s, 145.006/s/gpu LR: 0.000040 Logit Scale: 28.509 Contrastive_loss: 0.0073083 (0.053511) Loss: 0.0073083 (0.053511) 2025-03-20,01:23:48 | INFO | Train Epoch: 21 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 149.984/s, 149.984/s/gpu LR: 0.000040 Logit Scale: 28.528 Contrastive_loss: 0.040746 (0.053305) Loss: 0.040746 (0.053305) 2025-03-20,01:24:10 | INFO | Train Epoch: 21 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 149.438/s, 149.438/s/gpu LR: 0.000040 Logit Scale: 28.525 Contrastive_loss: 0.070888 (0.053584) Loss: 0.070888 (0.053584) 2025-03-20,01:24:31 | INFO | Train Epoch: 21 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 150.353/s, 150.353/s/gpu LR: 0.000040 Logit Scale: 28.545 Contrastive_loss: 0.14978 (0.055087) Loss: 0.14978 (0.055087) 2025-03-20,01:24:53 | INFO | Train Epoch: 21 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 149.659/s, 149.659/s/gpu LR: 0.000040 Logit Scale: 28.540 Contrastive_loss: 0.11281 (0.055975) Loss: 0.11281 (0.055975) 2025-03-20,01:25:14 | INFO | Train Epoch: 21 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 148.688/s, 148.688/s/gpu LR: 0.000040 Logit Scale: 28.539 Contrastive_loss: 0.060841 (0.056049) Loss: 0.060841 (0.056049) 2025-03-20,01:25:36 | INFO | Train Epoch: 21 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.212, 151.097/s, 151.097/s/gpu LR: 0.000040 Logit Scale: 28.543 Contrastive_loss: 0.041528 (0.055832) Loss: 0.041528 (0.055832) 2025-03-20,01:25:57 | INFO | Train Epoch: 21 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 150.365/s, 150.365/s/gpu LR: 0.000040 Logit Scale: 28.550 Contrastive_loss: 0.058800 (0.055876) Loss: 0.058800 (0.055876) 2025-03-20,01:26:19 | INFO | Train Epoch: 21 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 145.787/s, 145.787/s/gpu LR: 0.000040 Logit Scale: 28.562 Contrastive_loss: 0.10733 (0.056621) Loss: 0.10733 (0.056621) 2025-03-20,01:26:40 | INFO | Train Epoch: 21 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 151.637/s, 151.637/s/gpu LR: 0.000040 Logit Scale: 28.546 Contrastive_loss: 0.093948 (0.057155) Loss: 0.093948 (0.057155) 2025-03-20,01:27:01 | INFO | Train Epoch: 21 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.212, 146.996/s, 146.996/s/gpu LR: 0.000040 Logit Scale: 28.559 Contrastive_loss: 0.051891 (0.057081) Loss: 0.051891 (0.057081) 2025-03-20,01:27:23 | INFO | Train Epoch: 21 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 151.685/s, 151.685/s/gpu LR: 0.000040 Logit Scale: 28.560 Contrastive_loss: 0.0066258 (0.056380) Loss: 0.0066258 (0.056380) 2025-03-20,01:27:44 | INFO | Train Epoch: 21 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.212, 150.629/s, 150.629/s/gpu LR: 0.000040 Logit Scale: 28.565 Contrastive_loss: 0.041422 (0.056175) Loss: 0.041422 (0.056175) 2025-03-20,01:28:06 | INFO | Train Epoch: 21 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 136.308/s, 136.308/s/gpu LR: 0.000040 Logit Scale: 28.566 Contrastive_loss: 0.060494 (0.056233) Loss: 0.060494 (0.056233) 2025-03-20,01:28:27 | INFO | Train Epoch: 21 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 147.618/s, 147.618/s/gpu LR: 0.000040 Logit Scale: 28.570 Contrastive_loss: 0.076887 (0.056509) Loss: 0.076887 (0.056509) 2025-03-20,01:28:49 | INFO | Train Epoch: 21 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 131.084/s, 131.084/s/gpu LR: 0.000040 Logit Scale: 28.576 Contrastive_loss: 0.071288 (0.056703) Loss: 0.071288 (0.056703) 2025-03-20,01:29:11 | INFO | Train Epoch: 21 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.219, 136.285/s, 136.285/s/gpu LR: 0.000040 Logit Scale: 28.580 Contrastive_loss: 0.022804 (0.056263) Loss: 0.022804 (0.056263) 2025-03-20,01:29:33 | INFO | Train Epoch: 21 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.220, 147.572/s, 147.572/s/gpu LR: 0.000040 Logit Scale: 28.577 Contrastive_loss: 0.015578 (0.055741) Loss: 0.015578 (0.055741) 2025-03-20,01:29:55 | INFO | Train Epoch: 21 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.924/s, 149.924/s/gpu LR: 0.000040 Logit Scale: 28.570 Contrastive_loss: 0.063309 (0.055837) Loss: 0.063309 (0.055837) 2025-03-20,01:30:17 | INFO | Train Epoch: 21 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.221, 148.309/s, 148.309/s/gpu LR: 0.000039 Logit Scale: 28.562 Contrastive_loss: 0.024928 (0.055451) Loss: 0.024928 (0.055451) 2025-03-20,01:30:38 | INFO | Train Epoch: 21 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 151.871/s, 151.871/s/gpu LR: 0.000039 Logit Scale: 28.584 Contrastive_loss: 0.10296 (0.056037) Loss: 0.10296 (0.056037) 2025-03-20,01:31:00 | INFO | Train Epoch: 21 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 147.035/s, 147.035/s/gpu LR: 0.000039 Logit Scale: 28.603 Contrastive_loss: 0.077398 (0.056298) Loss: 0.077398 (0.056298) 2025-03-20,01:31:21 | INFO | Train Epoch: 21 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 146.602/s, 146.602/s/gpu LR: 0.000039 Logit Scale: 28.599 Contrastive_loss: 0.017356 (0.055828) Loss: 0.017356 (0.055828) 2025-03-20,01:31:43 | INFO | Train Epoch: 21 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 151.308/s, 151.308/s/gpu LR: 0.000039 Logit Scale: 28.598 Contrastive_loss: 0.027822 (0.055495) Loss: 0.027822 (0.055495) 2025-03-20,01:32:04 | INFO | Train Epoch: 21 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.213, 146.799/s, 146.799/s/gpu LR: 0.000039 Logit Scale: 28.596 Contrastive_loss: 0.053847 (0.055476) Loss: 0.053847 (0.055476) 2025-03-20,01:32:26 | INFO | Train Epoch: 21 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.691/s, 149.691/s/gpu LR: 0.000039 Logit Scale: 28.599 Contrastive_loss: 0.047544 (0.055383) Loss: 0.047544 (0.055383) 2025-03-20,01:32:47 | INFO | Train Epoch: 21 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.991/s, 149.991/s/gpu LR: 0.000039 Logit Scale: 28.602 Contrastive_loss: 0.048861 (0.055308) Loss: 0.048861 (0.055308) 2025-03-20,01:33:08 | INFO | Train Epoch: 21 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 147.139/s, 147.139/s/gpu LR: 0.000039 Logit Scale: 28.611 Contrastive_loss: 0.11104 (0.055942) Loss: 0.11104 (0.055942) 2025-03-20,01:33:30 | INFO | Train Epoch: 21 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 148.879/s, 148.879/s/gpu LR: 0.000039 Logit Scale: 28.590 Contrastive_loss: 0.12515 (0.056719) Loss: 0.12515 (0.056719) 2025-03-20,01:33:51 | INFO | Train Epoch: 21 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 150.396/s, 150.396/s/gpu LR: 0.000039 Logit Scale: 28.570 Contrastive_loss: 0.00056779 (0.056095) Loss: 0.00056779 (0.056095) 2025-03-20,01:34:13 | INFO | Train Epoch: 21 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.213, 149.204/s, 149.204/s/gpu LR: 0.000039 Logit Scale: 28.575 Contrastive_loss: 0.065844 (0.056203) Loss: 0.065844 (0.056203) 2025-03-20,01:34:34 | INFO | Train Epoch: 21 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.438/s, 148.438/s/gpu LR: 0.000039 Logit Scale: 28.594 Contrastive_loss: 0.032677 (0.055947) Loss: 0.032677 (0.055947) 2025-03-20,01:34:56 | INFO | Train Epoch: 21 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 148.325/s, 148.325/s/gpu LR: 0.000039 Logit Scale: 28.601 Contrastive_loss: 0.062146 (0.056013) Loss: 0.062146 (0.056013) 2025-03-20,01:35:17 | INFO | Train Epoch: 21 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.213, 152.091/s, 152.091/s/gpu LR: 0.000039 Logit Scale: 28.586 Contrastive_loss: 0.047186 (0.055920) Loss: 0.047186 (0.055920) 2025-03-20,01:35:38 | INFO | Train Epoch: 21 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.998/s, 148.998/s/gpu LR: 0.000039 Logit Scale: 28.571 Contrastive_loss: 0.046087 (0.055816) Loss: 0.046087 (0.055816) 2025-03-20,01:36:00 | INFO | Train Epoch: 21 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 147.617/s, 147.617/s/gpu LR: 0.000039 Logit Scale: 28.571 Contrastive_loss: 0.056775 (0.055826) Loss: 0.056775 (0.055826) 2025-03-20,01:36:21 | INFO | Train Epoch: 21 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 148.355/s, 148.355/s/gpu LR: 0.000039 Logit Scale: 28.587 Contrastive_loss: 0.053219 (0.055799) Loss: 0.053219 (0.055799) 2025-03-20,01:36:43 | INFO | Train Epoch: 21 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 152.782/s, 152.782/s/gpu LR: 0.000039 Logit Scale: 28.599 Contrastive_loss: 0.033012 (0.055567) Loss: 0.033012 (0.055567) 2025-03-20,01:37:05 | INFO | Train Epoch: 21 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 150.556/s, 150.556/s/gpu LR: 0.000039 Logit Scale: 28.606 Contrastive_loss: 0.011211 (0.055119) Loss: 0.011211 (0.055119) 2025-03-20,01:37:26 | INFO | Train Epoch: 21 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 149.551/s, 149.551/s/gpu LR: 0.000039 Logit Scale: 28.609 Contrastive_loss: 0.0035505 (0.054603) Loss: 0.0035505 (0.054603) 2025-03-20,01:37:47 | INFO | Train Epoch: 21 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.567/s, 149.567/s/gpu LR: 0.000039 Logit Scale: 28.610 Contrastive_loss: 0.0067634 (0.054129) Loss: 0.0067634 (0.054129) 2025-03-20,01:38:09 | INFO | Train Epoch: 21 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 151.474/s, 151.474/s/gpu LR: 0.000039 Logit Scale: 28.605 Contrastive_loss: 0.0059477 (0.053657) Loss: 0.0059477 (0.053657) 2025-03-20,01:38:30 | INFO | Train Epoch: 21 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 148.871/s, 148.871/s/gpu LR: 0.000039 Logit Scale: 28.606 Contrastive_loss: 0.12573 (0.054357) Loss: 0.12573 (0.054357) 2025-03-20,01:38:51 | INFO | Train Epoch: 21 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.211, 151.242/s, 151.242/s/gpu LR: 0.000039 Logit Scale: 28.610 Contrastive_loss: 0.054536 (0.054358) Loss: 0.054536 (0.054358) 2025-03-20,01:39:12 | INFO | Train Epoch: 21 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.211, 151.452/s, 151.452/s/gpu LR: 0.000039 Logit Scale: 28.599 Contrastive_loss: 0.031996 (0.054145) Loss: 0.031996 (0.054145) 2025-03-20,01:39:34 | INFO | Train Epoch: 21 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 149.380/s, 149.380/s/gpu LR: 0.000039 Logit Scale: 28.616 Contrastive_loss: 0.023883 (0.053860) Loss: 0.023883 (0.053860) 2025-03-20,01:39:55 | INFO | Train Epoch: 21 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 148.555/s, 148.555/s/gpu LR: 0.000039 Logit Scale: 28.626 Contrastive_loss: 0.081749 (0.054121) Loss: 0.081749 (0.054121) 2025-03-20,01:40:17 | INFO | Train Epoch: 21 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 150.078/s, 150.078/s/gpu LR: 0.000038 Logit Scale: 28.636 Contrastive_loss: 0.059006 (0.054166) Loss: 0.059006 (0.054166) 2025-03-20,01:40:38 | INFO | Train Epoch: 21 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.083/s, 149.083/s/gpu LR: 0.000038 Logit Scale: 28.615 Contrastive_loss: 0.020508 (0.053857) Loss: 0.020508 (0.053857) 2025-03-20,01:41:00 | INFO | Train Epoch: 21 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 148.979/s, 148.979/s/gpu LR: 0.000038 Logit Scale: 28.618 Contrastive_loss: 0.0019398 (0.053385) Loss: 0.0019398 (0.053385) 2025-03-20,01:41:21 | INFO | Train Epoch: 21 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 147.112/s, 147.112/s/gpu LR: 0.000038 Logit Scale: 28.629 Contrastive_loss: 0.049869 (0.053353) Loss: 0.049869 (0.053353) 2025-03-20,01:41:43 | INFO | Train Epoch: 21 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.213, 133.589/s, 133.589/s/gpu LR: 0.000038 Logit Scale: 28.621 Contrastive_loss: 0.011145 (0.052976) Loss: 0.011145 (0.052976) 2025-03-20,01:42:04 | INFO | Train Epoch: 21 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 149.146/s, 149.146/s/gpu LR: 0.000038 Logit Scale: 28.633 Contrastive_loss: 0.080289 (0.053218) Loss: 0.080289 (0.053218) 2025-03-20,01:42:26 | INFO | Train Epoch: 21 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 150.226/s, 150.226/s/gpu LR: 0.000038 Logit Scale: 28.639 Contrastive_loss: 0.055905 (0.053242) Loss: 0.055905 (0.053242) 2025-03-20,01:42:47 | INFO | Train Epoch: 21 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 150.278/s, 150.278/s/gpu LR: 0.000038 Logit Scale: 28.654 Contrastive_loss: 0.10349 (0.053679) Loss: 0.10349 (0.053679) 2025-03-20,01:43:08 | INFO | Train Epoch: 21 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 149.760/s, 149.760/s/gpu LR: 0.000038 Logit Scale: 28.662 Contrastive_loss: 0.0064826 (0.053272) Loss: 0.0064826 (0.053272) 2025-03-20,01:43:30 | INFO | Train Epoch: 21 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 149.975/s, 149.975/s/gpu LR: 0.000038 Logit Scale: 28.664 Contrastive_loss: 0.029832 (0.053071) Loss: 0.029832 (0.053071) 2025-03-20,01:43:51 | INFO | Train Epoch: 21 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 147.653/s, 147.653/s/gpu LR: 0.000038 Logit Scale: 28.661 Contrastive_loss: 0.045570 (0.053008) Loss: 0.045570 (0.053008) 2025-03-20,01:44:13 | INFO | Train Epoch: 21 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 150.689/s, 150.689/s/gpu LR: 0.000038 Logit Scale: 28.659 Contrastive_loss: 0.057049 (0.053042) Loss: 0.057049 (0.053042) 2025-03-20,01:44:35 | INFO | Train Epoch: 21 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.219, 144.075/s, 144.075/s/gpu LR: 0.000038 Logit Scale: 28.667 Contrastive_loss: 0.026954 (0.052824) Loss: 0.026954 (0.052824) 2025-03-20,01:44:57 | INFO | Train Epoch: 21 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.223, 141.853/s, 141.853/s/gpu LR: 0.000038 Logit Scale: 28.655 Contrastive_loss: 0.050617 (0.052806) Loss: 0.050617 (0.052806) 2025-03-20,01:45:19 | INFO | Train Epoch: 21 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.220, 145.699/s, 145.699/s/gpu LR: 0.000038 Logit Scale: 28.654 Contrastive_loss: 0.022991 (0.052562) Loss: 0.022991 (0.052562) 2025-03-20,01:45:41 | INFO | Train Epoch: 21 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.220, 132.827/s, 132.827/s/gpu LR: 0.000038 Logit Scale: 28.638 Contrastive_loss: 0.056736 (0.052596) Loss: 0.056736 (0.052596) 2025-03-20,01:46:03 | INFO | Train Epoch: 21 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 148.766/s, 148.766/s/gpu LR: 0.000038 Logit Scale: 28.639 Contrastive_loss: 0.039688 (0.052492) Loss: 0.039688 (0.052492) 2025-03-20,01:46:25 | INFO | Train Epoch: 21 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.218, 147.587/s, 147.587/s/gpu LR: 0.000038 Logit Scale: 28.631 Contrastive_loss: 0.12715 (0.053089) Loss: 0.12715 (0.053089) 2025-03-20,01:46:46 | INFO | Train Epoch: 21 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 149.230/s, 149.230/s/gpu LR: 0.000038 Logit Scale: 28.618 Contrastive_loss: 0.072584 (0.053244) Loss: 0.072584 (0.053244) 2025-03-20,01:47:08 | INFO | Train Epoch: 21 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 150.005/s, 150.005/s/gpu LR: 0.000038 Logit Scale: 28.613 Contrastive_loss: 0.064628 (0.053333) Loss: 0.064628 (0.053333) 2025-03-20,01:47:29 | INFO | Train Epoch: 21 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 145.641/s, 145.641/s/gpu LR: 0.000038 Logit Scale: 28.627 Contrastive_loss: 0.081258 (0.053551) Loss: 0.081258 (0.053551) 2025-03-20,01:47:51 | INFO | Train Epoch: 21 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 139.074/s, 139.074/s/gpu LR: 0.000038 Logit Scale: 28.632 Contrastive_loss: 0.054970 (0.053562) Loss: 0.054970 (0.053562) 2025-03-20,01:48:13 | INFO | Train Epoch: 21 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.219, 147.070/s, 147.070/s/gpu LR: 0.000038 Logit Scale: 28.610 Contrastive_loss: 0.14734 (0.054284) Loss: 0.14734 (0.054284) 2025-03-20,01:48:35 | INFO | Train Epoch: 21 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.219, 145.216/s, 145.216/s/gpu LR: 0.000038 Logit Scale: 28.616 Contrastive_loss: 0.093493 (0.054583) Loss: 0.093493 (0.054583) 2025-03-20,01:48:57 | INFO | Train Epoch: 21 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.218, 149.150/s, 149.150/s/gpu LR: 0.000038 Logit Scale: 28.619 Contrastive_loss: 0.075861 (0.054744) Loss: 0.075861 (0.054744) 2025-03-20,01:49:18 | INFO | Train Epoch: 21 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 148.536/s, 148.536/s/gpu LR: 0.000038 Logit Scale: 28.632 Contrastive_loss: 0.047047 (0.054686) Loss: 0.047047 (0.054686) 2025-03-20,01:49:40 | INFO | Train Epoch: 21 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 148.913/s, 148.913/s/gpu LR: 0.000038 Logit Scale: 28.630 Contrastive_loss: 0.027087 (0.054480) Loss: 0.027087 (0.054480) 2025-03-20,01:50:01 | INFO | Train Epoch: 21 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 149.091/s, 149.091/s/gpu LR: 0.000038 Logit Scale: 28.657 Contrastive_loss: 0.043896 (0.054402) Loss: 0.043896 (0.054402) 2025-03-20,01:50:23 | INFO | Train Epoch: 21 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 149.321/s, 149.321/s/gpu LR: 0.000038 Logit Scale: 28.646 Contrastive_loss: 0.0021781 (0.054018) Loss: 0.0021781 (0.054018) 2025-03-20,01:50:44 | INFO | Train Epoch: 21 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 149.790/s, 149.790/s/gpu LR: 0.000037 Logit Scale: 28.637 Contrastive_loss: 0.12587 (0.054543) Loss: 0.12587 (0.054543) 2025-03-20,01:51:06 | INFO | Train Epoch: 21 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 149.880/s, 149.880/s/gpu LR: 0.000037 Logit Scale: 28.634 Contrastive_loss: 0.16486 (0.055342) Loss: 0.16486 (0.055342) 2025-03-20,01:51:27 | INFO | Train Epoch: 21 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 150.300/s, 150.300/s/gpu LR: 0.000037 Logit Scale: 28.647 Contrastive_loss: 0.046524 (0.055278) Loss: 0.046524 (0.055278) 2025-03-20,01:51:48 | INFO | Train Epoch: 21 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 150.364/s, 150.364/s/gpu LR: 0.000037 Logit Scale: 28.634 Contrastive_loss: 0.13669 (0.055860) Loss: 0.13669 (0.055860) 2025-03-20,01:52:10 | INFO | Train Epoch: 21 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.213, 151.326/s, 151.326/s/gpu LR: 0.000037 Logit Scale: 28.633 Contrastive_loss: 0.019331 (0.055601) Loss: 0.019331 (0.055601) 2025-03-20,01:52:31 | INFO | Train Epoch: 21 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 149.395/s, 149.395/s/gpu LR: 0.000037 Logit Scale: 28.643 Contrastive_loss: 0.077505 (0.055755) Loss: 0.077505 (0.055755) 2025-03-20,01:52:53 | INFO | Train Epoch: 21 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.214, 154.759/s, 154.759/s/gpu LR: 0.000037 Logit Scale: 28.650 Contrastive_loss: 0.091929 (0.056008) Loss: 0.091929 (0.056008) 2025-03-20,01:53:14 | INFO | Train Epoch: 21 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.213, 151.842/s, 151.842/s/gpu LR: 0.000037 Logit Scale: 28.658 Contrastive_loss: 0.13649 (0.056567) Loss: 0.13649 (0.056567) 2025-03-20,01:53:35 | INFO | Train Epoch: 21 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.212, 150.017/s, 150.017/s/gpu LR: 0.000037 Logit Scale: 28.629 Contrastive_loss: 0.35230 (0.058607) Loss: 0.35230 (0.058607) 2025-03-20,01:53:57 | INFO | Train Epoch: 21 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 147.709/s, 147.709/s/gpu LR: 0.000037 Logit Scale: 28.619 Contrastive_loss: 0.11579 (0.058998) Loss: 0.11579 (0.058998) 2025-03-20,01:54:18 | INFO | Train Epoch: 21 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 150.805/s, 150.805/s/gpu LR: 0.000037 Logit Scale: 28.624 Contrastive_loss: 0.12267 (0.059431) Loss: 0.12267 (0.059431) 2025-03-20,01:54:39 | INFO | Train Epoch: 21 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 147.098/s, 147.098/s/gpu LR: 0.000037 Logit Scale: 28.617 Contrastive_loss: 0.024260 (0.059194) Loss: 0.024260 (0.059194) 2025-03-20,01:55:01 | INFO | Train Epoch: 21 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 149.422/s, 149.422/s/gpu LR: 0.000037 Logit Scale: 28.625 Contrastive_loss: 0.066676 (0.059244) Loss: 0.066676 (0.059244) 2025-03-20,01:55:23 | INFO | Train Epoch: 21 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 150.806/s, 150.806/s/gpu LR: 0.000037 Logit Scale: 28.614 Contrastive_loss: 0.0057219 (0.058887) Loss: 0.0057219 (0.058887) 2025-03-20,01:55:44 | INFO | Train Epoch: 21 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.211, 151.668/s, 151.668/s/gpu LR: 0.000037 Logit Scale: 28.607 Contrastive_loss: 0.062439 (0.058911) Loss: 0.062439 (0.058911) 2025-03-20,01:56:05 | INFO | Train Epoch: 21 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 149.402/s, 149.402/s/gpu LR: 0.000037 Logit Scale: 28.615 Contrastive_loss: 0.018528 (0.058645) Loss: 0.018528 (0.058645) 2025-03-20,01:56:27 | INFO | Train Epoch: 21 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.711/s, 148.711/s/gpu LR: 0.000037 Logit Scale: 28.623 Contrastive_loss: 0.12769 (0.059096) Loss: 0.12769 (0.059096) 2025-03-20,01:56:48 | INFO | Train Epoch: 21 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 148.939/s, 148.939/s/gpu LR: 0.000037 Logit Scale: 28.628 Contrastive_loss: 0.046526 (0.059015) Loss: 0.046526 (0.059015) 2025-03-20,01:57:09 | INFO | Train Epoch: 21 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 148.034/s, 148.034/s/gpu LR: 0.000037 Logit Scale: 28.639 Contrastive_loss: 0.11340 (0.059366) Loss: 0.11340 (0.059366) 2025-03-20,01:57:31 | INFO | Train Epoch: 21 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.806/s, 149.806/s/gpu LR: 0.000037 Logit Scale: 28.638 Contrastive_loss: 0.0023610 (0.059000) Loss: 0.0023610 (0.059000) 2025-03-20,01:57:52 | INFO | Train Epoch: 21 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.214, 149.824/s, 149.824/s/gpu LR: 0.000037 Logit Scale: 28.628 Contrastive_loss: 0.094602 (0.059227) Loss: 0.094602 (0.059227) 2025-03-20,01:58:14 | INFO | Train Epoch: 21 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.498/s, 150.498/s/gpu LR: 0.000037 Logit Scale: 28.625 Contrastive_loss: 0.11732 (0.059595) Loss: 0.11732 (0.059595) 2025-03-20,01:58:35 | INFO | Train Epoch: 21 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 148.833/s, 148.833/s/gpu LR: 0.000037 Logit Scale: 28.628 Contrastive_loss: 0.13031 (0.060039) Loss: 0.13031 (0.060039) 2025-03-20,01:58:57 | INFO | Train Epoch: 21 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.202/s, 150.202/s/gpu LR: 0.000037 Logit Scale: 28.635 Contrastive_loss: 0.060712 (0.060044) Loss: 0.060712 (0.060044) 2025-03-20,01:59:18 | INFO | Train Epoch: 21 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.213, 151.684/s, 151.684/s/gpu LR: 0.000037 Logit Scale: 28.632 Contrastive_loss: 0.082372 (0.060182) Loss: 0.082372 (0.060182) 2025-03-20,01:59:39 | INFO | Train Epoch: 21 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.212, 149.799/s, 149.799/s/gpu LR: 0.000037 Logit Scale: 28.631 Contrastive_loss: 0.018302 (0.059924) Loss: 0.018302 (0.059924) 2025-03-20,02:00:00 | INFO | Train Epoch: 21 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.212, 150.710/s, 150.710/s/gpu LR: 0.000037 Logit Scale: 28.616 Contrastive_loss: 0.27482 (0.061242) Loss: 0.27482 (0.061242) 2025-03-20,02:00:22 | INFO | Train Epoch: 21 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.213, 151.971/s, 151.971/s/gpu LR: 0.000037 Logit Scale: 28.617 Contrastive_loss: 0.10895 (0.061533) Loss: 0.10895 (0.061533) 2025-03-20,02:00:43 | INFO | Train Epoch: 21 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 148.692/s, 148.692/s/gpu LR: 0.000037 Logit Scale: 28.619 Contrastive_loss: 0.029157 (0.061337) Loss: 0.029157 (0.061337) 2025-03-20,02:01:05 | INFO | Train Epoch: 21 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 149.796/s, 149.796/s/gpu LR: 0.000036 Logit Scale: 28.616 Contrastive_loss: 0.0016255 (0.060977) Loss: 0.0016255 (0.060977) 2025-03-20,02:01:26 | INFO | Train Epoch: 21 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 149.888/s, 149.888/s/gpu LR: 0.000036 Logit Scale: 28.602 Contrastive_loss: 0.0095619 (0.060669) Loss: 0.0095619 (0.060669) 2025-03-20,02:01:47 | INFO | Train Epoch: 21 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.213, 149.150/s, 149.150/s/gpu LR: 0.000036 Logit Scale: 28.608 Contrastive_loss: 0.070022 (0.060725) Loss: 0.070022 (0.060725) 2025-03-20,02:02:08 | INFO | Train Epoch: 21 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.212, 151.794/s, 151.794/s/gpu LR: 0.000036 Logit Scale: 28.610 Contrastive_loss: 0.058242 (0.060710) Loss: 0.058242 (0.060710) 2025-03-20,02:02:30 | INFO | Train Epoch: 21 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.211, 151.524/s, 151.524/s/gpu LR: 0.000036 Logit Scale: 28.600 Contrastive_loss: 0.019958 (0.060470) Loss: 0.019958 (0.060470) 2025-03-20,02:02:51 | INFO | Train Epoch: 21 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.214, 147.912/s, 147.912/s/gpu LR: 0.000036 Logit Scale: 28.617 Contrastive_loss: 0.030328 (0.060294) Loss: 0.030328 (0.060294) 2025-03-20,02:03:13 | INFO | Train Epoch: 21 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 150.124/s, 150.124/s/gpu LR: 0.000036 Logit Scale: 28.619 Contrastive_loss: 0.060620 (0.060296) Loss: 0.060620 (0.060296) 2025-03-20,02:03:34 | INFO | Train Epoch: 21 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 148.539/s, 148.539/s/gpu LR: 0.000036 Logit Scale: 28.618 Contrastive_loss: 0.073316 (0.060371) Loss: 0.073316 (0.060371) 2025-03-20,02:03:56 | INFO | Train Epoch: 21 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 146.419/s, 146.419/s/gpu LR: 0.000036 Logit Scale: 28.606 Contrastive_loss: 0.012211 (0.060095) Loss: 0.012211 (0.060095) 2025-03-20,02:04:18 | INFO | Train Epoch: 21 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 147.250/s, 147.250/s/gpu LR: 0.000036 Logit Scale: 28.606 Contrastive_loss: 0.058821 (0.060087) Loss: 0.058821 (0.060087) 2025-03-20,02:04:39 | INFO | Train Epoch: 21 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 147.215/s, 147.215/s/gpu LR: 0.000036 Logit Scale: 28.608 Contrastive_loss: 0.061501 (0.060095) Loss: 0.061501 (0.060095) 2025-03-20,02:05:01 | INFO | Train Epoch: 21 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 148.982/s, 148.982/s/gpu LR: 0.000036 Logit Scale: 28.613 Contrastive_loss: 0.0094603 (0.059809) Loss: 0.0094603 (0.059809) 2025-03-20,02:05:23 | INFO | Train Epoch: 21 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.170/s, 149.170/s/gpu LR: 0.000036 Logit Scale: 28.623 Contrastive_loss: 0.0079669 (0.059518) Loss: 0.0079669 (0.059518) 2025-03-20,02:05:44 | INFO | Train Epoch: 21 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 149.793/s, 149.793/s/gpu LR: 0.000036 Logit Scale: 28.627 Contrastive_loss: 0.087334 (0.059673) Loss: 0.087334 (0.059673) 2025-03-20,02:06:06 | INFO | Train Epoch: 21 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 148.894/s, 148.894/s/gpu LR: 0.000036 Logit Scale: 28.622 Contrastive_loss: 0.045630 (0.059595) Loss: 0.045630 (0.059595) 2025-03-20,02:06:27 | INFO | Train Epoch: 21 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 145.789/s, 145.789/s/gpu LR: 0.000036 Logit Scale: 28.629 Contrastive_loss: 0.0098342 (0.059320) Loss: 0.0098342 (0.059320) 2025-03-20,02:06:48 | INFO | Train Epoch: 21 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 150.045/s, 150.045/s/gpu LR: 0.000036 Logit Scale: 28.620 Contrastive_loss: 0.036785 (0.059197) Loss: 0.036785 (0.059197) 2025-03-20,02:07:10 | INFO | Train Epoch: 21 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.552/s, 149.552/s/gpu LR: 0.000036 Logit Scale: 28.622 Contrastive_loss: 0.12241 (0.059542) Loss: 0.12241 (0.059542) 2025-03-20,02:07:31 | INFO | Train Epoch: 21 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.213, 149.855/s, 149.855/s/gpu LR: 0.000036 Logit Scale: 28.623 Contrastive_loss: 0.017999 (0.059316) Loss: 0.017999 (0.059316) 2025-03-20,02:07:53 | INFO | Train Epoch: 21 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.931/s, 149.931/s/gpu LR: 0.000036 Logit Scale: 28.627 Contrastive_loss: 0.082689 (0.059443) Loss: 0.082689 (0.059443) 2025-03-20,02:08:14 | INFO | Train Epoch: 21 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.422/s, 149.422/s/gpu LR: 0.000036 Logit Scale: 28.637 Contrastive_loss: 0.10414 (0.059683) Loss: 0.10414 (0.059683) 2025-03-20,02:08:35 | INFO | Train Epoch: 21 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 149.132/s, 149.132/s/gpu LR: 0.000036 Logit Scale: 28.636 Contrastive_loss: 0.0010532 (0.059369) Loss: 0.0010532 (0.059369) 2025-03-20,02:08:57 | INFO | Train Epoch: 21 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 148.551/s, 148.551/s/gpu LR: 0.000036 Logit Scale: 28.633 Contrastive_loss: 0.021206 (0.059166) Loss: 0.021206 (0.059166) 2025-03-20,02:09:18 | INFO | Train Epoch: 21 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.741/s, 148.741/s/gpu LR: 0.000036 Logit Scale: 28.626 Contrastive_loss: 0.0055898 (0.058883) Loss: 0.0055898 (0.058883) 2025-03-20,02:09:40 | INFO | Train Epoch: 21 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 151.240/s, 151.240/s/gpu LR: 0.000036 Logit Scale: 28.632 Contrastive_loss: 0.031617 (0.058739) Loss: 0.031617 (0.058739) 2025-03-20,02:10:01 | INFO | Train Epoch: 21 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.108/s, 148.108/s/gpu LR: 0.000036 Logit Scale: 28.636 Contrastive_loss: 0.096007 (0.058935) Loss: 0.096007 (0.058935) 2025-03-20,02:10:23 | INFO | Train Epoch: 21 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 148.090/s, 148.090/s/gpu LR: 0.000036 Logit Scale: 28.645 Contrastive_loss: 0.084516 (0.059068) Loss: 0.084516 (0.059068) 2025-03-20,02:10:45 | INFO | Train Epoch: 21 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.218, 145.522/s, 145.522/s/gpu LR: 0.000036 Logit Scale: 28.649 Contrastive_loss: 0.0037371 (0.058781) Loss: 0.0037371 (0.058781) 2025-03-20,02:11:07 | INFO | Train Epoch: 21 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.222, 147.909/s, 147.909/s/gpu LR: 0.000036 Logit Scale: 28.648 Contrastive_loss: 0.023273 (0.058598) Loss: 0.023273 (0.058598) 2025-03-20,02:11:28 | INFO | Train Epoch: 21 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.217, 149.326/s, 149.326/s/gpu LR: 0.000035 Logit Scale: 28.653 Contrastive_loss: 0.059506 (0.058603) Loss: 0.059506 (0.058603) 2025-03-20,02:11:50 | INFO | Train Epoch: 21 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 149.605/s, 149.605/s/gpu LR: 0.000035 Logit Scale: 28.650 Contrastive_loss: 0.042319 (0.058520) Loss: 0.042319 (0.058520) 2025-03-20,02:12:11 | INFO | Train Epoch: 21 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.768/s, 149.768/s/gpu LR: 0.000035 Logit Scale: 28.668 Contrastive_loss: 0.027605 (0.058363) Loss: 0.027605 (0.058363) 2025-03-20,02:12:33 | INFO | Train Epoch: 21 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 129.732/s, 129.732/s/gpu LR: 0.000035 Logit Scale: 28.674 Contrastive_loss: 0.17193 (0.058936) Loss: 0.17193 (0.058936) 2025-03-20,02:12:54 | INFO | Train Epoch: 21 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 150.209/s, 150.209/s/gpu LR: 0.000035 Logit Scale: 28.689 Contrastive_loss: 0.056450 (0.058924) Loss: 0.056450 (0.058924) 2025-03-20,02:13:16 | INFO | Train Epoch: 21 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.775/s, 149.775/s/gpu LR: 0.000035 Logit Scale: 28.684 Contrastive_loss: 0.018797 (0.058723) Loss: 0.018797 (0.058723) 2025-03-20,02:13:37 | INFO | Train Epoch: 21 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 147.812/s, 147.812/s/gpu LR: 0.000035 Logit Scale: 28.697 Contrastive_loss: 0.088729 (0.058872) Loss: 0.088729 (0.058872) 2025-03-20,02:13:59 | INFO | Train Epoch: 21 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 150.748/s, 150.748/s/gpu LR: 0.000035 Logit Scale: 28.688 Contrastive_loss: 0.074445 (0.058950) Loss: 0.074445 (0.058950) 2025-03-20,02:14:20 | INFO | Train Epoch: 21 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 149.421/s, 149.421/s/gpu LR: 0.000035 Logit Scale: 28.692 Contrastive_loss: 0.071277 (0.059010) Loss: 0.071277 (0.059010) 2025-03-20,02:14:42 | INFO | Train Epoch: 21 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 148.687/s, 148.687/s/gpu LR: 0.000035 Logit Scale: 28.687 Contrastive_loss: 0.038791 (0.058911) Loss: 0.038791 (0.058911) 2025-03-20,02:15:03 | INFO | Train Epoch: 21 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 149.696/s, 149.696/s/gpu LR: 0.000035 Logit Scale: 28.702 Contrastive_loss: 0.0051184 (0.058649) Loss: 0.0051184 (0.058649) 2025-03-20,02:15:25 | INFO | Train Epoch: 21 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 147.509/s, 147.509/s/gpu LR: 0.000035 Logit Scale: 28.681 Contrastive_loss: 0.00097361 (0.058369) Loss: 0.00097361 (0.058369) 2025-03-20,02:15:46 | INFO | Train Epoch: 21 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.464/s, 149.464/s/gpu LR: 0.000035 Logit Scale: 28.693 Contrastive_loss: 0.00096067 (0.058091) Loss: 0.00096067 (0.058091) 2025-03-20,02:16:08 | INFO | Train Epoch: 21 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 146.618/s, 146.618/s/gpu LR: 0.000035 Logit Scale: 28.700 Contrastive_loss: 0.13042 (0.058439) Loss: 0.13042 (0.058439) 2025-03-20,02:16:29 | INFO | Train Epoch: 21 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 143.729/s, 143.729/s/gpu LR: 0.000035 Logit Scale: 28.691 Contrastive_loss: 0.0054940 (0.058186) Loss: 0.0054940 (0.058186) 2025-03-20,02:16:51 | INFO | Train Epoch: 21 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.219, 146.820/s, 146.820/s/gpu LR: 0.000035 Logit Scale: 28.693 Contrastive_loss: 0.14172 (0.058584) Loss: 0.14172 (0.058584) 2025-03-20,02:17:13 | INFO | Train Epoch: 21 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 150.406/s, 150.406/s/gpu LR: 0.000035 Logit Scale: 28.700 Contrastive_loss: 0.13443 (0.058943) Loss: 0.13443 (0.058943) 2025-03-20,02:17:34 | INFO | Train Epoch: 21 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.360/s, 149.360/s/gpu LR: 0.000035 Logit Scale: 28.711 Contrastive_loss: 0.10189 (0.059146) Loss: 0.10189 (0.059146) 2025-03-20,02:17:57 | INFO | Train Epoch: 21 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.221, 145.234/s, 145.234/s/gpu LR: 0.000035 Logit Scale: 28.707 Contrastive_loss: 0.037183 (0.059043) Loss: 0.037183 (0.059043) 2025-03-20,02:18:19 | INFO | Train Epoch: 21 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.220, 146.495/s, 146.495/s/gpu LR: 0.000035 Logit Scale: 28.698 Contrastive_loss: 0.042252 (0.058964) Loss: 0.042252 (0.058964) 2025-03-20,02:18:41 | INFO | Train Epoch: 21 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.220, 146.416/s, 146.416/s/gpu LR: 0.000035 Logit Scale: 28.700 Contrastive_loss: 0.12500 (0.059271) Loss: 0.12500 (0.059271) 2025-03-20,02:19:02 | INFO | Train Epoch: 21 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.218, 148.283/s, 148.283/s/gpu LR: 0.000035 Logit Scale: 28.682 Contrastive_loss: 0.095168 (0.059437) Loss: 0.095168 (0.059437) 2025-03-20,02:19:24 | INFO | Train Epoch: 21 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 147.943/s, 147.943/s/gpu LR: 0.000035 Logit Scale: 28.691 Contrastive_loss: 0.14707 (0.059841) Loss: 0.14707 (0.059841) 2025-03-20,02:19:46 | INFO | Train Epoch: 21 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 146.546/s, 146.546/s/gpu LR: 0.000035 Logit Scale: 28.679 Contrastive_loss: 0.042320 (0.059761) Loss: 0.042320 (0.059761) 2025-03-20,02:20:07 | INFO | Train Epoch: 21 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 150.002/s, 150.002/s/gpu LR: 0.000035 Logit Scale: 28.671 Contrastive_loss: 0.095235 (0.059923) Loss: 0.095235 (0.059923) 2025-03-20,02:20:29 | INFO | Train Epoch: 21 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.611/s, 149.611/s/gpu LR: 0.000035 Logit Scale: 28.676 Contrastive_loss: 0.16267 (0.060390) Loss: 0.16267 (0.060390) 2025-03-20,02:20:50 | INFO | Train Epoch: 21 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 150.229/s, 150.229/s/gpu LR: 0.000035 Logit Scale: 28.664 Contrastive_loss: 0.040433 (0.060300) Loss: 0.040433 (0.060300) 2025-03-20,02:21:12 | INFO | Train Epoch: 21 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 150.133/s, 150.133/s/gpu LR: 0.000035 Logit Scale: 28.660 Contrastive_loss: 0.060387 (0.060300) Loss: 0.060387 (0.060300) 2025-03-20,02:21:33 | INFO | Train Epoch: 21 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 148.732/s, 148.732/s/gpu LR: 0.000035 Logit Scale: 28.669 Contrastive_loss: 0.10861 (0.060517) Loss: 0.10861 (0.060517) 2025-03-20,02:21:55 | INFO | Train Epoch: 21 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.170/s, 150.170/s/gpu LR: 0.000035 Logit Scale: 28.653 Contrastive_loss: 0.060704 (0.060518) Loss: 0.060704 (0.060518) 2025-03-20,02:22:16 | INFO | Train Epoch: 21 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 149.283/s, 149.283/s/gpu LR: 0.000034 Logit Scale: 28.663 Contrastive_loss: 0.021181 (0.060343) Loss: 0.021181 (0.060343) 2025-03-20,02:22:38 | INFO | Train Epoch: 21 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 149.371/s, 149.371/s/gpu LR: 0.000034 Logit Scale: 28.663 Contrastive_loss: 0.16094 (0.060788) Loss: 0.16094 (0.060788) 2025-03-20,02:22:59 | INFO | Train Epoch: 21 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.028/s, 148.028/s/gpu LR: 0.000034 Logit Scale: 28.669 Contrastive_loss: 0.083592 (0.060888) Loss: 0.083592 (0.060888) 2025-03-20,02:23:21 | INFO | Train Epoch: 21 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 148.158/s, 148.158/s/gpu LR: 0.000034 Logit Scale: 28.682 Contrastive_loss: 0.060221 (0.060885) Loss: 0.060221 (0.060885) 2025-03-20,02:23:42 | INFO | Train Epoch: 21 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 148.620/s, 148.620/s/gpu LR: 0.000034 Logit Scale: 28.682 Contrastive_loss: 0.14952 (0.061272) Loss: 0.14952 (0.061272) 2025-03-20,02:24:04 | INFO | Train Epoch: 21 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.213, 152.202/s, 152.202/s/gpu LR: 0.000034 Logit Scale: 28.681 Contrastive_loss: 0.11487 (0.061505) Loss: 0.11487 (0.061505) 2025-03-20,02:24:25 | INFO | Train Epoch: 21 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 147.667/s, 147.667/s/gpu LR: 0.000034 Logit Scale: 28.678 Contrastive_loss: 0.0065786 (0.061268) Loss: 0.0065786 (0.061268) 2025-03-20,02:24:47 | INFO | Train Epoch: 21 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 149.384/s, 149.384/s/gpu LR: 0.000034 Logit Scale: 28.676 Contrastive_loss: 0.032697 (0.061144) Loss: 0.032697 (0.061144) 2025-03-20,02:25:08 | INFO | Train Epoch: 21 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 148.715/s, 148.715/s/gpu LR: 0.000034 Logit Scale: 28.678 Contrastive_loss: 0.020475 (0.060970) Loss: 0.020475 (0.060970) 2025-03-20,02:25:30 | INFO | Train Epoch: 21 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 148.749/s, 148.749/s/gpu LR: 0.000034 Logit Scale: 28.677 Contrastive_loss: 0.024102 (0.060812) Loss: 0.024102 (0.060812) 2025-03-20,02:25:51 | INFO | Train Epoch: 21 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 149.344/s, 149.344/s/gpu LR: 0.000034 Logit Scale: 28.678 Contrastive_loss: 0.067424 (0.060841) Loss: 0.067424 (0.060841) 2025-03-20,02:26:12 | INFO | Train Epoch: 21 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 150.827/s, 150.827/s/gpu LR: 0.000034 Logit Scale: 28.682 Contrastive_loss: 0.080686 (0.060925) Loss: 0.080686 (0.060925) 2025-03-20,02:26:34 | INFO | Train Epoch: 21 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.016/s, 149.016/s/gpu LR: 0.000034 Logit Scale: 28.691 Contrastive_loss: 0.031582 (0.060801) Loss: 0.031582 (0.060801) 2025-03-20,02:26:55 | INFO | Train Epoch: 21 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 149.744/s, 149.744/s/gpu LR: 0.000034 Logit Scale: 28.682 Contrastive_loss: 0.12082 (0.061053) Loss: 0.12082 (0.061053) 2025-03-20,02:27:17 | INFO | Train Epoch: 21 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 150.627/s, 150.627/s/gpu LR: 0.000034 Logit Scale: 28.676 Contrastive_loss: 0.0012467 (0.060803) Loss: 0.0012467 (0.060803) 2025-03-20,02:27:38 | INFO | Train Epoch: 21 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.213, 150.871/s, 150.871/s/gpu LR: 0.000034 Logit Scale: 28.680 Contrastive_loss: 0.014895 (0.060611) Loss: 0.014895 (0.060611) 2025-03-20,02:27:46 | INFO | Train Epoch: 21 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.214, 150.837/s, 150.837/s/gpu LR: 0.000034 Logit Scale: 28.680 Contrastive_loss: 0.15442 (0.061001) Loss: 0.15442 (0.061001) 2025-03-20,02:27:46 | INFO | Eval Epoch: 22 [32 / 7443] Clip Loss: 4.635361 2025-03-20,02:27:52 | INFO | Eval Epoch: 22 [3232 / 7443] Clip Loss: 0.846691 2025-03-20,02:27:58 | INFO | Eval Epoch: 22 [6432 / 7443] Clip Loss: 0.633234 2025-03-20,02:28:01 | INFO | Eval Epoch: 22 image_to_text_mean_rank: 76.7770 image_to_text_median_rank: 5.0000 image_to_text_R@1: 0.1811 image_to_text_R@5: 0.5169 image_to_text_R@10: 0.6882 text_to_image_mean_rank: 51.4146 text_to_image_median_rank: 5.0000 text_to_image_R@1: 0.1732 text_to_image_R@5: 0.5109 text_to_image_R@10: 0.6810 clip_val_loss: 0.5896 epoch: 22.0000 num_samples: 7443.0000 2025-03-20,02:28:34 | INFO | Start epoch 22 2025-03-20,02:28:35 | INFO | Train Epoch: 22 [ 32/766009 (0%)] Data (t): 0.170 Batch (t): 0.374, 85.4922/s, 85.4922/s/gpu LR: 0.000034 Logit Scale: 28.680 Contrastive_loss: 0.043836 (0.043836) Loss: 0.043836 (0.043836) 2025-03-20,02:28:56 | INFO | Train Epoch: 22 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.216, 149.784/s, 149.784/s/gpu LR: 0.000034 Logit Scale: 28.691 Contrastive_loss: 0.0064873 (0.025161) Loss: 0.0064873 (0.025161) 2025-03-20,02:29:18 | INFO | Train Epoch: 22 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 147.924/s, 147.924/s/gpu LR: 0.000034 Logit Scale: 28.695 Contrastive_loss: 0.0087460 (0.019690) Loss: 0.0087460 (0.019690) 2025-03-20,02:29:39 | INFO | Train Epoch: 22 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 149.289/s, 149.289/s/gpu LR: 0.000034 Logit Scale: 28.708 Contrastive_loss: 0.056483 (0.028888) Loss: 0.056483 (0.028888) 2025-03-20,02:30:01 | INFO | Train Epoch: 22 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.213, 150.208/s, 150.208/s/gpu LR: 0.000034 Logit Scale: 28.710 Contrastive_loss: 0.097265 (0.042563) Loss: 0.097265 (0.042563) 2025-03-20,02:30:22 | INFO | Train Epoch: 22 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.218, 149.967/s, 149.967/s/gpu LR: 0.000034 Logit Scale: 28.726 Contrastive_loss: 0.0043334 (0.036192) Loss: 0.0043334 (0.036192) 2025-03-20,02:30:44 | INFO | Train Epoch: 22 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 149.206/s, 149.206/s/gpu LR: 0.000034 Logit Scale: 28.735 Contrastive_loss: 0.026687 (0.034834) Loss: 0.026687 (0.034834) 2025-03-20,02:31:05 | INFO | Train Epoch: 22 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 150.127/s, 150.127/s/gpu LR: 0.000034 Logit Scale: 28.742 Contrastive_loss: 0.0062998 (0.031267) Loss: 0.0062998 (0.031267) 2025-03-20,02:31:27 | INFO | Train Epoch: 22 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.213, 150.654/s, 150.654/s/gpu LR: 0.000034 Logit Scale: 28.751 Contrastive_loss: 0.0023951 (0.028059) Loss: 0.0023951 (0.028059) 2025-03-20,02:31:48 | INFO | Train Epoch: 22 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.213, 151.323/s, 151.323/s/gpu LR: 0.000034 Logit Scale: 28.756 Contrastive_loss: 0.015802 (0.026833) Loss: 0.015802 (0.026833) 2025-03-20,02:32:09 | INFO | Train Epoch: 22 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 151.858/s, 151.858/s/gpu LR: 0.000034 Logit Scale: 28.767 Contrastive_loss: 0.047754 (0.028735) Loss: 0.047754 (0.028735) 2025-03-20,02:32:31 | INFO | Train Epoch: 22 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.219, 145.735/s, 145.735/s/gpu LR: 0.000034 Logit Scale: 28.776 Contrastive_loss: 0.011219 (0.027275) Loss: 0.011219 (0.027275) 2025-03-20,02:32:53 | INFO | Train Epoch: 22 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.220, 148.550/s, 148.550/s/gpu LR: 0.000034 Logit Scale: 28.789 Contrastive_loss: 0.016808 (0.026470) Loss: 0.016808 (0.026470) 2025-03-20,02:33:15 | INFO | Train Epoch: 22 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 150.996/s, 150.996/s/gpu LR: 0.000034 Logit Scale: 28.795 Contrastive_loss: 0.017054 (0.025798) Loss: 0.017054 (0.025798) 2025-03-20,02:33:37 | INFO | Train Epoch: 22 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 146.516/s, 146.516/s/gpu LR: 0.000034 Logit Scale: 28.798 Contrastive_loss: 0.074099 (0.029018) Loss: 0.074099 (0.029018) 2025-03-20,02:33:58 | INFO | Train Epoch: 22 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.758/s, 148.758/s/gpu LR: 0.000033 Logit Scale: 28.803 Contrastive_loss: 0.00023413 (0.027219) Loss: 0.00023413 (0.027219) 2025-03-20,02:34:20 | INFO | Train Epoch: 22 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 149.639/s, 149.639/s/gpu LR: 0.000033 Logit Scale: 28.806 Contrastive_loss: 0.0030877 (0.025799) Loss: 0.0030877 (0.025799) 2025-03-20,02:34:41 | INFO | Train Epoch: 22 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 148.441/s, 148.441/s/gpu LR: 0.000033 Logit Scale: 28.822 Contrastive_loss: 0.00071135 (0.024406) Loss: 0.00071135 (0.024406) 2025-03-20,02:35:03 | INFO | Train Epoch: 22 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 148.433/s, 148.433/s/gpu LR: 0.000033 Logit Scale: 28.824 Contrastive_loss: 0.012595 (0.023784) Loss: 0.012595 (0.023784) 2025-03-20,02:35:24 | INFO | Train Epoch: 22 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.213, 151.196/s, 151.196/s/gpu LR: 0.000033 Logit Scale: 28.813 Contrastive_loss: 0.12955 (0.029072) Loss: 0.12955 (0.029072) 2025-03-20,02:35:46 | INFO | Train Epoch: 22 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 151.040/s, 151.040/s/gpu LR: 0.000033 Logit Scale: 28.802 Contrastive_loss: 0.091974 (0.032067) Loss: 0.091974 (0.032067) 2025-03-20,02:36:07 | INFO | Train Epoch: 22 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.212, 152.200/s, 152.200/s/gpu LR: 0.000033 Logit Scale: 28.805 Contrastive_loss: 0.00015705 (0.030617) Loss: 0.00015705 (0.030617) 2025-03-20,02:36:29 | INFO | Train Epoch: 22 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 149.781/s, 149.781/s/gpu LR: 0.000033 Logit Scale: 28.816 Contrastive_loss: 0.013159 (0.029858) Loss: 0.013159 (0.029858) 2025-03-20,02:36:50 | INFO | Train Epoch: 22 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 149.501/s, 149.501/s/gpu LR: 0.000033 Logit Scale: 28.811 Contrastive_loss: 0.055469 (0.030925) Loss: 0.055469 (0.030925) 2025-03-20,02:37:11 | INFO | Train Epoch: 22 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.213, 151.599/s, 151.599/s/gpu LR: 0.000033 Logit Scale: 28.823 Contrastive_loss: 0.058794 (0.032040) Loss: 0.058794 (0.032040) 2025-03-20,02:37:33 | INFO | Train Epoch: 22 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 146.764/s, 146.764/s/gpu LR: 0.000033 Logit Scale: 28.826 Contrastive_loss: 0.16382 (0.037108) Loss: 0.16382 (0.037108) 2025-03-20,02:37:55 | INFO | Train Epoch: 22 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 147.207/s, 147.207/s/gpu LR: 0.000033 Logit Scale: 28.824 Contrastive_loss: 0.096479 (0.039307) Loss: 0.096479 (0.039307) 2025-03-20,02:38:16 | INFO | Train Epoch: 22 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 148.326/s, 148.326/s/gpu LR: 0.000033 Logit Scale: 28.842 Contrastive_loss: 0.012440 (0.038348) Loss: 0.012440 (0.038348) 2025-03-20,02:38:38 | INFO | Train Epoch: 22 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.218, 143.078/s, 143.078/s/gpu LR: 0.000033 Logit Scale: 28.852 Contrastive_loss: 0.096594 (0.040356) Loss: 0.096594 (0.040356) 2025-03-20,02:39:00 | INFO | Train Epoch: 22 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.219, 148.090/s, 148.090/s/gpu LR: 0.000033 Logit Scale: 28.854 Contrastive_loss: 0.058802 (0.040971) Loss: 0.058802 (0.040971) 2025-03-20,02:39:22 | INFO | Train Epoch: 22 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 150.666/s, 150.666/s/gpu LR: 0.000033 Logit Scale: 28.845 Contrastive_loss: 0.11845 (0.043470) Loss: 0.11845 (0.043470) 2025-03-20,02:39:44 | INFO | Train Epoch: 22 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.218, 145.269/s, 145.269/s/gpu LR: 0.000033 Logit Scale: 28.842 Contrastive_loss: 0.073640 (0.044413) Loss: 0.073640 (0.044413) 2025-03-20,02:40:05 | INFO | Train Epoch: 22 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 146.455/s, 146.455/s/gpu LR: 0.000033 Logit Scale: 28.844 Contrastive_loss: 0.0078638 (0.043305) Loss: 0.0078638 (0.043305) 2025-03-20,02:40:27 | INFO | Train Epoch: 22 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 149.583/s, 149.583/s/gpu LR: 0.000033 Logit Scale: 28.825 Contrastive_loss: 0.010096 (0.042329) Loss: 0.010096 (0.042329) 2025-03-20,02:40:48 | INFO | Train Epoch: 22 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 145.360/s, 145.360/s/gpu LR: 0.000033 Logit Scale: 28.828 Contrastive_loss: 0.10347 (0.044075) Loss: 0.10347 (0.044075) 2025-03-20,02:41:10 | INFO | Train Epoch: 22 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.415/s, 149.415/s/gpu LR: 0.000033 Logit Scale: 28.821 Contrastive_loss: 0.0095186 (0.043115) Loss: 0.0095186 (0.043115) 2025-03-20,02:41:31 | INFO | Train Epoch: 22 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.634/s, 148.634/s/gpu LR: 0.000033 Logit Scale: 28.825 Contrastive_loss: 0.0010685 (0.041979) Loss: 0.0010685 (0.041979) 2025-03-20,02:41:53 | INFO | Train Epoch: 22 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.652/s, 149.652/s/gpu LR: 0.000033 Logit Scale: 28.813 Contrastive_loss: 0.10990 (0.043767) Loss: 0.10990 (0.043767) 2025-03-20,02:42:14 | INFO | Train Epoch: 22 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.213, 148.212/s, 148.212/s/gpu LR: 0.000033 Logit Scale: 28.824 Contrastive_loss: 0.15861 (0.046711) Loss: 0.15861 (0.046711) 2025-03-20,02:42:36 | INFO | Train Epoch: 22 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 148.385/s, 148.385/s/gpu LR: 0.000033 Logit Scale: 28.828 Contrastive_loss: 0.0025826 (0.045608) Loss: 0.0025826 (0.045608) 2025-03-20,02:42:57 | INFO | Train Epoch: 22 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 149.535/s, 149.535/s/gpu LR: 0.000033 Logit Scale: 28.834 Contrastive_loss: 0.066611 (0.046120) Loss: 0.066611 (0.046120) 2025-03-20,02:43:19 | INFO | Train Epoch: 22 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 147.369/s, 147.369/s/gpu LR: 0.000033 Logit Scale: 28.844 Contrastive_loss: 0.043903 (0.046068) Loss: 0.043903 (0.046068) 2025-03-20,02:43:40 | INFO | Train Epoch: 22 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 144.628/s, 144.628/s/gpu LR: 0.000033 Logit Scale: 28.852 Contrastive_loss: 0.082985 (0.046926) Loss: 0.082985 (0.046926) 2025-03-20,02:44:02 | INFO | Train Epoch: 22 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.615/s, 147.615/s/gpu LR: 0.000033 Logit Scale: 28.845 Contrastive_loss: 0.059256 (0.047206) Loss: 0.059256 (0.047206) 2025-03-20,02:44:24 | INFO | Train Epoch: 22 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 150.252/s, 150.252/s/gpu LR: 0.000033 Logit Scale: 28.838 Contrastive_loss: 0.027450 (0.046767) Loss: 0.027450 (0.046767) 2025-03-20,02:44:45 | INFO | Train Epoch: 22 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.217, 141.887/s, 141.887/s/gpu LR: 0.000032 Logit Scale: 28.834 Contrastive_loss: 0.062727 (0.047114) Loss: 0.062727 (0.047114) 2025-03-20,02:45:07 | INFO | Train Epoch: 22 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 152.657/s, 152.657/s/gpu LR: 0.000032 Logit Scale: 28.830 Contrastive_loss: 0.0088272 (0.046300) Loss: 0.0088272 (0.046300) 2025-03-20,02:45:28 | INFO | Train Epoch: 22 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 145.858/s, 145.858/s/gpu LR: 0.000032 Logit Scale: 28.826 Contrastive_loss: 0.057448 (0.046532) Loss: 0.057448 (0.046532) 2025-03-20,02:45:50 | INFO | Train Epoch: 22 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.220, 147.217/s, 147.217/s/gpu LR: 0.000032 Logit Scale: 28.831 Contrastive_loss: 0.014850 (0.045885) Loss: 0.014850 (0.045885) 2025-03-20,02:46:12 | INFO | Train Epoch: 22 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 148.300/s, 148.300/s/gpu LR: 0.000032 Logit Scale: 28.836 Contrastive_loss: 0.060221 (0.046172) Loss: 0.060221 (0.046172) 2025-03-20,02:46:34 | INFO | Train Epoch: 22 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 149.268/s, 149.268/s/gpu LR: 0.000032 Logit Scale: 28.847 Contrastive_loss: 0.016040 (0.045581) Loss: 0.016040 (0.045581) 2025-03-20,02:46:55 | INFO | Train Epoch: 22 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.420/s, 149.420/s/gpu LR: 0.000032 Logit Scale: 28.855 Contrastive_loss: 0.013863 (0.044971) Loss: 0.013863 (0.044971) 2025-03-20,02:47:17 | INFO | Train Epoch: 22 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 148.662/s, 148.662/s/gpu LR: 0.000032 Logit Scale: 28.860 Contrastive_loss: 0.16817 (0.047296) Loss: 0.16817 (0.047296) 2025-03-20,02:47:38 | INFO | Train Epoch: 22 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 150.220/s, 150.220/s/gpu LR: 0.000032 Logit Scale: 28.868 Contrastive_loss: 0.062763 (0.047582) Loss: 0.062763 (0.047582) 2025-03-20,02:47:59 | INFO | Train Epoch: 22 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.164/s, 149.164/s/gpu LR: 0.000032 Logit Scale: 28.861 Contrastive_loss: 0.00063422 (0.046729) Loss: 0.00063422 (0.046729) 2025-03-20,02:48:21 | INFO | Train Epoch: 22 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 147.646/s, 147.646/s/gpu LR: 0.000032 Logit Scale: 28.851 Contrastive_loss: 0.096333 (0.047614) Loss: 0.096333 (0.047614) 2025-03-20,02:48:42 | INFO | Train Epoch: 22 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.610/s, 149.610/s/gpu LR: 0.000032 Logit Scale: 28.842 Contrastive_loss: 0.13422 (0.049134) Loss: 0.13422 (0.049134) 2025-03-20,02:49:04 | INFO | Train Epoch: 22 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 147.342/s, 147.342/s/gpu LR: 0.000032 Logit Scale: 28.844 Contrastive_loss: 0.0028887 (0.048336) Loss: 0.0028887 (0.048336) 2025-03-20,02:49:26 | INFO | Train Epoch: 22 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.218, 147.077/s, 147.077/s/gpu LR: 0.000032 Logit Scale: 28.852 Contrastive_loss: 0.0013064 (0.047539) Loss: 0.0013064 (0.047539) 2025-03-20,02:49:48 | INFO | Train Epoch: 22 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 145.829/s, 145.829/s/gpu LR: 0.000032 Logit Scale: 28.852 Contrastive_loss: 0.0062724 (0.046851) Loss: 0.0062724 (0.046851) 2025-03-20,02:50:09 | INFO | Train Epoch: 22 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 147.570/s, 147.570/s/gpu LR: 0.000032 Logit Scale: 28.854 Contrastive_loss: 0.012073 (0.046281) Loss: 0.012073 (0.046281) 2025-03-20,02:50:31 | INFO | Train Epoch: 22 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.214, 147.726/s, 147.726/s/gpu LR: 0.000032 Logit Scale: 28.843 Contrastive_loss: 0.12555 (0.047560) Loss: 0.12555 (0.047560) 2025-03-20,02:50:52 | INFO | Train Epoch: 22 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 149.996/s, 149.996/s/gpu LR: 0.000032 Logit Scale: 28.831 Contrastive_loss: 0.0026283 (0.046847) Loss: 0.0026283 (0.046847) 2025-03-20,02:51:14 | INFO | Train Epoch: 22 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.218, 149.725/s, 149.725/s/gpu LR: 0.000032 Logit Scale: 28.829 Contrastive_loss: 0.0063211 (0.046213) Loss: 0.0063211 (0.046213) 2025-03-20,02:51:36 | INFO | Train Epoch: 22 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.217, 150.281/s, 150.281/s/gpu LR: 0.000032 Logit Scale: 28.840 Contrastive_loss: 0.0014829 (0.045525) Loss: 0.0014829 (0.045525) 2025-03-20,02:51:57 | INFO | Train Epoch: 22 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.212, 150.965/s, 150.965/s/gpu LR: 0.000032 Logit Scale: 28.846 Contrastive_loss: 0.00049164 (0.044843) Loss: 0.00049164 (0.044843) 2025-03-20,02:52:19 | INFO | Train Epoch: 22 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 147.841/s, 147.841/s/gpu LR: 0.000032 Logit Scale: 28.851 Contrastive_loss: 0.0048371 (0.044246) Loss: 0.0048371 (0.044246) 2025-03-20,02:52:40 | INFO | Train Epoch: 22 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 151.611/s, 151.611/s/gpu LR: 0.000032 Logit Scale: 28.853 Contrastive_loss: 0.067617 (0.044589) Loss: 0.067617 (0.044589) 2025-03-20,02:53:02 | INFO | Train Epoch: 22 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 147.466/s, 147.466/s/gpu LR: 0.000032 Logit Scale: 28.860 Contrastive_loss: 0.023495 (0.044284) Loss: 0.023495 (0.044284) 2025-03-20,02:53:24 | INFO | Train Epoch: 22 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.218, 146.900/s, 146.900/s/gpu LR: 0.000032 Logit Scale: 28.866 Contrastive_loss: 0.056774 (0.044462) Loss: 0.056774 (0.044462) 2025-03-20,02:53:45 | INFO | Train Epoch: 22 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.217, 149.920/s, 149.920/s/gpu LR: 0.000032 Logit Scale: 28.866 Contrastive_loss: 0.16954 (0.046224) Loss: 0.16954 (0.046224) 2025-03-20,02:54:07 | INFO | Train Epoch: 22 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.640/s, 149.640/s/gpu LR: 0.000032 Logit Scale: 28.864 Contrastive_loss: 0.049310 (0.046267) Loss: 0.049310 (0.046267) 2025-03-20,02:54:28 | INFO | Train Epoch: 22 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 149.563/s, 149.563/s/gpu LR: 0.000032 Logit Scale: 28.861 Contrastive_loss: 0.0025447 (0.045668) Loss: 0.0025447 (0.045668) 2025-03-20,02:54:50 | INFO | Train Epoch: 22 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 149.959/s, 149.959/s/gpu LR: 0.000032 Logit Scale: 28.867 Contrastive_loss: 0.21464 (0.047951) Loss: 0.21464 (0.047951) 2025-03-20,02:55:11 | INFO | Train Epoch: 22 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 143.820/s, 143.820/s/gpu LR: 0.000032 Logit Scale: 28.869 Contrastive_loss: 0.0014687 (0.047331) Loss: 0.0014687 (0.047331) 2025-03-20,02:55:33 | INFO | Train Epoch: 22 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 149.258/s, 149.258/s/gpu LR: 0.000032 Logit Scale: 28.872 Contrastive_loss: 0.062042 (0.047525) Loss: 0.062042 (0.047525) 2025-03-20,02:55:55 | INFO | Train Epoch: 22 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 147.331/s, 147.331/s/gpu LR: 0.000031 Logit Scale: 28.877 Contrastive_loss: 0.074058 (0.047870) Loss: 0.074058 (0.047870) 2025-03-20,02:56:16 | INFO | Train Epoch: 22 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 149.385/s, 149.385/s/gpu LR: 0.000031 Logit Scale: 28.883 Contrastive_loss: 0.0032520 (0.047298) Loss: 0.0032520 (0.047298) 2025-03-20,02:56:38 | INFO | Train Epoch: 22 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 148.932/s, 148.932/s/gpu LR: 0.000031 Logit Scale: 28.883 Contrastive_loss: 0.0053720 (0.046767) Loss: 0.0053720 (0.046767) 2025-03-20,02:56:59 | INFO | Train Epoch: 22 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.982/s, 150.982/s/gpu LR: 0.000031 Logit Scale: 28.869 Contrastive_loss: 0.035947 (0.046632) Loss: 0.035947 (0.046632) 2025-03-20,02:57:20 | INFO | Train Epoch: 22 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.213, 149.911/s, 149.911/s/gpu LR: 0.000031 Logit Scale: 28.874 Contrastive_loss: 0.097784 (0.047263) Loss: 0.097784 (0.047263) 2025-03-20,02:57:42 | INFO | Train Epoch: 22 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 149.273/s, 149.273/s/gpu LR: 0.000031 Logit Scale: 28.890 Contrastive_loss: 0.022153 (0.046957) Loss: 0.022153 (0.046957) 2025-03-20,02:58:03 | INFO | Train Epoch: 22 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.567/s, 149.567/s/gpu LR: 0.000031 Logit Scale: 28.891 Contrastive_loss: 0.0024344 (0.046421) Loss: 0.0024344 (0.046421) 2025-03-20,02:58:25 | INFO | Train Epoch: 22 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.214, 149.655/s, 149.655/s/gpu LR: 0.000031 Logit Scale: 28.895 Contrastive_loss: 0.031217 (0.046240) Loss: 0.031217 (0.046240) 2025-03-20,02:58:46 | INFO | Train Epoch: 22 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 148.672/s, 148.672/s/gpu LR: 0.000031 Logit Scale: 28.907 Contrastive_loss: 0.088919 (0.046742) Loss: 0.088919 (0.046742) 2025-03-20,02:59:08 | INFO | Train Epoch: 22 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 147.414/s, 147.414/s/gpu LR: 0.000031 Logit Scale: 28.902 Contrastive_loss: 0.076175 (0.047084) Loss: 0.076175 (0.047084) 2025-03-20,02:59:29 | INFO | Train Epoch: 22 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 148.166/s, 148.166/s/gpu LR: 0.000031 Logit Scale: 28.898 Contrastive_loss: 0.089705 (0.047574) Loss: 0.089705 (0.047574) 2025-03-20,02:59:51 | INFO | Train Epoch: 22 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.292/s, 148.292/s/gpu LR: 0.000031 Logit Scale: 28.899 Contrastive_loss: 0.079335 (0.047935) Loss: 0.079335 (0.047935) 2025-03-20,03:00:12 | INFO | Train Epoch: 22 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 148.993/s, 148.993/s/gpu LR: 0.000031 Logit Scale: 28.915 Contrastive_loss: 0.090076 (0.048408) Loss: 0.090076 (0.048408) 2025-03-20,03:00:34 | INFO | Train Epoch: 22 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.213, 149.056/s, 149.056/s/gpu LR: 0.000031 Logit Scale: 28.906 Contrastive_loss: 0.00066651 (0.047878) Loss: 0.00066651 (0.047878) 2025-03-20,03:00:55 | INFO | Train Epoch: 22 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 147.822/s, 147.822/s/gpu LR: 0.000031 Logit Scale: 28.897 Contrastive_loss: 0.0053241 (0.047410) Loss: 0.0053241 (0.047410) 2025-03-20,03:01:17 | INFO | Train Epoch: 22 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.217, 148.217/s, 148.217/s/gpu LR: 0.000031 Logit Scale: 28.904 Contrastive_loss: 0.018890 (0.047100) Loss: 0.018890 (0.047100) 2025-03-20,03:01:39 | INFO | Train Epoch: 22 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 148.277/s, 148.277/s/gpu LR: 0.000031 Logit Scale: 28.893 Contrastive_loss: 0.0010883 (0.046605) Loss: 0.0010883 (0.046605) 2025-03-20,03:02:00 | INFO | Train Epoch: 22 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 150.155/s, 150.155/s/gpu LR: 0.000031 Logit Scale: 28.896 Contrastive_loss: 0.066663 (0.046819) Loss: 0.066663 (0.046819) 2025-03-20,03:02:22 | INFO | Train Epoch: 22 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 147.945/s, 147.945/s/gpu LR: 0.000031 Logit Scale: 28.902 Contrastive_loss: 0.18359 (0.048258) Loss: 0.18359 (0.048258) 2025-03-20,03:02:43 | INFO | Train Epoch: 22 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 146.871/s, 146.871/s/gpu LR: 0.000031 Logit Scale: 28.903 Contrastive_loss: 0.028799 (0.048056) Loss: 0.028799 (0.048056) 2025-03-20,03:03:05 | INFO | Train Epoch: 22 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.033/s, 149.033/s/gpu LR: 0.000031 Logit Scale: 28.918 Contrastive_loss: 0.072434 (0.048307) Loss: 0.072434 (0.048307) 2025-03-20,03:03:26 | INFO | Train Epoch: 22 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 150.199/s, 150.199/s/gpu LR: 0.000031 Logit Scale: 28.913 Contrastive_loss: 0.046527 (0.048289) Loss: 0.046527 (0.048289) 2025-03-20,03:03:48 | INFO | Train Epoch: 22 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 149.666/s, 149.666/s/gpu LR: 0.000031 Logit Scale: 28.928 Contrastive_loss: 0.056967 (0.048377) Loss: 0.056967 (0.048377) 2025-03-20,03:04:09 | INFO | Train Epoch: 22 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 150.093/s, 150.093/s/gpu LR: 0.000031 Logit Scale: 28.919 Contrastive_loss: 0.046764 (0.048360) Loss: 0.046764 (0.048360) 2025-03-20,03:04:30 | INFO | Train Epoch: 22 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.215, 149.296/s, 149.296/s/gpu LR: 0.000031 Logit Scale: 28.921 Contrastive_loss: 0.076988 (0.048644) Loss: 0.076988 (0.048644) 2025-03-20,03:04:52 | INFO | Train Epoch: 22 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 148.828/s, 148.828/s/gpu LR: 0.000031 Logit Scale: 28.927 Contrastive_loss: 0.034891 (0.048509) Loss: 0.034891 (0.048509) 2025-03-20,03:05:13 | INFO | Train Epoch: 22 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.929/s, 149.929/s/gpu LR: 0.000031 Logit Scale: 28.922 Contrastive_loss: 0.057495 (0.048596) Loss: 0.057495 (0.048596) 2025-03-20,03:05:35 | INFO | Train Epoch: 22 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.488/s, 149.488/s/gpu LR: 0.000031 Logit Scale: 28.919 Contrastive_loss: 0.081134 (0.048909) Loss: 0.081134 (0.048909) 2025-03-20,03:05:56 | INFO | Train Epoch: 22 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 148.878/s, 148.878/s/gpu LR: 0.000031 Logit Scale: 28.928 Contrastive_loss: 0.00059964 (0.048449) Loss: 0.00059964 (0.048449) 2025-03-20,03:06:18 | INFO | Train Epoch: 22 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 148.934/s, 148.934/s/gpu LR: 0.000031 Logit Scale: 28.939 Contrastive_loss: 0.066939 (0.048624) Loss: 0.066939 (0.048624) 2025-03-20,03:06:39 | INFO | Train Epoch: 22 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 149.540/s, 149.540/s/gpu LR: 0.000031 Logit Scale: 28.941 Contrastive_loss: 0.0098170 (0.048261) Loss: 0.0098170 (0.048261) 2025-03-20,03:07:00 | INFO | Train Epoch: 22 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 149.425/s, 149.425/s/gpu LR: 0.000030 Logit Scale: 28.944 Contrastive_loss: 0.053547 (0.048310) Loss: 0.053547 (0.048310) 2025-03-20,03:07:22 | INFO | Train Epoch: 22 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 148.380/s, 148.380/s/gpu LR: 0.000030 Logit Scale: 28.950 Contrastive_loss: 0.25269 (0.050185) Loss: 0.25269 (0.050185) 2025-03-20,03:07:44 | INFO | Train Epoch: 22 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.219, 148.123/s, 148.123/s/gpu LR: 0.000030 Logit Scale: 28.946 Contrastive_loss: 0.098580 (0.050625) Loss: 0.098580 (0.050625) 2025-03-20,03:08:05 | INFO | Train Epoch: 22 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.213, 151.620/s, 151.620/s/gpu LR: 0.000030 Logit Scale: 28.953 Contrastive_loss: 0.038104 (0.050512) Loss: 0.038104 (0.050512) 2025-03-20,03:08:26 | INFO | Train Epoch: 22 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.213, 150.483/s, 150.483/s/gpu LR: 0.000030 Logit Scale: 28.970 Contrastive_loss: 0.023812 (0.050274) Loss: 0.023812 (0.050274) 2025-03-20,03:08:48 | INFO | Train Epoch: 22 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 148.176/s, 148.176/s/gpu LR: 0.000030 Logit Scale: 28.975 Contrastive_loss: 0.28799 (0.052377) Loss: 0.28799 (0.052377) 2025-03-20,03:09:10 | INFO | Train Epoch: 22 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.218, 146.101/s, 146.101/s/gpu LR: 0.000030 Logit Scale: 28.972 Contrastive_loss: 0.0010022 (0.051927) Loss: 0.0010022 (0.051927) 2025-03-20,03:09:31 | INFO | Train Epoch: 22 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 147.919/s, 147.919/s/gpu LR: 0.000030 Logit Scale: 28.978 Contrastive_loss: 0.058236 (0.051982) Loss: 0.058236 (0.051982) 2025-03-20,03:09:53 | INFO | Train Epoch: 22 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.413/s, 149.413/s/gpu LR: 0.000030 Logit Scale: 28.983 Contrastive_loss: 0.074070 (0.052172) Loss: 0.074070 (0.052172) 2025-03-20,03:10:14 | INFO | Train Epoch: 22 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.699/s, 148.699/s/gpu LR: 0.000030 Logit Scale: 28.995 Contrastive_loss: 0.062089 (0.052257) Loss: 0.062089 (0.052257) 2025-03-20,03:10:36 | INFO | Train Epoch: 22 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 148.039/s, 148.039/s/gpu LR: 0.000030 Logit Scale: 29.002 Contrastive_loss: 0.0023542 (0.051834) Loss: 0.0023542 (0.051834) 2025-03-20,03:10:58 | INFO | Train Epoch: 22 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 148.483/s, 148.483/s/gpu LR: 0.000030 Logit Scale: 29.001 Contrastive_loss: 0.0014327 (0.051410) Loss: 0.0014327 (0.051410) 2025-03-20,03:11:19 | INFO | Train Epoch: 22 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 147.712/s, 147.712/s/gpu LR: 0.000030 Logit Scale: 29.011 Contrastive_loss: 0.0083903 (0.051052) Loss: 0.0083903 (0.051052) 2025-03-20,03:11:41 | INFO | Train Epoch: 22 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 149.653/s, 149.653/s/gpu LR: 0.000030 Logit Scale: 29.006 Contrastive_loss: 0.013976 (0.050745) Loss: 0.013976 (0.050745) 2025-03-20,03:12:03 | INFO | Train Epoch: 22 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 151.825/s, 151.825/s/gpu LR: 0.000030 Logit Scale: 29.011 Contrastive_loss: 0.0011708 (0.050339) Loss: 0.0011708 (0.050339) 2025-03-20,03:12:24 | INFO | Train Epoch: 22 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 149.388/s, 149.388/s/gpu LR: 0.000030 Logit Scale: 29.007 Contrastive_loss: 0.091746 (0.050676) Loss: 0.091746 (0.050676) 2025-03-20,03:12:45 | INFO | Train Epoch: 22 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 153.205/s, 153.205/s/gpu LR: 0.000030 Logit Scale: 29.015 Contrastive_loss: 0.080262 (0.050914) Loss: 0.080262 (0.050914) 2025-03-20,03:13:07 | INFO | Train Epoch: 22 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 149.796/s, 149.796/s/gpu LR: 0.000030 Logit Scale: 29.016 Contrastive_loss: 0.21100 (0.052195) Loss: 0.21100 (0.052195) 2025-03-20,03:13:28 | INFO | Train Epoch: 22 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 149.168/s, 149.168/s/gpu LR: 0.000030 Logit Scale: 29.018 Contrastive_loss: 0.052124 (0.052194) Loss: 0.052124 (0.052194) 2025-03-20,03:13:50 | INFO | Train Epoch: 22 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 149.471/s, 149.471/s/gpu LR: 0.000030 Logit Scale: 29.021 Contrastive_loss: 0.11171 (0.052663) Loss: 0.11171 (0.052663) 2025-03-20,03:14:12 | INFO | Train Epoch: 22 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 145.585/s, 145.585/s/gpu LR: 0.000030 Logit Scale: 29.013 Contrastive_loss: 0.23131 (0.054059) Loss: 0.23131 (0.054059) 2025-03-20,03:14:33 | INFO | Train Epoch: 22 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.537/s, 148.537/s/gpu LR: 0.000030 Logit Scale: 29.005 Contrastive_loss: 0.0080742 (0.053702) Loss: 0.0080742 (0.053702) 2025-03-20,03:14:55 | INFO | Train Epoch: 22 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 148.885/s, 148.885/s/gpu LR: 0.000030 Logit Scale: 29.006 Contrastive_loss: 0.046592 (0.053648) Loss: 0.046592 (0.053648) 2025-03-20,03:15:16 | INFO | Train Epoch: 22 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 149.517/s, 149.517/s/gpu LR: 0.000030 Logit Scale: 29.010 Contrastive_loss: 0.041469 (0.053555) Loss: 0.041469 (0.053555) 2025-03-20,03:15:38 | INFO | Train Epoch: 22 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 149.326/s, 149.326/s/gpu LR: 0.000030 Logit Scale: 29.018 Contrastive_loss: 0.048702 (0.053518) Loss: 0.048702 (0.053518) 2025-03-20,03:15:59 | INFO | Train Epoch: 22 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.213, 152.051/s, 152.051/s/gpu LR: 0.000030 Logit Scale: 29.016 Contrastive_loss: 0.0037668 (0.053144) Loss: 0.0037668 (0.053144) 2025-03-20,03:16:20 | INFO | Train Epoch: 22 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 149.409/s, 149.409/s/gpu LR: 0.000030 Logit Scale: 29.008 Contrastive_loss: 0.052038 (0.053135) Loss: 0.052038 (0.053135) 2025-03-20,03:16:42 | INFO | Train Epoch: 22 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 150.999/s, 150.999/s/gpu LR: 0.000030 Logit Scale: 29.011 Contrastive_loss: 0.027741 (0.052947) Loss: 0.027741 (0.052947) 2025-03-20,03:17:04 | INFO | Train Epoch: 22 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 148.260/s, 148.260/s/gpu LR: 0.000030 Logit Scale: 29.004 Contrastive_loss: 0.0017963 (0.052571) Loss: 0.0017963 (0.052571) 2025-03-20,03:17:25 | INFO | Train Epoch: 22 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 148.101/s, 148.101/s/gpu LR: 0.000030 Logit Scale: 28.998 Contrastive_loss: 0.069216 (0.052693) Loss: 0.069216 (0.052693) 2025-03-20,03:17:47 | INFO | Train Epoch: 22 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 147.562/s, 147.562/s/gpu LR: 0.000030 Logit Scale: 28.993 Contrastive_loss: 0.14126 (0.053335) Loss: 0.14126 (0.053335) 2025-03-20,03:18:08 | INFO | Train Epoch: 22 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.901/s, 147.901/s/gpu LR: 0.000029 Logit Scale: 28.991 Contrastive_loss: 0.031049 (0.053174) Loss: 0.031049 (0.053174) 2025-03-20,03:18:30 | INFO | Train Epoch: 22 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.220, 146.277/s, 146.277/s/gpu LR: 0.000029 Logit Scale: 28.987 Contrastive_loss: 0.045751 (0.053121) Loss: 0.045751 (0.053121) 2025-03-20,03:18:52 | INFO | Train Epoch: 22 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 148.283/s, 148.283/s/gpu LR: 0.000029 Logit Scale: 28.985 Contrastive_loss: 0.0037148 (0.052771) Loss: 0.0037148 (0.052771) 2025-03-20,03:19:14 | INFO | Train Epoch: 22 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 146.250/s, 146.250/s/gpu LR: 0.000029 Logit Scale: 28.981 Contrastive_loss: 0.046961 (0.052730) Loss: 0.046961 (0.052730) 2025-03-20,03:19:35 | INFO | Train Epoch: 22 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 152.491/s, 152.491/s/gpu LR: 0.000029 Logit Scale: 28.981 Contrastive_loss: 0.13129 (0.053279) Loss: 0.13129 (0.053279) 2025-03-20,03:19:57 | INFO | Train Epoch: 22 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 149.371/s, 149.371/s/gpu LR: 0.000029 Logit Scale: 28.975 Contrastive_loss: 0.049784 (0.053255) Loss: 0.049784 (0.053255) 2025-03-20,03:20:18 | INFO | Train Epoch: 22 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 148.042/s, 148.042/s/gpu LR: 0.000029 Logit Scale: 28.980 Contrastive_loss: 0.024988 (0.053060) Loss: 0.024988 (0.053060) 2025-03-20,03:20:39 | INFO | Train Epoch: 22 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 149.687/s, 149.687/s/gpu LR: 0.000029 Logit Scale: 28.983 Contrastive_loss: 0.0034740 (0.052720) Loss: 0.0034740 (0.052720) 2025-03-20,03:21:01 | INFO | Train Epoch: 22 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 147.787/s, 147.787/s/gpu LR: 0.000029 Logit Scale: 28.984 Contrastive_loss: 0.045546 (0.052672) Loss: 0.045546 (0.052672) 2025-03-20,03:21:23 | INFO | Train Epoch: 22 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 147.411/s, 147.411/s/gpu LR: 0.000029 Logit Scale: 28.999 Contrastive_loss: 0.053106 (0.052675) Loss: 0.053106 (0.052675) 2025-03-20,03:21:44 | INFO | Train Epoch: 22 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.217, 149.274/s, 149.274/s/gpu LR: 0.000029 Logit Scale: 29.005 Contrastive_loss: 0.013191 (0.052410) Loss: 0.013191 (0.052410) 2025-03-20,03:22:06 | INFO | Train Epoch: 22 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.213, 152.591/s, 152.591/s/gpu LR: 0.000029 Logit Scale: 29.005 Contrastive_loss: 0.21999 (0.053527) Loss: 0.21999 (0.053527) 2025-03-20,03:22:27 | INFO | Train Epoch: 22 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.481/s, 149.481/s/gpu LR: 0.000029 Logit Scale: 29.011 Contrastive_loss: 0.083582 (0.053726) Loss: 0.083582 (0.053726) 2025-03-20,03:22:48 | INFO | Train Epoch: 22 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.474/s, 149.474/s/gpu LR: 0.000029 Logit Scale: 29.016 Contrastive_loss: 0.077940 (0.053885) Loss: 0.077940 (0.053885) 2025-03-20,03:23:10 | INFO | Train Epoch: 22 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 148.519/s, 148.519/s/gpu LR: 0.000029 Logit Scale: 29.023 Contrastive_loss: 0.16375 (0.054603) Loss: 0.16375 (0.054603) 2025-03-20,03:23:31 | INFO | Train Epoch: 22 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.085/s, 148.085/s/gpu LR: 0.000029 Logit Scale: 29.030 Contrastive_loss: 0.024542 (0.054408) Loss: 0.024542 (0.054408) 2025-03-20,03:23:53 | INFO | Train Epoch: 22 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 145.678/s, 145.678/s/gpu LR: 0.000029 Logit Scale: 29.033 Contrastive_loss: 0.048445 (0.054369) Loss: 0.048445 (0.054369) 2025-03-20,03:24:15 | INFO | Train Epoch: 22 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 147.558/s, 147.558/s/gpu LR: 0.000029 Logit Scale: 29.033 Contrastive_loss: 0.0043431 (0.054049) Loss: 0.0043431 (0.054049) 2025-03-20,03:24:36 | INFO | Train Epoch: 22 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 147.654/s, 147.654/s/gpu LR: 0.000029 Logit Scale: 29.028 Contrastive_loss: 0.060137 (0.054088) Loss: 0.060137 (0.054088) 2025-03-20,03:24:58 | INFO | Train Epoch: 22 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.220, 145.607/s, 145.607/s/gpu LR: 0.000029 Logit Scale: 29.027 Contrastive_loss: 0.065704 (0.054161) Loss: 0.065704 (0.054161) 2025-03-20,03:25:20 | INFO | Train Epoch: 22 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 149.999/s, 149.999/s/gpu LR: 0.000029 Logit Scale: 29.018 Contrastive_loss: 0.017042 (0.053928) Loss: 0.017042 (0.053928) 2025-03-20,03:25:41 | INFO | Train Epoch: 22 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 148.451/s, 148.451/s/gpu LR: 0.000029 Logit Scale: 29.010 Contrastive_loss: 0.046974 (0.053884) Loss: 0.046974 (0.053884) 2025-03-20,03:26:03 | INFO | Train Epoch: 22 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 149.107/s, 149.107/s/gpu LR: 0.000029 Logit Scale: 29.013 Contrastive_loss: 0.00057073 (0.053553) Loss: 0.00057073 (0.053553) 2025-03-20,03:26:24 | INFO | Train Epoch: 22 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.215, 151.450/s, 151.450/s/gpu LR: 0.000029 Logit Scale: 29.002 Contrastive_loss: 0.049992 (0.053531) Loss: 0.049992 (0.053531) 2025-03-20,03:26:46 | INFO | Train Epoch: 22 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.211, 151.959/s, 151.959/s/gpu LR: 0.000029 Logit Scale: 29.005 Contrastive_loss: 0.022456 (0.053340) Loss: 0.022456 (0.053340) 2025-03-20,03:27:07 | INFO | Train Epoch: 22 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 151.421/s, 151.421/s/gpu LR: 0.000029 Logit Scale: 29.010 Contrastive_loss: 0.13210 (0.053821) Loss: 0.13210 (0.053821) 2025-03-20,03:27:28 | INFO | Train Epoch: 22 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.213, 149.806/s, 149.806/s/gpu LR: 0.000029 Logit Scale: 29.020 Contrastive_loss: 0.0055423 (0.053528) Loss: 0.0055423 (0.053528) 2025-03-20,03:27:50 | INFO | Train Epoch: 22 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.213, 149.710/s, 149.710/s/gpu LR: 0.000029 Logit Scale: 29.034 Contrastive_loss: 0.22786 (0.054578) Loss: 0.22786 (0.054578) 2025-03-20,03:28:11 | INFO | Train Epoch: 22 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.213, 151.399/s, 151.399/s/gpu LR: 0.000029 Logit Scale: 29.031 Contrastive_loss: 0.060860 (0.054616) Loss: 0.060860 (0.054616) 2025-03-20,03:28:32 | INFO | Train Epoch: 22 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.214, 148.018/s, 148.018/s/gpu LR: 0.000029 Logit Scale: 29.047 Contrastive_loss: 0.046298 (0.054566) Loss: 0.046298 (0.054566) 2025-03-20,03:28:54 | INFO | Train Epoch: 22 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.220, 144.843/s, 144.843/s/gpu LR: 0.000029 Logit Scale: 29.024 Contrastive_loss: 0.022496 (0.054377) Loss: 0.022496 (0.054377) 2025-03-20,03:29:16 | INFO | Train Epoch: 22 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.221, 147.380/s, 147.380/s/gpu LR: 0.000029 Logit Scale: 29.037 Contrastive_loss: 0.084013 (0.054551) Loss: 0.084013 (0.054551) 2025-03-20,03:29:38 | INFO | Train Epoch: 22 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 152.363/s, 152.363/s/gpu LR: 0.000028 Logit Scale: 29.031 Contrastive_loss: 0.054653 (0.054552) Loss: 0.054653 (0.054552) 2025-03-20,03:30:00 | INFO | Train Epoch: 22 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.218, 144.655/s, 144.655/s/gpu LR: 0.000028 Logit Scale: 29.046 Contrastive_loss: 0.011361 (0.054300) Loss: 0.011361 (0.054300) 2025-03-20,03:30:22 | INFO | Train Epoch: 22 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 146.727/s, 146.727/s/gpu LR: 0.000028 Logit Scale: 29.049 Contrastive_loss: 0.025768 (0.054135) Loss: 0.025768 (0.054135) 2025-03-20,03:30:43 | INFO | Train Epoch: 22 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 145.713/s, 145.713/s/gpu LR: 0.000028 Logit Scale: 29.048 Contrastive_loss: 0.089341 (0.054338) Loss: 0.089341 (0.054338) 2025-03-20,03:31:05 | INFO | Train Epoch: 22 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.218, 145.776/s, 145.776/s/gpu LR: 0.000028 Logit Scale: 29.034 Contrastive_loss: 0.026055 (0.054176) Loss: 0.026055 (0.054176) 2025-03-20,03:31:27 | INFO | Train Epoch: 22 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 148.558/s, 148.558/s/gpu LR: 0.000028 Logit Scale: 29.039 Contrastive_loss: 0.14925 (0.054716) Loss: 0.14925 (0.054716) 2025-03-20,03:31:49 | INFO | Train Epoch: 22 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 147.040/s, 147.040/s/gpu LR: 0.000028 Logit Scale: 29.043 Contrastive_loss: 0.019266 (0.054516) Loss: 0.019266 (0.054516) 2025-03-20,03:32:11 | INFO | Train Epoch: 22 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.218, 147.240/s, 147.240/s/gpu LR: 0.000028 Logit Scale: 29.053 Contrastive_loss: 0.14600 (0.055030) Loss: 0.14600 (0.055030) 2025-03-20,03:32:33 | INFO | Train Epoch: 22 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 145.438/s, 145.438/s/gpu LR: 0.000028 Logit Scale: 29.063 Contrastive_loss: 0.10442 (0.055306) Loss: 0.10442 (0.055306) 2025-03-20,03:32:55 | INFO | Train Epoch: 22 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.222, 149.203/s, 149.203/s/gpu LR: 0.000028 Logit Scale: 29.065 Contrastive_loss: 0.039162 (0.055216) Loss: 0.039162 (0.055216) 2025-03-20,03:33:16 | INFO | Train Epoch: 22 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.213, 144.841/s, 144.841/s/gpu LR: 0.000028 Logit Scale: 29.053 Contrastive_loss: 0.0057142 (0.054943) Loss: 0.0057142 (0.054943) 2025-03-20,03:33:38 | INFO | Train Epoch: 22 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 149.875/s, 149.875/s/gpu LR: 0.000028 Logit Scale: 29.052 Contrastive_loss: 0.048462 (0.054907) Loss: 0.048462 (0.054907) 2025-03-20,03:33:59 | INFO | Train Epoch: 22 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 148.005/s, 148.005/s/gpu LR: 0.000028 Logit Scale: 29.053 Contrastive_loss: 0.11530 (0.055237) Loss: 0.11530 (0.055237) 2025-03-20,03:34:21 | INFO | Train Epoch: 22 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 148.271/s, 148.271/s/gpu LR: 0.000028 Logit Scale: 29.050 Contrastive_loss: 0.068021 (0.055307) Loss: 0.068021 (0.055307) 2025-03-20,03:34:42 | INFO | Train Epoch: 22 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 148.468/s, 148.468/s/gpu LR: 0.000028 Logit Scale: 29.048 Contrastive_loss: 0.0030476 (0.055024) Loss: 0.0030476 (0.055024) 2025-03-20,03:35:04 | INFO | Train Epoch: 22 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 150.038/s, 150.038/s/gpu LR: 0.000028 Logit Scale: 29.043 Contrastive_loss: 0.069101 (0.055100) Loss: 0.069101 (0.055100) 2025-03-20,03:35:25 | INFO | Train Epoch: 22 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 150.406/s, 150.406/s/gpu LR: 0.000028 Logit Scale: 29.044 Contrastive_loss: 0.055849 (0.055104) Loss: 0.055849 (0.055104) 2025-03-20,03:35:47 | INFO | Train Epoch: 22 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 150.199/s, 150.199/s/gpu LR: 0.000028 Logit Scale: 29.052 Contrastive_loss: 0.029364 (0.054967) Loss: 0.029364 (0.054967) 2025-03-20,03:36:08 | INFO | Train Epoch: 22 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 150.063/s, 150.063/s/gpu LR: 0.000028 Logit Scale: 29.038 Contrastive_loss: 0.19956 (0.055732) Loss: 0.19956 (0.055732) 2025-03-20,03:36:30 | INFO | Train Epoch: 22 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 148.814/s, 148.814/s/gpu LR: 0.000028 Logit Scale: 29.036 Contrastive_loss: 0.028205 (0.055587) Loss: 0.028205 (0.055587) 2025-03-20,03:36:51 | INFO | Train Epoch: 22 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 147.263/s, 147.263/s/gpu LR: 0.000028 Logit Scale: 29.041 Contrastive_loss: 0.00068150 (0.055300) Loss: 0.00068150 (0.055300) 2025-03-20,03:37:13 | INFO | Train Epoch: 22 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 149.302/s, 149.302/s/gpu LR: 0.000028 Logit Scale: 29.038 Contrastive_loss: 0.020673 (0.055119) Loss: 0.020673 (0.055119) 2025-03-20,03:37:34 | INFO | Train Epoch: 22 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 148.729/s, 148.729/s/gpu LR: 0.000028 Logit Scale: 29.033 Contrastive_loss: 0.088968 (0.055295) Loss: 0.088968 (0.055295) 2025-03-20,03:37:56 | INFO | Train Epoch: 22 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 149.535/s, 149.535/s/gpu LR: 0.000028 Logit Scale: 29.041 Contrastive_loss: 0.093434 (0.055491) Loss: 0.093434 (0.055491) 2025-03-20,03:38:17 | INFO | Train Epoch: 22 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 146.955/s, 146.955/s/gpu LR: 0.000028 Logit Scale: 29.042 Contrastive_loss: 0.028818 (0.055355) Loss: 0.028818 (0.055355) 2025-03-20,03:38:39 | INFO | Train Epoch: 22 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 146.479/s, 146.479/s/gpu LR: 0.000028 Logit Scale: 29.057 Contrastive_loss: 0.0088643 (0.055117) Loss: 0.0088643 (0.055117) 2025-03-20,03:39:01 | INFO | Train Epoch: 22 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.222, 146.700/s, 146.700/s/gpu LR: 0.000028 Logit Scale: 29.058 Contrastive_loss: 0.044001 (0.055061) Loss: 0.044001 (0.055061) 2025-03-20,03:39:24 | INFO | Train Epoch: 22 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.221, 144.844/s, 144.844/s/gpu LR: 0.000028 Logit Scale: 29.060 Contrastive_loss: 0.19804 (0.055783) Loss: 0.19804 (0.055783) 2025-03-20,03:39:46 | INFO | Train Epoch: 22 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.223, 141.738/s, 141.738/s/gpu LR: 0.000028 Logit Scale: 29.066 Contrastive_loss: 0.17351 (0.056375) Loss: 0.17351 (0.056375) 2025-03-20,03:40:08 | INFO | Train Epoch: 22 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.222, 143.459/s, 143.459/s/gpu LR: 0.000028 Logit Scale: 29.071 Contrastive_loss: 0.036211 (0.056274) Loss: 0.036211 (0.056274) 2025-03-20,03:40:30 | INFO | Train Epoch: 22 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.223, 145.223/s, 145.223/s/gpu LR: 0.000028 Logit Scale: 29.067 Contrastive_loss: 0.23745 (0.057175) Loss: 0.23745 (0.057175) 2025-03-20,03:40:52 | INFO | Train Epoch: 22 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.219, 145.456/s, 145.456/s/gpu LR: 0.000028 Logit Scale: 29.064 Contrastive_loss: 0.044400 (0.057112) Loss: 0.044400 (0.057112) 2025-03-20,03:41:14 | INFO | Train Epoch: 22 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.219, 145.528/s, 145.528/s/gpu LR: 0.000028 Logit Scale: 29.074 Contrastive_loss: 0.028164 (0.056969) Loss: 0.028164 (0.056969) 2025-03-20,03:41:36 | INFO | Train Epoch: 22 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.219, 145.448/s, 145.448/s/gpu LR: 0.000027 Logit Scale: 29.067 Contrastive_loss: 0.026485 (0.056820) Loss: 0.026485 (0.056820) 2025-03-20,03:41:58 | INFO | Train Epoch: 22 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.220, 145.531/s, 145.531/s/gpu LR: 0.000027 Logit Scale: 29.076 Contrastive_loss: 0.066629 (0.056868) Loss: 0.066629 (0.056868) 2025-03-20,03:42:20 | INFO | Train Epoch: 22 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.219, 147.542/s, 147.542/s/gpu LR: 0.000027 Logit Scale: 29.073 Contrastive_loss: 0.0040307 (0.056611) Loss: 0.0040307 (0.056611) 2025-03-20,03:42:41 | INFO | Train Epoch: 22 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 149.337/s, 149.337/s/gpu LR: 0.000027 Logit Scale: 29.078 Contrastive_loss: 0.0019103 (0.056347) Loss: 0.0019103 (0.056347) 2025-03-20,03:43:03 | INFO | Train Epoch: 22 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 149.227/s, 149.227/s/gpu LR: 0.000027 Logit Scale: 29.066 Contrastive_loss: 0.069759 (0.056411) Loss: 0.069759 (0.056411) 2025-03-20,03:43:24 | INFO | Train Epoch: 22 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.214, 148.368/s, 148.368/s/gpu LR: 0.000027 Logit Scale: 29.056 Contrastive_loss: 0.15134 (0.056866) Loss: 0.15134 (0.056866) 2025-03-20,03:43:46 | INFO | Train Epoch: 22 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 148.184/s, 148.184/s/gpu LR: 0.000027 Logit Scale: 29.059 Contrastive_loss: 0.0039972 (0.056614) Loss: 0.0039972 (0.056614) 2025-03-20,03:44:07 | INFO | Train Epoch: 22 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 149.141/s, 149.141/s/gpu LR: 0.000027 Logit Scale: 29.067 Contrastive_loss: 0.025541 (0.056467) Loss: 0.025541 (0.056467) 2025-03-20,03:44:29 | INFO | Train Epoch: 22 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 147.635/s, 147.635/s/gpu LR: 0.000027 Logit Scale: 29.077 Contrastive_loss: 0.070077 (0.056531) Loss: 0.070077 (0.056531) 2025-03-20,03:44:50 | INFO | Train Epoch: 22 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 150.020/s, 150.020/s/gpu LR: 0.000027 Logit Scale: 29.086 Contrastive_loss: 0.00031817 (0.056267) Loss: 0.00031817 (0.056267) 2025-03-20,03:45:12 | INFO | Train Epoch: 22 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 150.040/s, 150.040/s/gpu LR: 0.000027 Logit Scale: 29.099 Contrastive_loss: 0.031353 (0.056151) Loss: 0.031353 (0.056151) 2025-03-20,03:45:34 | INFO | Train Epoch: 22 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.219, 148.002/s, 148.002/s/gpu LR: 0.000027 Logit Scale: 29.098 Contrastive_loss: 0.044823 (0.056098) Loss: 0.044823 (0.056098) 2025-03-20,03:45:55 | INFO | Train Epoch: 22 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 150.237/s, 150.237/s/gpu LR: 0.000027 Logit Scale: 29.100 Contrastive_loss: 0.079819 (0.056208) Loss: 0.079819 (0.056208) 2025-03-20,03:46:17 | INFO | Train Epoch: 22 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 149.557/s, 149.557/s/gpu LR: 0.000027 Logit Scale: 29.110 Contrastive_loss: 0.068704 (0.056265) Loss: 0.068704 (0.056265) 2025-03-20,03:46:38 | INFO | Train Epoch: 22 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.300/s, 148.300/s/gpu LR: 0.000027 Logit Scale: 29.095 Contrastive_loss: 0.088480 (0.056413) Loss: 0.088480 (0.056413) 2025-03-20,03:47:00 | INFO | Train Epoch: 22 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 148.644/s, 148.644/s/gpu LR: 0.000027 Logit Scale: 29.091 Contrastive_loss: 0.034956 (0.056315) Loss: 0.034956 (0.056315) 2025-03-20,03:47:21 | INFO | Train Epoch: 22 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.252/s, 149.252/s/gpu LR: 0.000027 Logit Scale: 29.104 Contrastive_loss: 0.065849 (0.056358) Loss: 0.065849 (0.056358) 2025-03-20,03:47:43 | INFO | Train Epoch: 22 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 145.551/s, 145.551/s/gpu LR: 0.000027 Logit Scale: 29.108 Contrastive_loss: 0.029670 (0.056238) Loss: 0.029670 (0.056238) 2025-03-20,03:48:04 | INFO | Train Epoch: 22 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 149.995/s, 149.995/s/gpu LR: 0.000027 Logit Scale: 29.102 Contrastive_loss: 0.0019362 (0.055993) Loss: 0.0019362 (0.055993) 2025-03-20,03:48:26 | INFO | Train Epoch: 22 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 148.970/s, 148.970/s/gpu LR: 0.000027 Logit Scale: 29.087 Contrastive_loss: 0.024070 (0.055850) Loss: 0.024070 (0.055850) 2025-03-20,03:48:47 | INFO | Train Epoch: 22 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 150.966/s, 150.966/s/gpu LR: 0.000027 Logit Scale: 29.094 Contrastive_loss: 0.040347 (0.055781) Loss: 0.040347 (0.055781) 2025-03-20,03:49:09 | INFO | Train Epoch: 22 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 149.666/s, 149.666/s/gpu LR: 0.000027 Logit Scale: 29.101 Contrastive_loss: 0.058963 (0.055795) Loss: 0.058963 (0.055795) 2025-03-20,03:49:30 | INFO | Train Epoch: 22 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.507/s, 148.507/s/gpu LR: 0.000027 Logit Scale: 29.098 Contrastive_loss: 0.056934 (0.055800) Loss: 0.056934 (0.055800) 2025-03-20,03:49:52 | INFO | Train Epoch: 22 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.679/s, 148.679/s/gpu LR: 0.000027 Logit Scale: 29.095 Contrastive_loss: 0.064115 (0.055836) Loss: 0.064115 (0.055836) 2025-03-20,03:50:13 | INFO | Train Epoch: 22 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 148.916/s, 148.916/s/gpu LR: 0.000027 Logit Scale: 29.094 Contrastive_loss: 0.13642 (0.056190) Loss: 0.13642 (0.056190) 2025-03-20,03:50:35 | INFO | Train Epoch: 22 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 148.841/s, 148.841/s/gpu LR: 0.000027 Logit Scale: 29.095 Contrastive_loss: 0.00068906 (0.055948) Loss: 0.00068906 (0.055948) 2025-03-20,03:50:56 | INFO | Train Epoch: 22 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 148.948/s, 148.948/s/gpu LR: 0.000027 Logit Scale: 29.086 Contrastive_loss: 0.24012 (0.056748) Loss: 0.24012 (0.056748) 2025-03-20,03:51:18 | INFO | Train Epoch: 22 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 149.499/s, 149.499/s/gpu LR: 0.000027 Logit Scale: 29.078 Contrastive_loss: 0.077406 (0.056838) Loss: 0.077406 (0.056838) 2025-03-20,03:51:39 | INFO | Train Epoch: 22 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 149.710/s, 149.710/s/gpu LR: 0.000027 Logit Scale: 29.087 Contrastive_loss: 0.0045734 (0.056612) Loss: 0.0045734 (0.056612) 2025-03-20,03:52:01 | INFO | Train Epoch: 22 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.378/s, 149.378/s/gpu LR: 0.000027 Logit Scale: 29.087 Contrastive_loss: 0.071499 (0.056676) Loss: 0.071499 (0.056676) 2025-03-20,03:52:22 | INFO | Train Epoch: 22 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 148.977/s, 148.977/s/gpu LR: 0.000027 Logit Scale: 29.086 Contrastive_loss: 0.011008 (0.056481) Loss: 0.011008 (0.056481) 2025-03-20,03:52:44 | INFO | Train Epoch: 22 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 146.249/s, 146.249/s/gpu LR: 0.000027 Logit Scale: 29.078 Contrastive_loss: 0.14405 (0.056854) Loss: 0.14405 (0.056854) 2025-03-20,03:53:05 | INFO | Train Epoch: 22 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 149.432/s, 149.432/s/gpu LR: 0.000027 Logit Scale: 29.083 Contrastive_loss: 0.092455 (0.057005) Loss: 0.092455 (0.057005) 2025-03-20,03:53:27 | INFO | Train Epoch: 22 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.409/s, 149.409/s/gpu LR: 0.000026 Logit Scale: 29.074 Contrastive_loss: 0.021901 (0.056857) Loss: 0.021901 (0.056857) 2025-03-20,03:53:48 | INFO | Train Epoch: 22 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.453/s, 149.453/s/gpu LR: 0.000026 Logit Scale: 29.073 Contrastive_loss: 0.043657 (0.056801) Loss: 0.043657 (0.056801) 2025-03-20,03:54:10 | INFO | Train Epoch: 22 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 147.427/s, 147.427/s/gpu LR: 0.000026 Logit Scale: 29.067 Contrastive_loss: 0.048633 (0.056767) Loss: 0.048633 (0.056767) 2025-03-20,03:54:32 | INFO | Train Epoch: 22 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.218, 146.200/s, 146.200/s/gpu LR: 0.000026 Logit Scale: 29.075 Contrastive_loss: 0.027700 (0.056646) Loss: 0.027700 (0.056646) 2025-03-20,03:54:39 | INFO | Train Epoch: 22 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.220, 146.832/s, 146.832/s/gpu LR: 0.000026 Logit Scale: 29.079 Contrastive_loss: 0.012207 (0.056461) Loss: 0.012207 (0.056461) 2025-03-20,03:54:40 | INFO | Eval Epoch: 23 [32 / 7443] Clip Loss: 4.364478 2025-03-20,03:54:45 | INFO | Eval Epoch: 23 [3232 / 7443] Clip Loss: 0.861571 2025-03-20,03:54:51 | INFO | Eval Epoch: 23 [6432 / 7443] Clip Loss: 0.640339 2025-03-20,03:54:54 | INFO | Eval Epoch: 23 image_to_text_mean_rank: 80.9969 image_to_text_median_rank: 5.0000 image_to_text_R@1: 0.1808 image_to_text_R@5: 0.5320 image_to_text_R@10: 0.6981 text_to_image_mean_rank: 48.6499 text_to_image_median_rank: 5.0000 text_to_image_R@1: 0.1874 text_to_image_R@5: 0.5194 text_to_image_R@10: 0.6864 clip_val_loss: 0.5935 epoch: 23.0000 num_samples: 7443.0000 2025-03-20,03:55:27 | INFO | Start epoch 23 2025-03-20,03:55:28 | INFO | Train Epoch: 23 [ 32/766009 (0%)] Data (t): 0.178 Batch (t): 0.379, 84.4943/s, 84.4943/s/gpu LR: 0.000026 Logit Scale: 29.079 Contrastive_loss: 0.0082439 (0.0082439) Loss: 0.0082439 (0.0082439) 2025-03-20,03:55:49 | INFO | Train Epoch: 23 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.215, 148.487/s, 148.487/s/gpu LR: 0.000026 Logit Scale: 29.090 Contrastive_loss: 0.00030955 (0.0042767) Loss: 0.00030955 (0.0042767) 2025-03-20,03:56:11 | INFO | Train Epoch: 23 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.215, 149.316/s, 149.316/s/gpu LR: 0.000026 Logit Scale: 29.094 Contrastive_loss: 0.044619 (0.017724) Loss: 0.044619 (0.017724) 2025-03-20,03:56:32 | INFO | Train Epoch: 23 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.214, 150.268/s, 150.268/s/gpu LR: 0.000026 Logit Scale: 29.099 Contrastive_loss: 0.059617 (0.028197) Loss: 0.059617 (0.028197) 2025-03-20,03:56:54 | INFO | Train Epoch: 23 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 149.435/s, 149.435/s/gpu LR: 0.000026 Logit Scale: 29.101 Contrastive_loss: 0.0028776 (0.023133) Loss: 0.0028776 (0.023133) 2025-03-20,03:57:15 | INFO | Train Epoch: 23 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.213, 151.156/s, 151.156/s/gpu LR: 0.000026 Logit Scale: 29.104 Contrastive_loss: 0.016210 (0.021979) Loss: 0.016210 (0.021979) 2025-03-20,03:57:36 | INFO | Train Epoch: 23 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 151.354/s, 151.354/s/gpu LR: 0.000026 Logit Scale: 29.115 Contrastive_loss: 0.0039804 (0.019408) Loss: 0.0039804 (0.019408) 2025-03-20,03:57:57 | INFO | Train Epoch: 23 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 150.339/s, 150.339/s/gpu LR: 0.000026 Logit Scale: 29.124 Contrastive_loss: 0.0018521 (0.017214) Loss: 0.0018521 (0.017214) 2025-03-20,03:58:19 | INFO | Train Epoch: 23 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 150.104/s, 150.104/s/gpu LR: 0.000026 Logit Scale: 29.131 Contrastive_loss: 0.031657 (0.018818) Loss: 0.031657 (0.018818) 2025-03-20,03:58:40 | INFO | Train Epoch: 23 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.211, 151.605/s, 151.605/s/gpu LR: 0.000026 Logit Scale: 29.141 Contrastive_loss: 0.00013871 (0.016950) Loss: 0.00013871 (0.016950) 2025-03-20,03:59:02 | INFO | Train Epoch: 23 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.214, 148.426/s, 148.426/s/gpu LR: 0.000026 Logit Scale: 29.143 Contrastive_loss: 0.10751 (0.025183) Loss: 0.10751 (0.025183) 2025-03-20,03:59:23 | INFO | Train Epoch: 23 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.213, 150.535/s, 150.535/s/gpu LR: 0.000026 Logit Scale: 29.147 Contrastive_loss: 0.049494 (0.027209) Loss: 0.049494 (0.027209) 2025-03-20,03:59:44 | INFO | Train Epoch: 23 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 151.921/s, 151.921/s/gpu LR: 0.000026 Logit Scale: 29.150 Contrastive_loss: 0.013516 (0.026156) Loss: 0.013516 (0.026156) 2025-03-20,04:00:05 | INFO | Train Epoch: 23 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.212, 148.241/s, 148.241/s/gpu LR: 0.000026 Logit Scale: 29.152 Contrastive_loss: 0.039182 (0.027086) Loss: 0.039182 (0.027086) 2025-03-20,04:00:27 | INFO | Train Epoch: 23 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 149.305/s, 149.305/s/gpu LR: 0.000026 Logit Scale: 29.168 Contrastive_loss: 0.021022 (0.026682) Loss: 0.021022 (0.026682) 2025-03-20,04:00:49 | INFO | Train Epoch: 23 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 148.384/s, 148.384/s/gpu LR: 0.000026 Logit Scale: 29.172 Contrastive_loss: 0.0083299 (0.025535) Loss: 0.0083299 (0.025535) 2025-03-20,04:01:10 | INFO | Train Epoch: 23 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.901/s, 149.901/s/gpu LR: 0.000026 Logit Scale: 29.179 Contrastive_loss: 0.018845 (0.025141) Loss: 0.018845 (0.025141) 2025-03-20,04:01:32 | INFO | Train Epoch: 23 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 149.983/s, 149.983/s/gpu LR: 0.000026 Logit Scale: 29.178 Contrastive_loss: 0.0011826 (0.023810) Loss: 0.0011826 (0.023810) 2025-03-20,04:01:53 | INFO | Train Epoch: 23 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.213, 151.505/s, 151.505/s/gpu LR: 0.000026 Logit Scale: 29.174 Contrastive_loss: 0.053881 (0.025393) Loss: 0.053881 (0.025393) 2025-03-20,04:02:14 | INFO | Train Epoch: 23 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.197/s, 149.197/s/gpu LR: 0.000026 Logit Scale: 29.183 Contrastive_loss: 0.055127 (0.026880) Loss: 0.055127 (0.026880) 2025-03-20,04:02:36 | INFO | Train Epoch: 23 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 148.747/s, 148.747/s/gpu LR: 0.000026 Logit Scale: 29.186 Contrastive_loss: 0.030916 (0.027072) Loss: 0.030916 (0.027072) 2025-03-20,04:02:58 | INFO | Train Epoch: 23 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.218, 145.556/s, 145.556/s/gpu LR: 0.000026 Logit Scale: 29.181 Contrastive_loss: 0.0016401 (0.025916) Loss: 0.0016401 (0.025916) 2025-03-20,04:03:19 | INFO | Train Epoch: 23 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.218, 147.068/s, 147.068/s/gpu LR: 0.000026 Logit Scale: 29.179 Contrastive_loss: 0.0017701 (0.024866) Loss: 0.0017701 (0.024866) 2025-03-20,04:03:41 | INFO | Train Epoch: 23 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 145.508/s, 145.508/s/gpu LR: 0.000026 Logit Scale: 29.180 Contrastive_loss: 0.048523 (0.025852) Loss: 0.048523 (0.025852) 2025-03-20,04:04:03 | INFO | Train Epoch: 23 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 147.846/s, 147.846/s/gpu LR: 0.000026 Logit Scale: 29.179 Contrastive_loss: 0.040900 (0.026454) Loss: 0.040900 (0.026454) 2025-03-20,04:04:25 | INFO | Train Epoch: 23 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.219, 147.534/s, 147.534/s/gpu LR: 0.000026 Logit Scale: 29.178 Contrastive_loss: 0.018999 (0.026167) Loss: 0.018999 (0.026167) 2025-03-20,04:04:47 | INFO | Train Epoch: 23 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.220, 147.551/s, 147.551/s/gpu LR: 0.000026 Logit Scale: 29.190 Contrastive_loss: 0.0035975 (0.025331) Loss: 0.0035975 (0.025331) 2025-03-20,04:05:09 | INFO | Train Epoch: 23 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.218, 146.570/s, 146.570/s/gpu LR: 0.000026 Logit Scale: 29.186 Contrastive_loss: 7.7860e-05 (0.024429) Loss: 7.7860e-05 (0.024429) 2025-03-20,04:05:31 | INFO | Train Epoch: 23 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.221, 144.115/s, 144.115/s/gpu LR: 0.000026 Logit Scale: 29.183 Contrastive_loss: 0.046155 (0.025178) Loss: 0.046155 (0.025178) 2025-03-20,04:05:53 | INFO | Train Epoch: 23 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.221, 145.793/s, 145.793/s/gpu LR: 0.000026 Logit Scale: 29.183 Contrastive_loss: 0.0040655 (0.024475) Loss: 0.0040655 (0.024475) 2025-03-20,04:06:15 | INFO | Train Epoch: 23 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.220, 144.743/s, 144.743/s/gpu LR: 0.000025 Logit Scale: 29.189 Contrastive_loss: 0.10091 (0.026940) Loss: 0.10091 (0.026940) 2025-03-20,04:06:37 | INFO | Train Epoch: 23 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.220, 145.722/s, 145.722/s/gpu LR: 0.000025 Logit Scale: 29.206 Contrastive_loss: 0.030752 (0.027059) Loss: 0.030752 (0.027059) 2025-03-20,04:06:59 | INFO | Train Epoch: 23 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.219, 148.359/s, 148.359/s/gpu LR: 0.000025 Logit Scale: 29.208 Contrastive_loss: 0.026804 (0.027052) Loss: 0.026804 (0.027052) 2025-03-20,04:07:20 | INFO | Train Epoch: 23 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 148.078/s, 148.078/s/gpu LR: 0.000025 Logit Scale: 29.215 Contrastive_loss: 0.11409 (0.029612) Loss: 0.11409 (0.029612) 2025-03-20,04:07:42 | INFO | Train Epoch: 23 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.219, 148.316/s, 148.316/s/gpu LR: 0.000025 Logit Scale: 29.208 Contrastive_loss: 0.011580 (0.029096) Loss: 0.011580 (0.029096) 2025-03-20,04:08:04 | INFO | Train Epoch: 23 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 149.252/s, 149.252/s/gpu LR: 0.000025 Logit Scale: 29.211 Contrastive_loss: 0.0040138 (0.028400) Loss: 0.0040138 (0.028400) 2025-03-20,04:08:26 | INFO | Train Epoch: 23 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.218, 150.190/s, 150.190/s/gpu LR: 0.000025 Logit Scale: 29.233 Contrastive_loss: 0.0015095 (0.027673) Loss: 0.0015095 (0.027673) 2025-03-20,04:08:48 | INFO | Train Epoch: 23 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.177/s, 148.177/s/gpu LR: 0.000025 Logit Scale: 29.235 Contrastive_loss: 0.053323 (0.028348) Loss: 0.053323 (0.028348) 2025-03-20,04:09:09 | INFO | Train Epoch: 23 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.218, 146.658/s, 146.658/s/gpu LR: 0.000025 Logit Scale: 29.214 Contrastive_loss: 0.11905 (0.030674) Loss: 0.11905 (0.030674) 2025-03-20,04:09:31 | INFO | Train Epoch: 23 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.221, 144.073/s, 144.073/s/gpu LR: 0.000025 Logit Scale: 29.207 Contrastive_loss: 0.034813 (0.030777) Loss: 0.034813 (0.030777) 2025-03-20,04:09:53 | INFO | Train Epoch: 23 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.218, 146.705/s, 146.705/s/gpu LR: 0.000025 Logit Scale: 29.197 Contrastive_loss: 0.051161 (0.031274) Loss: 0.051161 (0.031274) 2025-03-20,04:10:15 | INFO | Train Epoch: 23 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.219, 147.225/s, 147.225/s/gpu LR: 0.000025 Logit Scale: 29.202 Contrastive_loss: 0.061823 (0.032002) Loss: 0.061823 (0.032002) 2025-03-20,04:10:37 | INFO | Train Epoch: 23 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.218, 148.071/s, 148.071/s/gpu LR: 0.000025 Logit Scale: 29.204 Contrastive_loss: 0.052350 (0.032475) Loss: 0.052350 (0.032475) 2025-03-20,04:10:59 | INFO | Train Epoch: 23 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.219, 147.119/s, 147.119/s/gpu LR: 0.000025 Logit Scale: 29.206 Contrastive_loss: 0.0050676 (0.031852) Loss: 0.0050676 (0.031852) 2025-03-20,04:11:20 | INFO | Train Epoch: 23 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 148.542/s, 148.542/s/gpu LR: 0.000025 Logit Scale: 29.211 Contrastive_loss: 0.023800 (0.031673) Loss: 0.023800 (0.031673) 2025-03-20,04:11:42 | INFO | Train Epoch: 23 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.215, 148.861/s, 148.861/s/gpu LR: 0.000025 Logit Scale: 29.210 Contrastive_loss: 0.031053 (0.031659) Loss: 0.031053 (0.031659) 2025-03-20,04:12:03 | INFO | Train Epoch: 23 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 150.214/s, 150.214/s/gpu LR: 0.000025 Logit Scale: 29.211 Contrastive_loss: 0.043967 (0.031921) Loss: 0.043967 (0.031921) 2025-03-20,04:12:25 | INFO | Train Epoch: 23 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.123/s, 149.123/s/gpu LR: 0.000025 Logit Scale: 29.216 Contrastive_loss: 0.037035 (0.032028) Loss: 0.037035 (0.032028) 2025-03-20,04:12:47 | INFO | Train Epoch: 23 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 148.872/s, 148.872/s/gpu LR: 0.000025 Logit Scale: 29.230 Contrastive_loss: 0.0069738 (0.031517) Loss: 0.0069738 (0.031517) 2025-03-20,04:13:08 | INFO | Train Epoch: 23 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 148.740/s, 148.740/s/gpu LR: 0.000025 Logit Scale: 29.233 Contrastive_loss: 0.0037353 (0.030961) Loss: 0.0037353 (0.030961) 2025-03-20,04:13:30 | INFO | Train Epoch: 23 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 146.745/s, 146.745/s/gpu LR: 0.000025 Logit Scale: 29.252 Contrastive_loss: 0.011935 (0.030588) Loss: 0.011935 (0.030588) 2025-03-20,04:13:52 | INFO | Train Epoch: 23 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 147.925/s, 147.925/s/gpu LR: 0.000025 Logit Scale: 29.266 Contrastive_loss: 0.063656 (0.031224) Loss: 0.063656 (0.031224) 2025-03-20,04:14:13 | INFO | Train Epoch: 23 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.217, 148.700/s, 148.700/s/gpu LR: 0.000025 Logit Scale: 29.266 Contrastive_loss: 0.061836 (0.031801) Loss: 0.061836 (0.031801) 2025-03-20,04:14:35 | INFO | Train Epoch: 23 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.218, 146.341/s, 146.341/s/gpu LR: 0.000025 Logit Scale: 29.273 Contrastive_loss: 0.00091742 (0.031229) Loss: 0.00091742 (0.031229) 2025-03-20,04:14:57 | INFO | Train Epoch: 23 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 147.762/s, 147.762/s/gpu LR: 0.000025 Logit Scale: 29.265 Contrastive_loss: 0.031555 (0.031235) Loss: 0.031555 (0.031235) 2025-03-20,04:15:18 | INFO | Train Epoch: 23 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 149.335/s, 149.335/s/gpu LR: 0.000025 Logit Scale: 29.263 Contrastive_loss: 0.013479 (0.030918) Loss: 0.013479 (0.030918) 2025-03-20,04:15:40 | INFO | Train Epoch: 23 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.089/s, 149.089/s/gpu LR: 0.000025 Logit Scale: 29.267 Contrastive_loss: 0.035435 (0.030998) Loss: 0.035435 (0.030998) 2025-03-20,04:16:01 | INFO | Train Epoch: 23 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 148.371/s, 148.371/s/gpu LR: 0.000025 Logit Scale: 29.257 Contrastive_loss: 0.085258 (0.031933) Loss: 0.085258 (0.031933) 2025-03-20,04:16:23 | INFO | Train Epoch: 23 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.212, 150.855/s, 150.855/s/gpu LR: 0.000025 Logit Scale: 29.266 Contrastive_loss: 0.011462 (0.031586) Loss: 0.011462 (0.031586) 2025-03-20,04:16:44 | INFO | Train Epoch: 23 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.211, 151.999/s, 151.999/s/gpu LR: 0.000025 Logit Scale: 29.261 Contrastive_loss: 0.0055762 (0.031153) Loss: 0.0055762 (0.031153) 2025-03-20,04:17:05 | INFO | Train Epoch: 23 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.213, 151.301/s, 151.301/s/gpu LR: 0.000025 Logit Scale: 29.272 Contrastive_loss: 0.044842 (0.031377) Loss: 0.044842 (0.031377) 2025-03-20,04:17:26 | INFO | Train Epoch: 23 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 148.087/s, 148.087/s/gpu LR: 0.000025 Logit Scale: 29.277 Contrastive_loss: 0.048373 (0.031651) Loss: 0.048373 (0.031651) 2025-03-20,04:17:48 | INFO | Train Epoch: 23 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 148.970/s, 148.970/s/gpu LR: 0.000025 Logit Scale: 29.272 Contrastive_loss: 0.052863 (0.031988) Loss: 0.052863 (0.031988) 2025-03-20,04:18:10 | INFO | Train Epoch: 23 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 145.951/s, 145.951/s/gpu LR: 0.000025 Logit Scale: 29.262 Contrastive_loss: 8.3039e-05 (0.031489) Loss: 8.3039e-05 (0.031489) 2025-03-20,04:18:31 | INFO | Train Epoch: 23 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 148.710/s, 148.710/s/gpu LR: 0.000024 Logit Scale: 29.275 Contrastive_loss: 0.069662 (0.032077) Loss: 0.069662 (0.032077) 2025-03-20,04:18:52 | INFO | Train Epoch: 23 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.213, 150.204/s, 150.204/s/gpu LR: 0.000024 Logit Scale: 29.277 Contrastive_loss: 0.043572 (0.032251) Loss: 0.043572 (0.032251) 2025-03-20,04:19:14 | INFO | Train Epoch: 23 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.212, 151.469/s, 151.469/s/gpu LR: 0.000024 Logit Scale: 29.274 Contrastive_loss: 0.0052090 (0.031847) Loss: 0.0052090 (0.031847) 2025-03-20,04:19:35 | INFO | Train Epoch: 23 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.305/s, 149.305/s/gpu LR: 0.000024 Logit Scale: 29.283 Contrastive_loss: 0.026013 (0.031761) Loss: 0.026013 (0.031761) 2025-03-20,04:19:56 | INFO | Train Epoch: 23 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 146.837/s, 146.837/s/gpu LR: 0.000024 Logit Scale: 29.289 Contrastive_loss: 0.12300 (0.033084) Loss: 0.12300 (0.033084) 2025-03-20,04:20:19 | INFO | Train Epoch: 23 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.222, 145.428/s, 145.428/s/gpu LR: 0.000024 Logit Scale: 29.283 Contrastive_loss: 0.043768 (0.033236) Loss: 0.043768 (0.033236) 2025-03-20,04:20:40 | INFO | Train Epoch: 23 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 149.613/s, 149.613/s/gpu LR: 0.000024 Logit Scale: 29.285 Contrastive_loss: 0.0023585 (0.032801) Loss: 0.0023585 (0.032801) 2025-03-20,04:21:02 | INFO | Train Epoch: 23 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 151.093/s, 151.093/s/gpu LR: 0.000024 Logit Scale: 29.277 Contrastive_loss: 0.082081 (0.033486) Loss: 0.082081 (0.033486) 2025-03-20,04:21:23 | INFO | Train Epoch: 23 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 146.664/s, 146.664/s/gpu LR: 0.000024 Logit Scale: 29.287 Contrastive_loss: 0.086727 (0.034215) Loss: 0.086727 (0.034215) 2025-03-20,04:21:45 | INFO | Train Epoch: 23 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 150.270/s, 150.270/s/gpu LR: 0.000024 Logit Scale: 29.283 Contrastive_loss: 0.021966 (0.034050) Loss: 0.021966 (0.034050) 2025-03-20,04:22:07 | INFO | Train Epoch: 23 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.218, 146.202/s, 146.202/s/gpu LR: 0.000024 Logit Scale: 29.292 Contrastive_loss: 0.0042782 (0.033653) Loss: 0.0042782 (0.033653) 2025-03-20,04:22:28 | INFO | Train Epoch: 23 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 144.401/s, 144.401/s/gpu LR: 0.000024 Logit Scale: 29.296 Contrastive_loss: 0.0028656 (0.033248) Loss: 0.0028656 (0.033248) 2025-03-20,04:22:50 | INFO | Train Epoch: 23 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 149.649/s, 149.649/s/gpu LR: 0.000024 Logit Scale: 29.295 Contrastive_loss: 0.026972 (0.033166) Loss: 0.026972 (0.033166) 2025-03-20,04:23:11 | INFO | Train Epoch: 23 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.214, 147.944/s, 147.944/s/gpu LR: 0.000024 Logit Scale: 29.304 Contrastive_loss: 0.026676 (0.033083) Loss: 0.026676 (0.033083) 2025-03-20,04:23:33 | INFO | Train Epoch: 23 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.218, 149.197/s, 149.197/s/gpu LR: 0.000024 Logit Scale: 29.316 Contrastive_loss: 0.085765 (0.033750) Loss: 0.085765 (0.033750) 2025-03-20,04:23:55 | INFO | Train Epoch: 23 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 148.720/s, 148.720/s/gpu LR: 0.000024 Logit Scale: 29.320 Contrastive_loss: 0.11803 (0.034803) Loss: 0.11803 (0.034803) 2025-03-20,04:24:16 | INFO | Train Epoch: 23 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.206/s, 149.206/s/gpu LR: 0.000024 Logit Scale: 29.320 Contrastive_loss: 0.087845 (0.035458) Loss: 0.087845 (0.035458) 2025-03-20,04:24:38 | INFO | Train Epoch: 23 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.633/s, 149.633/s/gpu LR: 0.000024 Logit Scale: 29.319 Contrastive_loss: 0.091632 (0.036143) Loss: 0.091632 (0.036143) 2025-03-20,04:24:59 | INFO | Train Epoch: 23 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 147.949/s, 147.949/s/gpu LR: 0.000024 Logit Scale: 29.321 Contrastive_loss: 0.026048 (0.036022) Loss: 0.026048 (0.036022) 2025-03-20,04:25:21 | INFO | Train Epoch: 23 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.394/s, 148.394/s/gpu LR: 0.000024 Logit Scale: 29.329 Contrastive_loss: 0.0013877 (0.035609) Loss: 0.0013877 (0.035609) 2025-03-20,04:25:42 | INFO | Train Epoch: 23 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 149.563/s, 149.563/s/gpu LR: 0.000024 Logit Scale: 29.335 Contrastive_loss: 0.051497 (0.035796) Loss: 0.051497 (0.035796) 2025-03-20,04:26:04 | INFO | Train Epoch: 23 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 148.263/s, 148.263/s/gpu LR: 0.000024 Logit Scale: 29.349 Contrastive_loss: 0.17809 (0.037451) Loss: 0.17809 (0.037451) 2025-03-20,04:26:25 | INFO | Train Epoch: 23 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.645/s, 149.645/s/gpu LR: 0.000024 Logit Scale: 29.341 Contrastive_loss: 0.0033989 (0.037059) Loss: 0.0033989 (0.037059) 2025-03-20,04:26:47 | INFO | Train Epoch: 23 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.887/s, 149.887/s/gpu LR: 0.000024 Logit Scale: 29.340 Contrastive_loss: 0.0087218 (0.036737) Loss: 0.0087218 (0.036737) 2025-03-20,04:27:08 | INFO | Train Epoch: 23 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 149.499/s, 149.499/s/gpu LR: 0.000024 Logit Scale: 29.339 Contrastive_loss: 0.11407 (0.037606) Loss: 0.11407 (0.037606) 2025-03-20,04:27:29 | INFO | Train Epoch: 23 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 150.076/s, 150.076/s/gpu LR: 0.000024 Logit Scale: 29.333 Contrastive_loss: 0.0062266 (0.037258) Loss: 0.0062266 (0.037258) 2025-03-20,04:27:51 | INFO | Train Epoch: 23 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 150.589/s, 150.589/s/gpu LR: 0.000024 Logit Scale: 29.337 Contrastive_loss: 0.012487 (0.036985) Loss: 0.012487 (0.036985) 2025-03-20,04:28:12 | INFO | Train Epoch: 23 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.815/s, 149.815/s/gpu LR: 0.000024 Logit Scale: 29.344 Contrastive_loss: 0.12623 (0.037955) Loss: 0.12623 (0.037955) 2025-03-20,04:28:33 | INFO | Train Epoch: 23 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.211, 151.627/s, 151.627/s/gpu LR: 0.000024 Logit Scale: 29.356 Contrastive_loss: 0.062825 (0.038223) Loss: 0.062825 (0.038223) 2025-03-20,04:28:55 | INFO | Train Epoch: 23 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.211, 150.426/s, 150.426/s/gpu LR: 0.000024 Logit Scale: 29.361 Contrastive_loss: 0.10608 (0.038945) Loss: 0.10608 (0.038945) 2025-03-20,04:29:16 | INFO | Train Epoch: 23 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.212, 151.991/s, 151.991/s/gpu LR: 0.000024 Logit Scale: 29.350 Contrastive_loss: 0.0059451 (0.038597) Loss: 0.0059451 (0.038597) 2025-03-20,04:29:37 | INFO | Train Epoch: 23 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.916/s, 149.916/s/gpu LR: 0.000024 Logit Scale: 29.344 Contrastive_loss: 0.0017641 (0.038214) Loss: 0.0017641 (0.038214) 2025-03-20,04:29:59 | INFO | Train Epoch: 23 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.218, 146.097/s, 146.097/s/gpu LR: 0.000024 Logit Scale: 29.348 Contrastive_loss: 0.24889 (0.040386) Loss: 0.24889 (0.040386) 2025-03-20,04:30:21 | INFO | Train Epoch: 23 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.220, 148.261/s, 148.261/s/gpu LR: 0.000024 Logit Scale: 29.340 Contrastive_loss: 0.012600 (0.040102) Loss: 0.012600 (0.040102) 2025-03-20,04:30:42 | INFO | Train Epoch: 23 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 151.232/s, 151.232/s/gpu LR: 0.000024 Logit Scale: 29.340 Contrastive_loss: 0.089654 (0.040603) Loss: 0.089654 (0.040603) 2025-03-20,04:31:04 | INFO | Train Epoch: 23 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 150.858/s, 150.858/s/gpu LR: 0.000023 Logit Scale: 29.342 Contrastive_loss: 0.030507 (0.040502) Loss: 0.030507 (0.040502) 2025-03-20,04:31:25 | INFO | Train Epoch: 23 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 149.685/s, 149.685/s/gpu LR: 0.000023 Logit Scale: 29.335 Contrastive_loss: 0.00054380 (0.040106) Loss: 0.00054380 (0.040106) 2025-03-20,04:31:47 | INFO | Train Epoch: 23 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 149.771/s, 149.771/s/gpu LR: 0.000023 Logit Scale: 29.338 Contrastive_loss: 0.048275 (0.040186) Loss: 0.048275 (0.040186) 2025-03-20,04:32:08 | INFO | Train Epoch: 23 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 149.202/s, 149.202/s/gpu LR: 0.000023 Logit Scale: 29.350 Contrastive_loss: 0.023430 (0.040023) Loss: 0.023430 (0.040023) 2025-03-20,04:32:30 | INFO | Train Epoch: 23 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 151.030/s, 151.030/s/gpu LR: 0.000023 Logit Scale: 29.345 Contrastive_loss: 0.0079123 (0.039715) Loss: 0.0079123 (0.039715) 2025-03-20,04:32:51 | INFO | Train Epoch: 23 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.989/s, 149.989/s/gpu LR: 0.000023 Logit Scale: 29.338 Contrastive_loss: 0.016770 (0.039496) Loss: 0.016770 (0.039496) 2025-03-20,04:33:13 | INFO | Train Epoch: 23 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.215, 150.235/s, 150.235/s/gpu LR: 0.000023 Logit Scale: 29.338 Contrastive_loss: 0.013653 (0.039252) Loss: 0.013653 (0.039252) 2025-03-20,04:33:34 | INFO | Train Epoch: 23 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.379/s, 148.379/s/gpu LR: 0.000023 Logit Scale: 29.337 Contrastive_loss: 0.028928 (0.039156) Loss: 0.028928 (0.039156) 2025-03-20,04:33:56 | INFO | Train Epoch: 23 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.649/s, 149.649/s/gpu LR: 0.000023 Logit Scale: 29.341 Contrastive_loss: 0.10305 (0.039747) Loss: 0.10305 (0.039747) 2025-03-20,04:34:17 | INFO | Train Epoch: 23 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 149.114/s, 149.114/s/gpu LR: 0.000023 Logit Scale: 29.332 Contrastive_loss: 0.10799 (0.040374) Loss: 0.10799 (0.040374) 2025-03-20,04:34:39 | INFO | Train Epoch: 23 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 147.977/s, 147.977/s/gpu LR: 0.000023 Logit Scale: 29.330 Contrastive_loss: 0.0053811 (0.040055) Loss: 0.0053811 (0.040055) 2025-03-20,04:35:00 | INFO | Train Epoch: 23 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 148.304/s, 148.304/s/gpu LR: 0.000023 Logit Scale: 29.331 Contrastive_loss: 0.0015006 (0.039708) Loss: 0.0015006 (0.039708) 2025-03-20,04:35:22 | INFO | Train Epoch: 23 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 149.448/s, 149.448/s/gpu LR: 0.000023 Logit Scale: 29.330 Contrastive_loss: 0.086737 (0.040128) Loss: 0.086737 (0.040128) 2025-03-20,04:35:43 | INFO | Train Epoch: 23 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 147.999/s, 147.999/s/gpu LR: 0.000023 Logit Scale: 29.341 Contrastive_loss: 0.049625 (0.040212) Loss: 0.049625 (0.040212) 2025-03-20,04:36:05 | INFO | Train Epoch: 23 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 147.975/s, 147.975/s/gpu LR: 0.000023 Logit Scale: 29.349 Contrastive_loss: 0.077487 (0.040539) Loss: 0.077487 (0.040539) 2025-03-20,04:36:27 | INFO | Train Epoch: 23 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.334/s, 149.334/s/gpu LR: 0.000023 Logit Scale: 29.352 Contrastive_loss: 0.058777 (0.040698) Loss: 0.058777 (0.040698) 2025-03-20,04:36:48 | INFO | Train Epoch: 23 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 147.663/s, 147.663/s/gpu LR: 0.000023 Logit Scale: 29.355 Contrastive_loss: 0.074994 (0.040993) Loss: 0.074994 (0.040993) 2025-03-20,04:37:10 | INFO | Train Epoch: 23 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 149.447/s, 149.447/s/gpu LR: 0.000023 Logit Scale: 29.362 Contrastive_loss: 0.093994 (0.041446) Loss: 0.093994 (0.041446) 2025-03-20,04:37:31 | INFO | Train Epoch: 23 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 148.458/s, 148.458/s/gpu LR: 0.000023 Logit Scale: 29.369 Contrastive_loss: 0.061760 (0.041618) Loss: 0.061760 (0.041618) 2025-03-20,04:37:53 | INFO | Train Epoch: 23 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 149.666/s, 149.666/s/gpu LR: 0.000023 Logit Scale: 29.369 Contrastive_loss: 0.0061997 (0.041321) Loss: 0.0061997 (0.041321) 2025-03-20,04:38:15 | INFO | Train Epoch: 23 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.218, 144.426/s, 144.426/s/gpu LR: 0.000023 Logit Scale: 29.371 Contrastive_loss: 0.11995 (0.041976) Loss: 0.11995 (0.041976) 2025-03-20,04:38:36 | INFO | Train Epoch: 23 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.219, 147.232/s, 147.232/s/gpu LR: 0.000023 Logit Scale: 29.380 Contrastive_loss: 0.0018794 (0.041645) Loss: 0.0018794 (0.041645) 2025-03-20,04:38:58 | INFO | Train Epoch: 23 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 151.113/s, 151.113/s/gpu LR: 0.000023 Logit Scale: 29.386 Contrastive_loss: 0.036257 (0.041600) Loss: 0.036257 (0.041600) 2025-03-20,04:39:19 | INFO | Train Epoch: 23 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.213, 147.991/s, 147.991/s/gpu LR: 0.000023 Logit Scale: 29.376 Contrastive_loss: 0.13385 (0.042350) Loss: 0.13385 (0.042350) 2025-03-20,04:39:41 | INFO | Train Epoch: 23 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.220, 142.936/s, 142.936/s/gpu LR: 0.000023 Logit Scale: 29.371 Contrastive_loss: 0.054980 (0.042452) Loss: 0.054980 (0.042452) 2025-03-20,04:40:03 | INFO | Train Epoch: 23 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 150.237/s, 150.237/s/gpu LR: 0.000023 Logit Scale: 29.368 Contrastive_loss: 0.074459 (0.042708) Loss: 0.074459 (0.042708) 2025-03-20,04:40:24 | INFO | Train Epoch: 23 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 149.470/s, 149.470/s/gpu LR: 0.000023 Logit Scale: 29.372 Contrastive_loss: 0.13328 (0.043427) Loss: 0.13328 (0.043427) 2025-03-20,04:40:46 | INFO | Train Epoch: 23 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 150.293/s, 150.293/s/gpu LR: 0.000023 Logit Scale: 29.376 Contrastive_loss: 0.046420 (0.043451) Loss: 0.046420 (0.043451) 2025-03-20,04:41:07 | INFO | Train Epoch: 23 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 148.988/s, 148.988/s/gpu LR: 0.000023 Logit Scale: 29.372 Contrastive_loss: 0.048729 (0.043492) Loss: 0.048729 (0.043492) 2025-03-20,04:41:29 | INFO | Train Epoch: 23 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 147.862/s, 147.862/s/gpu LR: 0.000023 Logit Scale: 29.376 Contrastive_loss: 0.13358 (0.044190) Loss: 0.13358 (0.044190) 2025-03-20,04:41:50 | INFO | Train Epoch: 23 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.217, 146.117/s, 146.117/s/gpu LR: 0.000023 Logit Scale: 29.374 Contrastive_loss: 0.046827 (0.044211) Loss: 0.046827 (0.044211) 2025-03-20,04:42:12 | INFO | Train Epoch: 23 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.218, 148.943/s, 148.943/s/gpu LR: 0.000023 Logit Scale: 29.379 Contrastive_loss: 0.014474 (0.043984) Loss: 0.014474 (0.043984) 2025-03-20,04:42:34 | INFO | Train Epoch: 23 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 146.090/s, 146.090/s/gpu LR: 0.000023 Logit Scale: 29.383 Contrastive_loss: 0.043019 (0.043976) Loss: 0.043019 (0.043976) 2025-03-20,04:42:56 | INFO | Train Epoch: 23 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 147.907/s, 147.907/s/gpu LR: 0.000023 Logit Scale: 29.396 Contrastive_loss: 0.032276 (0.043888) Loss: 0.032276 (0.043888) 2025-03-20,04:43:17 | INFO | Train Epoch: 23 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 148.487/s, 148.487/s/gpu LR: 0.000023 Logit Scale: 29.409 Contrastive_loss: 0.063303 (0.044033) Loss: 0.063303 (0.044033) 2025-03-20,04:43:39 | INFO | Train Epoch: 23 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 148.658/s, 148.658/s/gpu LR: 0.000022 Logit Scale: 29.405 Contrastive_loss: 0.080844 (0.044306) Loss: 0.080844 (0.044306) 2025-03-20,04:44:00 | INFO | Train Epoch: 23 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 147.476/s, 147.476/s/gpu LR: 0.000022 Logit Scale: 29.408 Contrastive_loss: 0.025207 (0.044165) Loss: 0.025207 (0.044165) 2025-03-20,04:44:22 | INFO | Train Epoch: 23 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.220, 144.973/s, 144.973/s/gpu LR: 0.000022 Logit Scale: 29.415 Contrastive_loss: 0.016546 (0.043964) Loss: 0.016546 (0.043964) 2025-03-20,04:44:44 | INFO | Train Epoch: 23 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 151.261/s, 151.261/s/gpu LR: 0.000022 Logit Scale: 29.406 Contrastive_loss: 0.0074388 (0.043699) Loss: 0.0074388 (0.043699) 2025-03-20,04:45:05 | INFO | Train Epoch: 23 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.212, 152.081/s, 152.081/s/gpu LR: 0.000022 Logit Scale: 29.415 Contrastive_loss: 0.027217 (0.043581) Loss: 0.027217 (0.043581) 2025-03-20,04:45:27 | INFO | Train Epoch: 23 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 148.136/s, 148.136/s/gpu LR: 0.000022 Logit Scale: 29.422 Contrastive_loss: 0.067405 (0.043751) Loss: 0.067405 (0.043751) 2025-03-20,04:45:49 | INFO | Train Epoch: 23 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 147.753/s, 147.753/s/gpu LR: 0.000022 Logit Scale: 29.431 Contrastive_loss: 0.011323 (0.043521) Loss: 0.011323 (0.043521) 2025-03-20,04:46:10 | INFO | Train Epoch: 23 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 147.941/s, 147.941/s/gpu LR: 0.000022 Logit Scale: 29.445 Contrastive_loss: 0.031506 (0.043436) Loss: 0.031506 (0.043436) 2025-03-20,04:46:32 | INFO | Train Epoch: 23 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 149.334/s, 149.334/s/gpu LR: 0.000022 Logit Scale: 29.454 Contrastive_loss: 0.00070702 (0.043137) Loss: 0.00070702 (0.043137) 2025-03-20,04:46:53 | INFO | Train Epoch: 23 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 148.115/s, 148.115/s/gpu LR: 0.000022 Logit Scale: 29.452 Contrastive_loss: 0.0054703 (0.042876) Loss: 0.0054703 (0.042876) 2025-03-20,04:47:15 | INFO | Train Epoch: 23 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 149.334/s, 149.334/s/gpu LR: 0.000022 Logit Scale: 29.457 Contrastive_loss: 0.061619 (0.043005) Loss: 0.061619 (0.043005) 2025-03-20,04:47:36 | INFO | Train Epoch: 23 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 151.279/s, 151.279/s/gpu LR: 0.000022 Logit Scale: 29.452 Contrastive_loss: 0.0048500 (0.042744) Loss: 0.0048500 (0.042744) 2025-03-20,04:47:58 | INFO | Train Epoch: 23 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 148.910/s, 148.910/s/gpu LR: 0.000022 Logit Scale: 29.462 Contrastive_loss: 0.029803 (0.042656) Loss: 0.029803 (0.042656) 2025-03-20,04:48:19 | INFO | Train Epoch: 23 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 146.597/s, 146.597/s/gpu LR: 0.000022 Logit Scale: 29.465 Contrastive_loss: 0.0021029 (0.042382) Loss: 0.0021029 (0.042382) 2025-03-20,04:48:41 | INFO | Train Epoch: 23 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.217, 148.831/s, 148.831/s/gpu LR: 0.000022 Logit Scale: 29.455 Contrastive_loss: 0.091127 (0.042709) Loss: 0.091127 (0.042709) 2025-03-20,04:49:03 | INFO | Train Epoch: 23 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 148.847/s, 148.847/s/gpu LR: 0.000022 Logit Scale: 29.457 Contrastive_loss: 0.11126 (0.043166) Loss: 0.11126 (0.043166) 2025-03-20,04:49:24 | INFO | Train Epoch: 23 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.212, 151.610/s, 151.610/s/gpu LR: 0.000022 Logit Scale: 29.467 Contrastive_loss: 0.078197 (0.043398) Loss: 0.078197 (0.043398) 2025-03-20,04:49:45 | INFO | Train Epoch: 23 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 147.106/s, 147.106/s/gpu LR: 0.000022 Logit Scale: 29.462 Contrastive_loss: 0.019160 (0.043238) Loss: 0.019160 (0.043238) 2025-03-20,04:50:07 | INFO | Train Epoch: 23 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.343/s, 149.343/s/gpu LR: 0.000022 Logit Scale: 29.455 Contrastive_loss: 0.0076943 (0.043006) Loss: 0.0076943 (0.043006) 2025-03-20,04:50:28 | INFO | Train Epoch: 23 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 143.558/s, 143.558/s/gpu LR: 0.000022 Logit Scale: 29.467 Contrastive_loss: 0.0014696 (0.042736) Loss: 0.0014696 (0.042736) 2025-03-20,04:50:50 | INFO | Train Epoch: 23 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 149.289/s, 149.289/s/gpu LR: 0.000022 Logit Scale: 29.460 Contrastive_loss: 0.088029 (0.043029) Loss: 0.088029 (0.043029) 2025-03-20,04:51:11 | INFO | Train Epoch: 23 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 149.188/s, 149.188/s/gpu LR: 0.000022 Logit Scale: 29.461 Contrastive_loss: 0.00051895 (0.042756) Loss: 0.00051895 (0.042756) 2025-03-20,04:51:33 | INFO | Train Epoch: 23 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 148.505/s, 148.505/s/gpu LR: 0.000022 Logit Scale: 29.445 Contrastive_loss: 0.10530 (0.043154) Loss: 0.10530 (0.043154) 2025-03-20,04:51:54 | INFO | Train Epoch: 23 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 148.164/s, 148.164/s/gpu LR: 0.000022 Logit Scale: 29.440 Contrastive_loss: 0.00089931 (0.042887) Loss: 0.00089931 (0.042887) 2025-03-20,04:52:16 | INFO | Train Epoch: 23 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.691/s, 149.691/s/gpu LR: 0.000022 Logit Scale: 29.435 Contrastive_loss: 0.051124 (0.042939) Loss: 0.051124 (0.042939) 2025-03-20,04:52:37 | INFO | Train Epoch: 23 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.161/s, 149.161/s/gpu LR: 0.000022 Logit Scale: 29.427 Contrastive_loss: 0.0077141 (0.042719) Loss: 0.0077141 (0.042719) 2025-03-20,04:52:59 | INFO | Train Epoch: 23 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.221, 131.596/s, 131.596/s/gpu LR: 0.000022 Logit Scale: 29.423 Contrastive_loss: 0.0024031 (0.042468) Loss: 0.0024031 (0.042468) 2025-03-20,04:53:21 | INFO | Train Epoch: 23 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.220, 131.115/s, 131.115/s/gpu LR: 0.000022 Logit Scale: 29.434 Contrastive_loss: 0.0030947 (0.042225) Loss: 0.0030947 (0.042225) 2025-03-20,04:53:43 | INFO | Train Epoch: 23 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.219, 147.425/s, 147.425/s/gpu LR: 0.000022 Logit Scale: 29.437 Contrastive_loss: 0.0021328 (0.041979) Loss: 0.0021328 (0.041979) 2025-03-20,04:54:05 | INFO | Train Epoch: 23 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.221, 144.591/s, 144.591/s/gpu LR: 0.000022 Logit Scale: 29.445 Contrastive_loss: 0.029086 (0.041901) Loss: 0.029086 (0.041901) 2025-03-20,04:54:28 | INFO | Train Epoch: 23 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.223, 144.044/s, 144.044/s/gpu LR: 0.000022 Logit Scale: 29.457 Contrastive_loss: 0.00058028 (0.041650) Loss: 0.00058028 (0.041650) 2025-03-20,04:54:50 | INFO | Train Epoch: 23 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.221, 145.051/s, 145.051/s/gpu LR: 0.000022 Logit Scale: 29.450 Contrastive_loss: 0.0030363 (0.041418) Loss: 0.0030363 (0.041418) 2025-03-20,04:55:11 | INFO | Train Epoch: 23 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 149.065/s, 149.065/s/gpu LR: 0.000022 Logit Scale: 29.454 Contrastive_loss: 0.00021190 (0.041171) Loss: 0.00021190 (0.041171) 2025-03-20,04:55:33 | INFO | Train Epoch: 23 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 149.818/s, 149.818/s/gpu LR: 0.000022 Logit Scale: 29.452 Contrastive_loss: 0.12303 (0.041658) Loss: 0.12303 (0.041658) 2025-03-20,04:55:55 | INFO | Train Epoch: 23 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 142.952/s, 142.952/s/gpu LR: 0.000022 Logit Scale: 29.449 Contrastive_loss: 0.10307 (0.042021) Loss: 0.10307 (0.042021) 2025-03-20,04:56:17 | INFO | Train Epoch: 23 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.221, 147.041/s, 147.041/s/gpu LR: 0.000022 Logit Scale: 29.444 Contrastive_loss: 0.016275 (0.041870) Loss: 0.016275 (0.041870) 2025-03-20,04:56:38 | INFO | Train Epoch: 23 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 150.811/s, 150.811/s/gpu LR: 0.000021 Logit Scale: 29.450 Contrastive_loss: 0.13092 (0.042391) Loss: 0.13092 (0.042391) 2025-03-20,04:57:00 | INFO | Train Epoch: 23 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 149.506/s, 149.506/s/gpu LR: 0.000021 Logit Scale: 29.451 Contrastive_loss: 0.00012328 (0.042145) Loss: 0.00012328 (0.042145) 2025-03-20,04:57:21 | INFO | Train Epoch: 23 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 150.861/s, 150.861/s/gpu LR: 0.000021 Logit Scale: 29.451 Contrastive_loss: 0.047019 (0.042173) Loss: 0.047019 (0.042173) 2025-03-20,04:57:43 | INFO | Train Epoch: 23 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 149.213/s, 149.213/s/gpu LR: 0.000021 Logit Scale: 29.445 Contrastive_loss: 0.12814 (0.042667) Loss: 0.12814 (0.042667) 2025-03-20,04:58:04 | INFO | Train Epoch: 23 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.216, 149.354/s, 149.354/s/gpu LR: 0.000021 Logit Scale: 29.442 Contrastive_loss: 0.0037789 (0.042445) Loss: 0.0037789 (0.042445) 2025-03-20,04:58:26 | INFO | Train Epoch: 23 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.383/s, 149.383/s/gpu LR: 0.000021 Logit Scale: 29.441 Contrastive_loss: 0.012039 (0.042272) Loss: 0.012039 (0.042272) 2025-03-20,04:58:47 | INFO | Train Epoch: 23 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 146.309/s, 146.309/s/gpu LR: 0.000021 Logit Scale: 29.442 Contrastive_loss: 0.14541 (0.042855) Loss: 0.14541 (0.042855) 2025-03-20,04:59:09 | INFO | Train Epoch: 23 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.009/s, 149.009/s/gpu LR: 0.000021 Logit Scale: 29.443 Contrastive_loss: 0.13979 (0.043400) Loss: 0.13979 (0.043400) 2025-03-20,04:59:30 | INFO | Train Epoch: 23 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 146.679/s, 146.679/s/gpu LR: 0.000021 Logit Scale: 29.441 Contrastive_loss: 0.046986 (0.043420) Loss: 0.046986 (0.043420) 2025-03-20,04:59:52 | INFO | Train Epoch: 23 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 149.342/s, 149.342/s/gpu LR: 0.000021 Logit Scale: 29.436 Contrastive_loss: 0.0048459 (0.043205) Loss: 0.0048459 (0.043205) 2025-03-20,05:00:13 | INFO | Train Epoch: 23 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 149.770/s, 149.770/s/gpu LR: 0.000021 Logit Scale: 29.440 Contrastive_loss: 0.22526 (0.044211) Loss: 0.22526 (0.044211) 2025-03-20,05:00:35 | INFO | Train Epoch: 23 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.213, 150.080/s, 150.080/s/gpu LR: 0.000021 Logit Scale: 29.428 Contrastive_loss: 0.067778 (0.044341) Loss: 0.067778 (0.044341) 2025-03-20,05:00:56 | INFO | Train Epoch: 23 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 147.231/s, 147.231/s/gpu LR: 0.000021 Logit Scale: 29.435 Contrastive_loss: 0.14388 (0.044885) Loss: 0.14388 (0.044885) 2025-03-20,05:01:18 | INFO | Train Epoch: 23 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.213, 150.537/s, 150.537/s/gpu LR: 0.000021 Logit Scale: 29.435 Contrastive_loss: 0.063609 (0.044986) Loss: 0.063609 (0.044986) 2025-03-20,05:01:39 | INFO | Train Epoch: 23 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.212, 150.971/s, 150.971/s/gpu LR: 0.000021 Logit Scale: 29.442 Contrastive_loss: 0.069320 (0.045118) Loss: 0.069320 (0.045118) 2025-03-20,05:02:00 | INFO | Train Epoch: 23 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.212, 151.837/s, 151.837/s/gpu LR: 0.000021 Logit Scale: 29.440 Contrastive_loss: 0.060686 (0.045202) Loss: 0.060686 (0.045202) 2025-03-20,05:02:22 | INFO | Train Epoch: 23 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.217, 147.056/s, 147.056/s/gpu LR: 0.000021 Logit Scale: 29.447 Contrastive_loss: 0.039262 (0.045170) Loss: 0.039262 (0.045170) 2025-03-20,05:02:43 | INFO | Train Epoch: 23 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 151.570/s, 151.570/s/gpu LR: 0.000021 Logit Scale: 29.455 Contrastive_loss: 0.00094631 (0.044935) Loss: 0.00094631 (0.044935) 2025-03-20,05:03:04 | INFO | Train Epoch: 23 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 145.975/s, 145.975/s/gpu LR: 0.000021 Logit Scale: 29.454 Contrastive_loss: 0.00096905 (0.044702) Loss: 0.00096905 (0.044702) 2025-03-20,05:03:26 | INFO | Train Epoch: 23 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.219, 145.972/s, 145.972/s/gpu LR: 0.000021 Logit Scale: 29.452 Contrastive_loss: 0.031409 (0.044632) Loss: 0.031409 (0.044632) 2025-03-20,05:03:48 | INFO | Train Epoch: 23 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.850/s, 148.850/s/gpu LR: 0.000021 Logit Scale: 29.450 Contrastive_loss: 0.00027016 (0.044400) Loss: 0.00027016 (0.044400) 2025-03-20,05:04:10 | INFO | Train Epoch: 23 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 148.993/s, 148.993/s/gpu LR: 0.000021 Logit Scale: 29.444 Contrastive_loss: 0.025650 (0.044302) Loss: 0.025650 (0.044302) 2025-03-20,05:04:31 | INFO | Train Epoch: 23 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.212, 151.202/s, 151.202/s/gpu LR: 0.000021 Logit Scale: 29.449 Contrastive_loss: 0.085501 (0.044516) Loss: 0.085501 (0.044516) 2025-03-20,05:04:52 | INFO | Train Epoch: 23 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.212, 150.323/s, 150.323/s/gpu LR: 0.000021 Logit Scale: 29.454 Contrastive_loss: 0.043893 (0.044512) Loss: 0.043893 (0.044512) 2025-03-20,05:05:13 | INFO | Train Epoch: 23 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.770/s, 148.770/s/gpu LR: 0.000021 Logit Scale: 29.455 Contrastive_loss: 0.054141 (0.044562) Loss: 0.054141 (0.044562) 2025-03-20,05:05:35 | INFO | Train Epoch: 23 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 148.604/s, 148.604/s/gpu LR: 0.000021 Logit Scale: 29.460 Contrastive_loss: 0.0022521 (0.044346) Loss: 0.0022521 (0.044346) 2025-03-20,05:05:57 | INFO | Train Epoch: 23 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 147.886/s, 147.886/s/gpu LR: 0.000021 Logit Scale: 29.465 Contrastive_loss: 0.00082467 (0.044125) Loss: 0.00082467 (0.044125) 2025-03-20,05:06:18 | INFO | Train Epoch: 23 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.217, 141.717/s, 141.717/s/gpu LR: 0.000021 Logit Scale: 29.469 Contrastive_loss: 0.047090 (0.044140) Loss: 0.047090 (0.044140) 2025-03-20,05:06:41 | INFO | Train Epoch: 23 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.223, 148.254/s, 148.254/s/gpu LR: 0.000021 Logit Scale: 29.474 Contrastive_loss: 0.00054488 (0.043921) Loss: 0.00054488 (0.043921) 2025-03-20,05:07:03 | INFO | Train Epoch: 23 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.223, 144.162/s, 144.162/s/gpu LR: 0.000021 Logit Scale: 29.483 Contrastive_loss: 0.044733 (0.043925) Loss: 0.044733 (0.043925) 2025-03-20,05:07:25 | INFO | Train Epoch: 23 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.223, 146.376/s, 146.376/s/gpu LR: 0.000021 Logit Scale: 29.480 Contrastive_loss: 0.067524 (0.044042) Loss: 0.067524 (0.044042) 2025-03-20,05:07:47 | INFO | Train Epoch: 23 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 145.340/s, 145.340/s/gpu LR: 0.000021 Logit Scale: 29.487 Contrastive_loss: 0.036764 (0.044006) Loss: 0.036764 (0.044006) 2025-03-20,05:08:08 | INFO | Train Epoch: 23 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 151.917/s, 151.917/s/gpu LR: 0.000021 Logit Scale: 29.488 Contrastive_loss: 0.054471 (0.044058) Loss: 0.054471 (0.044058) 2025-03-20,05:08:30 | INFO | Train Epoch: 23 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 148.883/s, 148.883/s/gpu LR: 0.000021 Logit Scale: 29.486 Contrastive_loss: 0.30020 (0.045313) Loss: 0.30020 (0.045313) 2025-03-20,05:08:51 | INFO | Train Epoch: 23 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 147.755/s, 147.755/s/gpu LR: 0.000021 Logit Scale: 29.478 Contrastive_loss: 0.022279 (0.045201) Loss: 0.022279 (0.045201) 2025-03-20,05:09:13 | INFO | Train Epoch: 23 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.283/s, 148.283/s/gpu LR: 0.000021 Logit Scale: 29.474 Contrastive_loss: 0.0022464 (0.044993) Loss: 0.0022464 (0.044993) 2025-03-20,05:09:34 | INFO | Train Epoch: 23 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.852/s, 149.852/s/gpu LR: 0.000021 Logit Scale: 29.473 Contrastive_loss: 0.10508 (0.045283) Loss: 0.10508 (0.045283) 2025-03-20,05:09:56 | INFO | Train Epoch: 23 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 148.976/s, 148.976/s/gpu LR: 0.000020 Logit Scale: 29.478 Contrastive_loss: 0.021333 (0.045168) Loss: 0.021333 (0.045168) 2025-03-20,05:10:17 | INFO | Train Epoch: 23 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.218, 84.4874/s, 84.4874/s/gpu LR: 0.000020 Logit Scale: 29.478 Contrastive_loss: 0.11589 (0.045506) Loss: 0.11589 (0.045506) 2025-03-20,05:10:39 | INFO | Train Epoch: 23 [668832/766009 (87%)] Data (t): 0.000 Batch (t): 0.213, 150.129/s, 150.129/s/gpu LR: 0.000020 Logit Scale: 29.481 Contrastive_loss: 0.021264 (0.045391) Loss: 0.021264 (0.045391) 2025-03-20,05:11:00 | INFO | Train Epoch: 23 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 149.674/s, 149.674/s/gpu LR: 0.000020 Logit Scale: 29.480 Contrastive_loss: 0.043753 (0.045383) Loss: 0.043753 (0.045383) 2025-03-20,05:11:22 | INFO | Train Epoch: 23 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.214, 150.072/s, 150.072/s/gpu LR: 0.000020 Logit Scale: 29.477 Contrastive_loss: 0.044039 (0.045377) Loss: 0.044039 (0.045377) 2025-03-20,05:11:43 | INFO | Train Epoch: 23 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 151.795/s, 151.795/s/gpu LR: 0.000020 Logit Scale: 29.470 Contrastive_loss: 0.0054538 (0.045189) Loss: 0.0054538 (0.045189) 2025-03-20,05:12:04 | INFO | Train Epoch: 23 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.213, 149.068/s, 149.068/s/gpu LR: 0.000020 Logit Scale: 29.456 Contrastive_loss: 0.034050 (0.045137) Loss: 0.034050 (0.045137) 2025-03-20,05:12:26 | INFO | Train Epoch: 23 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 151.692/s, 151.692/s/gpu LR: 0.000020 Logit Scale: 29.450 Contrastive_loss: 0.10670 (0.045423) Loss: 0.10670 (0.045423) 2025-03-20,05:12:47 | INFO | Train Epoch: 23 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 146.696/s, 146.696/s/gpu LR: 0.000020 Logit Scale: 29.449 Contrastive_loss: 0.011824 (0.045268) Loss: 0.011824 (0.045268) 2025-03-20,05:13:08 | INFO | Train Epoch: 23 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.213, 153.883/s, 153.883/s/gpu LR: 0.000020 Logit Scale: 29.446 Contrastive_loss: 0.011187 (0.045111) Loss: 0.011187 (0.045111) 2025-03-20,05:13:30 | INFO | Train Epoch: 23 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.815/s, 149.815/s/gpu LR: 0.000020 Logit Scale: 29.448 Contrastive_loss: 0.12941 (0.045497) Loss: 0.12941 (0.045497) 2025-03-20,05:13:52 | INFO | Train Epoch: 23 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 146.119/s, 146.119/s/gpu LR: 0.000020 Logit Scale: 29.453 Contrastive_loss: 0.18810 (0.046149) Loss: 0.18810 (0.046149) 2025-03-20,05:14:14 | INFO | Train Epoch: 23 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.219, 145.015/s, 145.015/s/gpu LR: 0.000020 Logit Scale: 29.458 Contrastive_loss: 0.12895 (0.046525) Loss: 0.12895 (0.046525) 2025-03-20,05:14:35 | INFO | Train Epoch: 23 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 145.347/s, 145.347/s/gpu LR: 0.000020 Logit Scale: 29.463 Contrastive_loss: 0.0094584 (0.046357) Loss: 0.0094584 (0.046357) 2025-03-20,05:14:57 | INFO | Train Epoch: 23 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.219, 146.607/s, 146.607/s/gpu LR: 0.000020 Logit Scale: 29.464 Contrastive_loss: 0.13160 (0.046741) Loss: 0.13160 (0.046741) 2025-03-20,05:15:19 | INFO | Train Epoch: 23 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 148.272/s, 148.272/s/gpu LR: 0.000020 Logit Scale: 29.468 Contrastive_loss: 0.044094 (0.046729) Loss: 0.044094 (0.046729) 2025-03-20,05:15:41 | INFO | Train Epoch: 23 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.218, 146.931/s, 146.931/s/gpu LR: 0.000020 Logit Scale: 29.472 Contrastive_loss: 0.0016501 (0.046528) Loss: 0.0016501 (0.046528) 2025-03-20,05:16:02 | INFO | Train Epoch: 23 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.218, 146.561/s, 146.561/s/gpu LR: 0.000020 Logit Scale: 29.483 Contrastive_loss: 0.00018924 (0.046322) Loss: 0.00018924 (0.046322) 2025-03-20,05:16:24 | INFO | Train Epoch: 23 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 145.275/s, 145.275/s/gpu LR: 0.000020 Logit Scale: 29.488 Contrastive_loss: 0.0019102 (0.046126) Loss: 0.0019102 (0.046126) 2025-03-20,05:16:45 | INFO | Train Epoch: 23 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 149.877/s, 149.877/s/gpu LR: 0.000020 Logit Scale: 29.485 Contrastive_loss: 0.12369 (0.046467) Loss: 0.12369 (0.046467) 2025-03-20,05:17:07 | INFO | Train Epoch: 23 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 149.608/s, 149.608/s/gpu LR: 0.000020 Logit Scale: 29.497 Contrastive_loss: 0.064695 (0.046547) Loss: 0.064695 (0.046547) 2025-03-20,05:17:28 | INFO | Train Epoch: 23 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 149.628/s, 149.628/s/gpu LR: 0.000020 Logit Scale: 29.513 Contrastive_loss: 0.0022450 (0.046354) Loss: 0.0022450 (0.046354) 2025-03-20,05:17:50 | INFO | Train Epoch: 23 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 146.450/s, 146.450/s/gpu LR: 0.000020 Logit Scale: 29.514 Contrastive_loss: 0.057552 (0.046403) Loss: 0.057552 (0.046403) 2025-03-20,05:18:12 | INFO | Train Epoch: 23 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.217, 149.254/s, 149.254/s/gpu LR: 0.000020 Logit Scale: 29.515 Contrastive_loss: 0.024431 (0.046307) Loss: 0.024431 (0.046307) 2025-03-20,05:18:33 | INFO | Train Epoch: 23 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.807/s, 149.807/s/gpu LR: 0.000020 Logit Scale: 29.510 Contrastive_loss: 0.050765 (0.046327) Loss: 0.050765 (0.046327) 2025-03-20,05:18:54 | INFO | Train Epoch: 23 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 149.846/s, 149.846/s/gpu LR: 0.000020 Logit Scale: 29.507 Contrastive_loss: 0.22545 (0.047095) Loss: 0.22545 (0.047095) 2025-03-20,05:19:16 | INFO | Train Epoch: 23 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 149.940/s, 149.940/s/gpu LR: 0.000020 Logit Scale: 29.507 Contrastive_loss: 0.068273 (0.047186) Loss: 0.068273 (0.047186) 2025-03-20,05:19:38 | INFO | Train Epoch: 23 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 150.641/s, 150.641/s/gpu LR: 0.000020 Logit Scale: 29.512 Contrastive_loss: 0.0091549 (0.047024) Loss: 0.0091549 (0.047024) 2025-03-20,05:19:59 | INFO | Train Epoch: 23 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 148.575/s, 148.575/s/gpu LR: 0.000020 Logit Scale: 29.516 Contrastive_loss: 0.00042282 (0.046827) Loss: 0.00042282 (0.046827) 2025-03-20,05:20:21 | INFO | Train Epoch: 23 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 148.139/s, 148.139/s/gpu LR: 0.000020 Logit Scale: 29.516 Contrastive_loss: 0.067874 (0.046915) Loss: 0.067874 (0.046915) 2025-03-20,05:20:42 | INFO | Train Epoch: 23 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 150.474/s, 150.474/s/gpu LR: 0.000020 Logit Scale: 29.511 Contrastive_loss: 0.087525 (0.047086) Loss: 0.087525 (0.047086) 2025-03-20,05:21:04 | INFO | Train Epoch: 23 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 151.319/s, 151.319/s/gpu LR: 0.000020 Logit Scale: 29.508 Contrastive_loss: 0.025991 (0.046998) Loss: 0.025991 (0.046998) 2025-03-20,05:21:25 | INFO | Train Epoch: 23 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.214, 145.849/s, 145.849/s/gpu LR: 0.000020 Logit Scale: 29.510 Contrastive_loss: 0.053694 (0.047026) Loss: 0.053694 (0.047026) 2025-03-20,05:21:33 | INFO | Train Epoch: 23 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.220, 146.729/s, 146.729/s/gpu LR: 0.000020 Logit Scale: 29.507 Contrastive_loss: 0.071643 (0.047128) Loss: 0.071643 (0.047128) 2025-03-20,05:21:33 | INFO | Eval Epoch: 24 [32 / 7443] Clip Loss: 4.164058 2025-03-20,05:21:39 | INFO | Eval Epoch: 24 [3232 / 7443] Clip Loss: 0.882250 2025-03-20,05:21:45 | INFO | Eval Epoch: 24 [6432 / 7443] Clip Loss: 0.654390 2025-03-20,05:21:47 | INFO | Eval Epoch: 24 image_to_text_mean_rank: 81.9793 image_to_text_median_rank: 5.0000 image_to_text_R@1: 0.1939 image_to_text_R@5: 0.5449 image_to_text_R@10: 0.7093 text_to_image_mean_rank: 49.7162 text_to_image_median_rank: 5.0000 text_to_image_R@1: 0.1927 text_to_image_R@5: 0.5295 text_to_image_R@10: 0.6961 clip_val_loss: 0.6055 epoch: 24.0000 num_samples: 7443.0000 2025-03-20,05:22:20 | INFO | Start epoch 24 2025-03-20,05:22:21 | INFO | Train Epoch: 24 [ 32/766009 (0%)] Data (t): 0.181 Batch (t): 0.382, 83.6631/s, 83.6631/s/gpu LR: 0.000020 Logit Scale: 29.508 Contrastive_loss: 0.14074 (0.14074) Loss: 0.14074 (0.14074) 2025-03-20,05:22:43 | INFO | Train Epoch: 24 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.219, 149.678/s, 149.678/s/gpu LR: 0.000020 Logit Scale: 29.514 Contrastive_loss: 0.063660 (0.10220) Loss: 0.063660 (0.10220) 2025-03-20,05:23:04 | INFO | Train Epoch: 24 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 147.309/s, 147.309/s/gpu LR: 0.000020 Logit Scale: 29.517 Contrastive_loss: 0.014836 (0.073080) Loss: 0.014836 (0.073080) 2025-03-20,05:23:26 | INFO | Train Epoch: 24 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.220, 148.038/s, 148.038/s/gpu LR: 0.000020 Logit Scale: 29.527 Contrastive_loss: 0.042704 (0.065486) Loss: 0.042704 (0.065486) 2025-03-20,05:23:48 | INFO | Train Epoch: 24 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.219, 145.160/s, 145.160/s/gpu LR: 0.000020 Logit Scale: 29.519 Contrastive_loss: 0.036989 (0.059787) Loss: 0.036989 (0.059787) 2025-03-20,05:24:10 | INFO | Train Epoch: 24 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.217, 149.725/s, 149.725/s/gpu LR: 0.000019 Logit Scale: 29.524 Contrastive_loss: 0.079526 (0.063077) Loss: 0.079526 (0.063077) 2025-03-20,05:24:31 | INFO | Train Epoch: 24 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 149.948/s, 149.948/s/gpu LR: 0.000019 Logit Scale: 29.530 Contrastive_loss: 0.091284 (0.067106) Loss: 0.091284 (0.067106) 2025-03-20,05:24:53 | INFO | Train Epoch: 24 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 145.059/s, 145.059/s/gpu LR: 0.000019 Logit Scale: 29.540 Contrastive_loss: 0.0014749 (0.058902) Loss: 0.0014749 (0.058902) 2025-03-20,05:25:14 | INFO | Train Epoch: 24 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.212, 151.481/s, 151.481/s/gpu LR: 0.000019 Logit Scale: 29.552 Contrastive_loss: 0.0017612 (0.052553) Loss: 0.0017612 (0.052553) 2025-03-20,05:25:35 | INFO | Train Epoch: 24 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.212, 151.816/s, 151.816/s/gpu LR: 0.000019 Logit Scale: 29.551 Contrastive_loss: 0.020169 (0.049315) Loss: 0.020169 (0.049315) 2025-03-20,05:25:57 | INFO | Train Epoch: 24 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 149.203/s, 149.203/s/gpu LR: 0.000019 Logit Scale: 29.545 Contrastive_loss: 0.097014 (0.053651) Loss: 0.097014 (0.053651) 2025-03-20,05:26:18 | INFO | Train Epoch: 24 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.543/s, 149.543/s/gpu LR: 0.000019 Logit Scale: 29.539 Contrastive_loss: 0.059280 (0.054120) Loss: 0.059280 (0.054120) 2025-03-20,05:26:40 | INFO | Train Epoch: 24 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 147.950/s, 147.950/s/gpu LR: 0.000019 Logit Scale: 29.544 Contrastive_loss: 0.056032 (0.054267) Loss: 0.056032 (0.054267) 2025-03-20,05:27:01 | INFO | Train Epoch: 24 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.592/s, 149.592/s/gpu LR: 0.000019 Logit Scale: 29.547 Contrastive_loss: 0.00071197 (0.050442) Loss: 0.00071197 (0.050442) 2025-03-20,05:27:23 | INFO | Train Epoch: 24 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.215, 145.847/s, 145.847/s/gpu LR: 0.000019 Logit Scale: 29.548 Contrastive_loss: 0.052355 (0.050569) Loss: 0.052355 (0.050569) 2025-03-20,05:27:45 | INFO | Train Epoch: 24 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.220, 146.086/s, 146.086/s/gpu LR: 0.000019 Logit Scale: 29.552 Contrastive_loss: 0.024955 (0.048968) Loss: 0.024955 (0.048968) 2025-03-20,05:28:07 | INFO | Train Epoch: 24 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.220, 147.028/s, 147.028/s/gpu LR: 0.000019 Logit Scale: 29.557 Contrastive_loss: 0.0041965 (0.046335) Loss: 0.0041965 (0.046335) 2025-03-20,05:28:28 | INFO | Train Epoch: 24 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 150.958/s, 150.958/s/gpu LR: 0.000019 Logit Scale: 29.561 Contrastive_loss: 0.10221 (0.049439) Loss: 0.10221 (0.049439) 2025-03-20,05:28:50 | INFO | Train Epoch: 24 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.213/s, 149.213/s/gpu LR: 0.000019 Logit Scale: 29.545 Contrastive_loss: 0.00036841 (0.046856) Loss: 0.00036841 (0.046856) 2025-03-20,05:29:12 | INFO | Train Epoch: 24 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 147.575/s, 147.575/s/gpu LR: 0.000019 Logit Scale: 29.553 Contrastive_loss: 0.083336 (0.048680) Loss: 0.083336 (0.048680) 2025-03-20,05:29:34 | INFO | Train Epoch: 24 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.222, 147.111/s, 147.111/s/gpu LR: 0.000019 Logit Scale: 29.561 Contrastive_loss: 0.064234 (0.049421) Loss: 0.064234 (0.049421) 2025-03-20,05:29:55 | INFO | Train Epoch: 24 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.217, 147.521/s, 147.521/s/gpu LR: 0.000019 Logit Scale: 29.566 Contrastive_loss: 0.021297 (0.048142) Loss: 0.021297 (0.048142) 2025-03-20,05:30:17 | INFO | Train Epoch: 24 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 148.237/s, 148.237/s/gpu LR: 0.000019 Logit Scale: 29.570 Contrastive_loss: 0.048065 (0.048139) Loss: 0.048065 (0.048139) 2025-03-20,05:30:38 | INFO | Train Epoch: 24 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 150.156/s, 150.156/s/gpu LR: 0.000019 Logit Scale: 29.571 Contrastive_loss: 0.031097 (0.047429) Loss: 0.031097 (0.047429) 2025-03-20,05:31:00 | INFO | Train Epoch: 24 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 150.089/s, 150.089/s/gpu LR: 0.000019 Logit Scale: 29.573 Contrastive_loss: 0.00039641 (0.045548) Loss: 0.00039641 (0.045548) 2025-03-20,05:31:21 | INFO | Train Epoch: 24 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.213, 149.579/s, 149.579/s/gpu LR: 0.000019 Logit Scale: 29.566 Contrastive_loss: 0.0013373 (0.043847) Loss: 0.0013373 (0.043847) 2025-03-20,05:31:43 | INFO | Train Epoch: 24 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.212, 150.927/s, 150.927/s/gpu LR: 0.000019 Logit Scale: 29.565 Contrastive_loss: 0.071441 (0.044869) Loss: 0.071441 (0.044869) 2025-03-20,05:32:04 | INFO | Train Epoch: 24 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 148.016/s, 148.016/s/gpu LR: 0.000019 Logit Scale: 29.559 Contrastive_loss: 0.074497 (0.045927) Loss: 0.074497 (0.045927) 2025-03-20,05:32:25 | INFO | Train Epoch: 24 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.213, 150.957/s, 150.957/s/gpu LR: 0.000019 Logit Scale: 29.555 Contrastive_loss: 0.043260 (0.045835) Loss: 0.043260 (0.045835) 2025-03-20,05:32:47 | INFO | Train Epoch: 24 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 149.019/s, 149.019/s/gpu LR: 0.000019 Logit Scale: 29.552 Contrastive_loss: 0.060445 (0.046322) Loss: 0.060445 (0.046322) 2025-03-20,05:33:08 | INFO | Train Epoch: 24 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.119/s, 149.119/s/gpu LR: 0.000019 Logit Scale: 29.554 Contrastive_loss: 0.0022157 (0.044900) Loss: 0.0022157 (0.044900) 2025-03-20,05:33:30 | INFO | Train Epoch: 24 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.789/s, 148.789/s/gpu LR: 0.000019 Logit Scale: 29.560 Contrastive_loss: 0.0040772 (0.043624) Loss: 0.0040772 (0.043624) 2025-03-20,05:33:52 | INFO | Train Epoch: 24 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 147.050/s, 147.050/s/gpu LR: 0.000019 Logit Scale: 29.568 Contrastive_loss: 0.00045854 (0.042316) Loss: 0.00045854 (0.042316) 2025-03-20,05:34:13 | INFO | Train Epoch: 24 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.217, 146.430/s, 146.430/s/gpu LR: 0.000019 Logit Scale: 29.572 Contrastive_loss: 0.029890 (0.041950) Loss: 0.029890 (0.041950) 2025-03-20,05:34:35 | INFO | Train Epoch: 24 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 150.354/s, 150.354/s/gpu LR: 0.000019 Logit Scale: 29.574 Contrastive_loss: 0.079874 (0.043034) Loss: 0.079874 (0.043034) 2025-03-20,05:34:56 | INFO | Train Epoch: 24 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.492/s, 149.492/s/gpu LR: 0.000019 Logit Scale: 29.582 Contrastive_loss: 0.0058245 (0.042000) Loss: 0.0058245 (0.042000) 2025-03-20,05:35:18 | INFO | Train Epoch: 24 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.163/s, 148.163/s/gpu LR: 0.000019 Logit Scale: 29.583 Contrastive_loss: 0.081996 (0.043081) Loss: 0.081996 (0.043081) 2025-03-20,05:35:39 | INFO | Train Epoch: 24 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.551/s, 149.551/s/gpu LR: 0.000019 Logit Scale: 29.593 Contrastive_loss: 0.12893 (0.045340) Loss: 0.12893 (0.045340) 2025-03-20,05:36:01 | INFO | Train Epoch: 24 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 147.982/s, 147.982/s/gpu LR: 0.000019 Logit Scale: 29.588 Contrastive_loss: 0.10411 (0.046847) Loss: 0.10411 (0.046847) 2025-03-20,05:36:22 | INFO | Train Epoch: 24 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 148.632/s, 148.632/s/gpu LR: 0.000019 Logit Scale: 29.593 Contrastive_loss: 0.10115 (0.048205) Loss: 0.10115 (0.048205) 2025-03-20,05:36:44 | INFO | Train Epoch: 24 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 146.134/s, 146.134/s/gpu LR: 0.000019 Logit Scale: 29.596 Contrastive_loss: 0.011560 (0.047311) Loss: 0.011560 (0.047311) 2025-03-20,05:37:06 | INFO | Train Epoch: 24 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.219, 147.591/s, 147.591/s/gpu LR: 0.000019 Logit Scale: 29.600 Contrastive_loss: 0.040601 (0.047151) Loss: 0.040601 (0.047151) 2025-03-20,05:37:27 | INFO | Train Epoch: 24 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 152.225/s, 152.225/s/gpu LR: 0.000019 Logit Scale: 29.609 Contrastive_loss: 0.057583 (0.047394) Loss: 0.057583 (0.047394) 2025-03-20,05:37:49 | INFO | Train Epoch: 24 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.115/s, 147.115/s/gpu LR: 0.000019 Logit Scale: 29.612 Contrastive_loss: 0.044254 (0.047322) Loss: 0.044254 (0.047322) 2025-03-20,05:38:11 | INFO | Train Epoch: 24 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.930/s, 147.930/s/gpu LR: 0.000018 Logit Scale: 29.604 Contrastive_loss: 0.11460 (0.048817) Loss: 0.11460 (0.048817) 2025-03-20,05:38:33 | INFO | Train Epoch: 24 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.217, 147.616/s, 147.616/s/gpu LR: 0.000018 Logit Scale: 29.613 Contrastive_loss: 0.0023729 (0.047808) Loss: 0.0023729 (0.047808) 2025-03-20,05:38:54 | INFO | Train Epoch: 24 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 140.797/s, 140.797/s/gpu LR: 0.000018 Logit Scale: 29.619 Contrastive_loss: 0.0028185 (0.046850) Loss: 0.0028185 (0.046850) 2025-03-20,05:39:16 | INFO | Train Epoch: 24 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.218, 145.563/s, 145.563/s/gpu LR: 0.000018 Logit Scale: 29.611 Contrastive_loss: 0.017044 (0.046230) Loss: 0.017044 (0.046230) 2025-03-20,05:39:38 | INFO | Train Epoch: 24 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 146.695/s, 146.695/s/gpu LR: 0.000018 Logit Scale: 29.621 Contrastive_loss: 0.043926 (0.046183) Loss: 0.043926 (0.046183) 2025-03-20,05:40:00 | INFO | Train Epoch: 24 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.220, 147.014/s, 147.014/s/gpu LR: 0.000018 Logit Scale: 29.630 Contrastive_loss: 0.055649 (0.046372) Loss: 0.055649 (0.046372) 2025-03-20,05:40:21 | INFO | Train Epoch: 24 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 145.025/s, 145.025/s/gpu LR: 0.000018 Logit Scale: 29.639 Contrastive_loss: 0.0020382 (0.045503) Loss: 0.0020382 (0.045503) 2025-03-20,05:40:43 | INFO | Train Epoch: 24 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.221, 145.754/s, 145.754/s/gpu LR: 0.000018 Logit Scale: 29.637 Contrastive_loss: 0.00057964 (0.044639) Loss: 0.00057964 (0.044639) 2025-03-20,05:41:06 | INFO | Train Epoch: 24 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.220, 143.129/s, 143.129/s/gpu LR: 0.000018 Logit Scale: 29.645 Contrastive_loss: 0.046379 (0.044672) Loss: 0.046379 (0.044672) 2025-03-20,05:41:27 | INFO | Train Epoch: 24 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 147.284/s, 147.284/s/gpu LR: 0.000018 Logit Scale: 29.656 Contrastive_loss: 0.078396 (0.045296) Loss: 0.078396 (0.045296) 2025-03-20,05:41:49 | INFO | Train Epoch: 24 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.220, 147.172/s, 147.172/s/gpu LR: 0.000018 Logit Scale: 29.664 Contrastive_loss: 0.065031 (0.045655) Loss: 0.065031 (0.045655) 2025-03-20,05:42:11 | INFO | Train Epoch: 24 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 149.153/s, 149.153/s/gpu LR: 0.000018 Logit Scale: 29.674 Contrastive_loss: 0.0027533 (0.044889) Loss: 0.0027533 (0.044889) 2025-03-20,05:42:32 | INFO | Train Epoch: 24 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 147.590/s, 147.590/s/gpu LR: 0.000018 Logit Scale: 29.675 Contrastive_loss: 0.023608 (0.044515) Loss: 0.023608 (0.044515) 2025-03-20,05:42:54 | INFO | Train Epoch: 24 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.215, 150.026/s, 150.026/s/gpu LR: 0.000018 Logit Scale: 29.674 Contrastive_loss: 0.0030210 (0.043800) Loss: 0.0030210 (0.043800) 2025-03-20,05:43:16 | INFO | Train Epoch: 24 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 145.504/s, 145.504/s/gpu LR: 0.000018 Logit Scale: 29.676 Contrastive_loss: 0.043637 (0.043797) Loss: 0.043637 (0.043797) 2025-03-20,05:43:38 | INFO | Train Epoch: 24 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.220, 146.929/s, 146.929/s/gpu LR: 0.000018 Logit Scale: 29.681 Contrastive_loss: 0.00044379 (0.043075) Loss: 0.00044379 (0.043075) 2025-03-20,05:43:59 | INFO | Train Epoch: 24 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.218, 147.007/s, 147.007/s/gpu LR: 0.000018 Logit Scale: 29.686 Contrastive_loss: 0.010217 (0.042536) Loss: 0.010217 (0.042536) 2025-03-20,05:44:21 | INFO | Train Epoch: 24 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 147.678/s, 147.678/s/gpu LR: 0.000018 Logit Scale: 29.688 Contrastive_loss: 0.0069959 (0.041963) Loss: 0.0069959 (0.041963) 2025-03-20,05:44:43 | INFO | Train Epoch: 24 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.217, 146.402/s, 146.402/s/gpu LR: 0.000018 Logit Scale: 29.694 Contrastive_loss: 0.18015 (0.044156) Loss: 0.18015 (0.044156) 2025-03-20,05:45:04 | INFO | Train Epoch: 24 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.216, 149.133/s, 149.133/s/gpu LR: 0.000018 Logit Scale: 29.695 Contrastive_loss: 0.066370 (0.044503) Loss: 0.066370 (0.044503) 2025-03-20,05:45:26 | INFO | Train Epoch: 24 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.218, 146.605/s, 146.605/s/gpu LR: 0.000018 Logit Scale: 29.697 Contrastive_loss: 0.020583 (0.044135) Loss: 0.020583 (0.044135) 2025-03-20,05:45:48 | INFO | Train Epoch: 24 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.217, 148.678/s, 148.678/s/gpu LR: 0.000018 Logit Scale: 29.703 Contrastive_loss: 0.16616 (0.045984) Loss: 0.16616 (0.045984) 2025-03-20,05:46:09 | INFO | Train Epoch: 24 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 148.691/s, 148.691/s/gpu LR: 0.000018 Logit Scale: 29.704 Contrastive_loss: 0.010462 (0.045454) Loss: 0.010462 (0.045454) 2025-03-20,05:46:31 | INFO | Train Epoch: 24 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.217, 147.853/s, 147.853/s/gpu LR: 0.000018 Logit Scale: 29.700 Contrastive_loss: 0.057616 (0.045633) Loss: 0.057616 (0.045633) 2025-03-20,05:46:53 | INFO | Train Epoch: 24 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.217, 149.458/s, 149.458/s/gpu LR: 0.000018 Logit Scale: 29.695 Contrastive_loss: 0.12266 (0.046749) Loss: 0.12266 (0.046749) 2025-03-20,05:47:14 | INFO | Train Epoch: 24 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 147.065/s, 147.065/s/gpu LR: 0.000018 Logit Scale: 29.694 Contrastive_loss: 0.053616 (0.046847) Loss: 0.053616 (0.046847) 2025-03-20,05:47:36 | INFO | Train Epoch: 24 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.063/s, 148.063/s/gpu LR: 0.000018 Logit Scale: 29.697 Contrastive_loss: 0.066347 (0.047122) Loss: 0.066347 (0.047122) 2025-03-20,05:47:57 | INFO | Train Epoch: 24 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 147.707/s, 147.707/s/gpu LR: 0.000018 Logit Scale: 29.696 Contrastive_loss: 0.039567 (0.047017) Loss: 0.039567 (0.047017) 2025-03-20,05:48:19 | INFO | Train Epoch: 24 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 147.425/s, 147.425/s/gpu LR: 0.000018 Logit Scale: 29.699 Contrastive_loss: 0.084983 (0.047537) Loss: 0.084983 (0.047537) 2025-03-20,05:48:41 | INFO | Train Epoch: 24 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 148.528/s, 148.528/s/gpu LR: 0.000018 Logit Scale: 29.712 Contrastive_loss: 0.070773 (0.047851) Loss: 0.070773 (0.047851) 2025-03-20,05:49:02 | INFO | Train Epoch: 24 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.127/s, 149.127/s/gpu LR: 0.000018 Logit Scale: 29.713 Contrastive_loss: 0.041961 (0.047773) Loss: 0.041961 (0.047773) 2025-03-20,05:49:24 | INFO | Train Epoch: 24 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 150.556/s, 150.556/s/gpu LR: 0.000018 Logit Scale: 29.720 Contrastive_loss: 0.10840 (0.048570) Loss: 0.10840 (0.048570) 2025-03-20,05:49:45 | INFO | Train Epoch: 24 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 148.970/s, 148.970/s/gpu LR: 0.000018 Logit Scale: 29.716 Contrastive_loss: 0.011118 (0.048084) Loss: 0.011118 (0.048084) 2025-03-20,05:50:07 | INFO | Train Epoch: 24 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 147.702/s, 147.702/s/gpu LR: 0.000018 Logit Scale: 29.718 Contrastive_loss: 0.10064 (0.048758) Loss: 0.10064 (0.048758) 2025-03-20,05:50:28 | INFO | Train Epoch: 24 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 150.569/s, 150.569/s/gpu LR: 0.000018 Logit Scale: 29.720 Contrastive_loss: 0.11192 (0.049557) Loss: 0.11192 (0.049557) 2025-03-20,05:50:50 | INFO | Train Epoch: 24 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.246/s, 150.246/s/gpu LR: 0.000018 Logit Scale: 29.724 Contrastive_loss: 0.063283 (0.049729) Loss: 0.063283 (0.049729) 2025-03-20,05:51:11 | INFO | Train Epoch: 24 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 150.371/s, 150.371/s/gpu LR: 0.000018 Logit Scale: 29.732 Contrastive_loss: 0.060635 (0.049863) Loss: 0.060635 (0.049863) 2025-03-20,05:51:33 | INFO | Train Epoch: 24 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 150.165/s, 150.165/s/gpu LR: 0.000018 Logit Scale: 29.735 Contrastive_loss: 0.055597 (0.049933) Loss: 0.055597 (0.049933) 2025-03-20,05:51:54 | INFO | Train Epoch: 24 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 150.025/s, 150.025/s/gpu LR: 0.000018 Logit Scale: 29.735 Contrastive_loss: 0.00038209 (0.049336) Loss: 0.00038209 (0.049336) 2025-03-20,05:52:16 | INFO | Train Epoch: 24 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 148.616/s, 148.616/s/gpu LR: 0.000017 Logit Scale: 29.736 Contrastive_loss: 0.0084814 (0.048850) Loss: 0.0084814 (0.048850) 2025-03-20,05:52:38 | INFO | Train Epoch: 24 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 148.285/s, 148.285/s/gpu LR: 0.000017 Logit Scale: 29.732 Contrastive_loss: 0.00041487 (0.048280) Loss: 0.00041487 (0.048280) 2025-03-20,05:52:59 | INFO | Train Epoch: 24 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.586/s, 149.586/s/gpu LR: 0.000017 Logit Scale: 29.734 Contrastive_loss: 0.063947 (0.048462) Loss: 0.063947 (0.048462) 2025-03-20,05:53:20 | INFO | Train Epoch: 24 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 151.148/s, 151.148/s/gpu LR: 0.000017 Logit Scale: 29.735 Contrastive_loss: 0.034149 (0.048298) Loss: 0.034149 (0.048298) 2025-03-20,05:53:42 | INFO | Train Epoch: 24 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 149.906/s, 149.906/s/gpu LR: 0.000017 Logit Scale: 29.747 Contrastive_loss: 0.0024653 (0.047777) Loss: 0.0024653 (0.047777) 2025-03-20,05:54:03 | INFO | Train Epoch: 24 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 148.752/s, 148.752/s/gpu LR: 0.000017 Logit Scale: 29.748 Contrastive_loss: 0.00052405 (0.047246) Loss: 0.00052405 (0.047246) 2025-03-20,05:54:25 | INFO | Train Epoch: 24 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 149.327/s, 149.327/s/gpu LR: 0.000017 Logit Scale: 29.755 Contrastive_loss: 0.053185 (0.047312) Loss: 0.053185 (0.047312) 2025-03-20,05:54:47 | INFO | Train Epoch: 24 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.217, 147.505/s, 147.505/s/gpu LR: 0.000017 Logit Scale: 29.761 Contrastive_loss: 0.071808 (0.047581) Loss: 0.071808 (0.047581) 2025-03-20,05:55:08 | INFO | Train Epoch: 24 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 149.163/s, 149.163/s/gpu LR: 0.000017 Logit Scale: 29.762 Contrastive_loss: 0.043944 (0.047542) Loss: 0.043944 (0.047542) 2025-03-20,05:55:30 | INFO | Train Epoch: 24 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 147.905/s, 147.905/s/gpu LR: 0.000017 Logit Scale: 29.764 Contrastive_loss: 0.0069069 (0.047105) Loss: 0.0069069 (0.047105) 2025-03-20,05:55:51 | INFO | Train Epoch: 24 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.392/s, 148.392/s/gpu LR: 0.000017 Logit Scale: 29.764 Contrastive_loss: 0.0019940 (0.046625) Loss: 0.0019940 (0.046625) 2025-03-20,05:56:13 | INFO | Train Epoch: 24 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.217, 147.569/s, 147.569/s/gpu LR: 0.000017 Logit Scale: 29.768 Contrastive_loss: 0.061886 (0.046785) Loss: 0.061886 (0.046785) 2025-03-20,05:56:34 | INFO | Train Epoch: 24 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.215, 149.434/s, 149.434/s/gpu LR: 0.000017 Logit Scale: 29.768 Contrastive_loss: 0.0061999 (0.046363) Loss: 0.0061999 (0.046363) 2025-03-20,05:56:56 | INFO | Train Epoch: 24 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 146.249/s, 146.249/s/gpu LR: 0.000017 Logit Scale: 29.770 Contrastive_loss: 0.00092998 (0.045894) Loss: 0.00092998 (0.045894) 2025-03-20,05:57:18 | INFO | Train Epoch: 24 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 148.505/s, 148.505/s/gpu LR: 0.000017 Logit Scale: 29.771 Contrastive_loss: 0.038366 (0.045817) Loss: 0.038366 (0.045817) 2025-03-20,05:57:39 | INFO | Train Epoch: 24 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 147.919/s, 147.919/s/gpu LR: 0.000017 Logit Scale: 29.771 Contrastive_loss: 0.019472 (0.045551) Loss: 0.019472 (0.045551) 2025-03-20,05:58:01 | INFO | Train Epoch: 24 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.218, 148.147/s, 148.147/s/gpu LR: 0.000017 Logit Scale: 29.767 Contrastive_loss: 0.0040374 (0.045136) Loss: 0.0040374 (0.045136) 2025-03-20,05:58:23 | INFO | Train Epoch: 24 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 147.403/s, 147.403/s/gpu LR: 0.000017 Logit Scale: 29.769 Contrastive_loss: 0.00013136 (0.044691) Loss: 0.00013136 (0.044691) 2025-03-20,05:58:44 | INFO | Train Epoch: 24 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 149.286/s, 149.286/s/gpu LR: 0.000017 Logit Scale: 29.773 Contrastive_loss: 0.069865 (0.044937) Loss: 0.069865 (0.044937) 2025-03-20,05:59:06 | INFO | Train Epoch: 24 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.216, 147.881/s, 147.881/s/gpu LR: 0.000017 Logit Scale: 29.773 Contrastive_loss: 0.081814 (0.045295) Loss: 0.081814 (0.045295) 2025-03-20,05:59:27 | INFO | Train Epoch: 24 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 147.388/s, 147.388/s/gpu LR: 0.000017 Logit Scale: 29.776 Contrastive_loss: 0.0012936 (0.044872) Loss: 0.0012936 (0.044872) 2025-03-20,05:59:49 | INFO | Train Epoch: 24 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.213, 151.330/s, 151.330/s/gpu LR: 0.000017 Logit Scale: 29.776 Contrastive_loss: 0.11127 (0.045505) Loss: 0.11127 (0.045505) 2025-03-20,06:00:10 | INFO | Train Epoch: 24 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 150.377/s, 150.377/s/gpu LR: 0.000017 Logit Scale: 29.779 Contrastive_loss: 0.044072 (0.045491) Loss: 0.044072 (0.045491) 2025-03-20,06:00:32 | INFO | Train Epoch: 24 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 147.947/s, 147.947/s/gpu LR: 0.000017 Logit Scale: 29.784 Contrastive_loss: 0.036094 (0.045403) Loss: 0.036094 (0.045403) 2025-03-20,06:00:53 | INFO | Train Epoch: 24 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.213, 151.020/s, 151.020/s/gpu LR: 0.000017 Logit Scale: 29.783 Contrastive_loss: 0.062500 (0.045562) Loss: 0.062500 (0.045562) 2025-03-20,06:01:15 | INFO | Train Epoch: 24 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.217, 146.012/s, 146.012/s/gpu LR: 0.000017 Logit Scale: 29.794 Contrastive_loss: 0.071597 (0.045800) Loss: 0.071597 (0.045800) 2025-03-20,06:01:37 | INFO | Train Epoch: 24 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.219, 147.283/s, 147.283/s/gpu LR: 0.000017 Logit Scale: 29.795 Contrastive_loss: 0.044417 (0.045788) Loss: 0.044417 (0.045788) 2025-03-20,06:01:58 | INFO | Train Epoch: 24 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 149.767/s, 149.767/s/gpu LR: 0.000017 Logit Scale: 29.797 Contrastive_loss: 0.034173 (0.045683) Loss: 0.034173 (0.045683) 2025-03-20,06:02:20 | INFO | Train Epoch: 24 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.513/s, 149.513/s/gpu LR: 0.000017 Logit Scale: 29.807 Contrastive_loss: 0.00012365 (0.045276) Loss: 0.00012365 (0.045276) 2025-03-20,06:02:41 | INFO | Train Epoch: 24 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 147.616/s, 147.616/s/gpu LR: 0.000017 Logit Scale: 29.811 Contrastive_loss: 0.0028710 (0.044901) Loss: 0.0028710 (0.044901) 2025-03-20,06:03:03 | INFO | Train Epoch: 24 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.216, 148.751/s, 148.751/s/gpu LR: 0.000017 Logit Scale: 29.818 Contrastive_loss: 0.0011957 (0.044518) Loss: 0.0011957 (0.044518) 2025-03-20,06:03:24 | INFO | Train Epoch: 24 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 151.430/s, 151.430/s/gpu LR: 0.000017 Logit Scale: 29.825 Contrastive_loss: 0.051603 (0.044579) Loss: 0.051603 (0.044579) 2025-03-20,06:03:46 | INFO | Train Epoch: 24 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 148.977/s, 148.977/s/gpu LR: 0.000017 Logit Scale: 29.828 Contrastive_loss: 0.057071 (0.044687) Loss: 0.057071 (0.044687) 2025-03-20,06:04:08 | INFO | Train Epoch: 24 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.218, 147.846/s, 147.846/s/gpu LR: 0.000017 Logit Scale: 29.827 Contrastive_loss: 0.096557 (0.045130) Loss: 0.096557 (0.045130) 2025-03-20,06:04:29 | INFO | Train Epoch: 24 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 148.970/s, 148.970/s/gpu LR: 0.000017 Logit Scale: 29.820 Contrastive_loss: 0.0030051 (0.044773) Loss: 0.0030051 (0.044773) 2025-03-20,06:04:51 | INFO | Train Epoch: 24 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 148.667/s, 148.667/s/gpu LR: 0.000017 Logit Scale: 29.814 Contrastive_loss: 0.0013472 (0.044409) Loss: 0.0013472 (0.044409) 2025-03-20,06:05:12 | INFO | Train Epoch: 24 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 148.304/s, 148.304/s/gpu LR: 0.000017 Logit Scale: 29.815 Contrastive_loss: 0.00079128 (0.044045) Loss: 0.00079128 (0.044045) 2025-03-20,06:05:34 | INFO | Train Epoch: 24 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.214, 149.356/s, 149.356/s/gpu LR: 0.000017 Logit Scale: 29.819 Contrastive_loss: 0.092839 (0.044448) Loss: 0.092839 (0.044448) 2025-03-20,06:05:55 | INFO | Train Epoch: 24 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 148.458/s, 148.458/s/gpu LR: 0.000017 Logit Scale: 29.827 Contrastive_loss: 0.058925 (0.044567) Loss: 0.058925 (0.044567) 2025-03-20,06:06:17 | INFO | Train Epoch: 24 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 148.445/s, 148.445/s/gpu LR: 0.000017 Logit Scale: 29.831 Contrastive_loss: 0.058758 (0.044682) Loss: 0.058758 (0.044682) 2025-03-20,06:06:39 | INFO | Train Epoch: 24 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 147.982/s, 147.982/s/gpu LR: 0.000016 Logit Scale: 29.833 Contrastive_loss: 0.045857 (0.044692) Loss: 0.045857 (0.044692) 2025-03-20,06:07:00 | INFO | Train Epoch: 24 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.213, 150.292/s, 150.292/s/gpu LR: 0.000016 Logit Scale: 29.830 Contrastive_loss: 0.060137 (0.044815) Loss: 0.060137 (0.044815) 2025-03-20,06:07:21 | INFO | Train Epoch: 24 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 149.474/s, 149.474/s/gpu LR: 0.000016 Logit Scale: 29.836 Contrastive_loss: 0.12168 (0.045425) Loss: 0.12168 (0.045425) 2025-03-20,06:07:43 | INFO | Train Epoch: 24 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 149.516/s, 149.516/s/gpu LR: 0.000016 Logit Scale: 29.846 Contrastive_loss: 0.00078300 (0.045074) Loss: 0.00078300 (0.045074) 2025-03-20,06:08:04 | INFO | Train Epoch: 24 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.213, 148.263/s, 148.263/s/gpu LR: 0.000016 Logit Scale: 29.839 Contrastive_loss: 0.0048475 (0.044760) Loss: 0.0048475 (0.044760) 2025-03-20,06:08:26 | INFO | Train Epoch: 24 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 149.439/s, 149.439/s/gpu LR: 0.000016 Logit Scale: 29.843 Contrastive_loss: 0.10077 (0.045194) Loss: 0.10077 (0.045194) 2025-03-20,06:08:47 | INFO | Train Epoch: 24 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 149.977/s, 149.977/s/gpu LR: 0.000016 Logit Scale: 29.848 Contrastive_loss: 0.00017009 (0.044848) Loss: 0.00017009 (0.044848) 2025-03-20,06:09:09 | INFO | Train Epoch: 24 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 146.349/s, 146.349/s/gpu LR: 0.000016 Logit Scale: 29.848 Contrastive_loss: 0.078513 (0.045105) Loss: 0.078513 (0.045105) 2025-03-20,06:09:30 | INFO | Train Epoch: 24 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.219, 146.323/s, 146.323/s/gpu LR: 0.000016 Logit Scale: 29.847 Contrastive_loss: 0.0074449 (0.044819) Loss: 0.0074449 (0.044819) 2025-03-20,06:09:53 | INFO | Train Epoch: 24 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.221, 144.215/s, 144.215/s/gpu LR: 0.000016 Logit Scale: 29.843 Contrastive_loss: 0.0019191 (0.044497) Loss: 0.0019191 (0.044497) 2025-03-20,06:10:14 | INFO | Train Epoch: 24 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.218, 148.036/s, 148.036/s/gpu LR: 0.000016 Logit Scale: 29.847 Contrastive_loss: 0.019406 (0.044309) Loss: 0.019406 (0.044309) 2025-03-20,06:10:36 | INFO | Train Epoch: 24 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.217, 149.242/s, 149.242/s/gpu LR: 0.000016 Logit Scale: 29.851 Contrastive_loss: 0.063656 (0.044453) Loss: 0.063656 (0.044453) 2025-03-20,06:10:57 | INFO | Train Epoch: 24 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 149.674/s, 149.674/s/gpu LR: 0.000016 Logit Scale: 29.853 Contrastive_loss: 0.0099785 (0.044199) Loss: 0.0099785 (0.044199) 2025-03-20,06:11:19 | INFO | Train Epoch: 24 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 149.306/s, 149.306/s/gpu LR: 0.000016 Logit Scale: 29.856 Contrastive_loss: 0.061488 (0.044325) Loss: 0.061488 (0.044325) 2025-03-20,06:11:40 | INFO | Train Epoch: 24 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 149.563/s, 149.563/s/gpu LR: 0.000016 Logit Scale: 29.853 Contrastive_loss: 0.053529 (0.044392) Loss: 0.053529 (0.044392) 2025-03-20,06:12:02 | INFO | Train Epoch: 24 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 148.374/s, 148.374/s/gpu LR: 0.000016 Logit Scale: 29.856 Contrastive_loss: 0.062663 (0.044524) Loss: 0.062663 (0.044524) 2025-03-20,06:12:23 | INFO | Train Epoch: 24 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 147.748/s, 147.748/s/gpu LR: 0.000016 Logit Scale: 29.857 Contrastive_loss: 0.015223 (0.044314) Loss: 0.015223 (0.044314) 2025-03-20,06:12:45 | INFO | Train Epoch: 24 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.221, 143.784/s, 143.784/s/gpu LR: 0.000016 Logit Scale: 29.861 Contrastive_loss: 0.14529 (0.045030) Loss: 0.14529 (0.045030) 2025-03-20,06:13:07 | INFO | Train Epoch: 24 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 145.918/s, 145.918/s/gpu LR: 0.000016 Logit Scale: 29.861 Contrastive_loss: 0.13077 (0.045634) Loss: 0.13077 (0.045634) 2025-03-20,06:13:29 | INFO | Train Epoch: 24 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.218, 148.950/s, 148.950/s/gpu LR: 0.000016 Logit Scale: 29.854 Contrastive_loss: 0.065975 (0.045776) Loss: 0.065975 (0.045776) 2025-03-20,06:13:50 | INFO | Train Epoch: 24 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 147.141/s, 147.141/s/gpu LR: 0.000016 Logit Scale: 29.854 Contrastive_loss: 0.00085218 (0.045465) Loss: 0.00085218 (0.045465) 2025-03-20,06:14:12 | INFO | Train Epoch: 24 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.579/s, 149.579/s/gpu LR: 0.000016 Logit Scale: 29.854 Contrastive_loss: 0.019284 (0.045284) Loss: 0.019284 (0.045284) 2025-03-20,06:14:33 | INFO | Train Epoch: 24 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.606/s, 149.606/s/gpu LR: 0.000016 Logit Scale: 29.857 Contrastive_loss: 0.0023623 (0.044990) Loss: 0.0023623 (0.044990) 2025-03-20,06:14:55 | INFO | Train Epoch: 24 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.155/s, 149.155/s/gpu LR: 0.000016 Logit Scale: 29.863 Contrastive_loss: 0.050359 (0.045026) Loss: 0.050359 (0.045026) 2025-03-20,06:15:16 | INFO | Train Epoch: 24 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.449/s, 148.449/s/gpu LR: 0.000016 Logit Scale: 29.867 Contrastive_loss: 0.16725 (0.045852) Loss: 0.16725 (0.045852) 2025-03-20,06:15:38 | INFO | Train Epoch: 24 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.555/s, 149.555/s/gpu LR: 0.000016 Logit Scale: 29.870 Contrastive_loss: 0.072006 (0.046028) Loss: 0.072006 (0.046028) 2025-03-20,06:15:59 | INFO | Train Epoch: 24 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 148.840/s, 148.840/s/gpu LR: 0.000016 Logit Scale: 29.879 Contrastive_loss: 0.072540 (0.046205) Loss: 0.072540 (0.046205) 2025-03-20,06:16:21 | INFO | Train Epoch: 24 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.212, 142.662/s, 142.662/s/gpu LR: 0.000016 Logit Scale: 29.883 Contrastive_loss: 0.0026467 (0.045916) Loss: 0.0026467 (0.045916) 2025-03-20,06:16:42 | INFO | Train Epoch: 24 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.219, 149.053/s, 149.053/s/gpu LR: 0.000016 Logit Scale: 29.885 Contrastive_loss: 0.00028182 (0.045616) Loss: 0.00028182 (0.045616) 2025-03-20,06:17:04 | INFO | Train Epoch: 24 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.218, 147.244/s, 147.244/s/gpu LR: 0.000016 Logit Scale: 29.884 Contrastive_loss: 0.047839 (0.045630) Loss: 0.047839 (0.045630) 2025-03-20,06:17:26 | INFO | Train Epoch: 24 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.969/s, 148.969/s/gpu LR: 0.000016 Logit Scale: 29.881 Contrastive_loss: 0.069143 (0.045783) Loss: 0.069143 (0.045783) 2025-03-20,06:17:47 | INFO | Train Epoch: 24 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 147.321/s, 147.321/s/gpu LR: 0.000016 Logit Scale: 29.879 Contrastive_loss: 5.1457e-05 (0.045488) Loss: 5.1457e-05 (0.045488) 2025-03-20,06:18:09 | INFO | Train Epoch: 24 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.217, 147.454/s, 147.454/s/gpu LR: 0.000016 Logit Scale: 29.880 Contrastive_loss: 0.013671 (0.045284) Loss: 0.013671 (0.045284) 2025-03-20,06:18:31 | INFO | Train Epoch: 24 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.219, 146.227/s, 146.227/s/gpu LR: 0.000016 Logit Scale: 29.886 Contrastive_loss: 0.16481 (0.046045) Loss: 0.16481 (0.046045) 2025-03-20,06:18:53 | INFO | Train Epoch: 24 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.220, 147.793/s, 147.793/s/gpu LR: 0.000016 Logit Scale: 29.889 Contrastive_loss: 0.0030443 (0.045773) Loss: 0.0030443 (0.045773) 2025-03-20,06:19:14 | INFO | Train Epoch: 24 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 151.404/s, 151.404/s/gpu LR: 0.000016 Logit Scale: 29.891 Contrastive_loss: 0.017692 (0.045597) Loss: 0.017692 (0.045597) 2025-03-20,06:19:36 | INFO | Train Epoch: 24 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.212, 152.079/s, 152.079/s/gpu LR: 0.000016 Logit Scale: 29.886 Contrastive_loss: 0.051851 (0.045636) Loss: 0.051851 (0.045636) 2025-03-20,06:19:57 | INFO | Train Epoch: 24 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 149.744/s, 149.744/s/gpu LR: 0.000016 Logit Scale: 29.889 Contrastive_loss: 0.0083972 (0.045404) Loss: 0.0083972 (0.045404) 2025-03-20,06:20:19 | INFO | Train Epoch: 24 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.217, 147.563/s, 147.563/s/gpu LR: 0.000016 Logit Scale: 29.885 Contrastive_loss: 0.0094126 (0.045182) Loss: 0.0094126 (0.045182) 2025-03-20,06:20:41 | INFO | Train Epoch: 24 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 148.737/s, 148.737/s/gpu LR: 0.000016 Logit Scale: 29.876 Contrastive_loss: 0.0017684 (0.044916) Loss: 0.0017684 (0.044916) 2025-03-20,06:21:02 | INFO | Train Epoch: 24 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 148.543/s, 148.543/s/gpu LR: 0.000016 Logit Scale: 29.867 Contrastive_loss: 0.0074723 (0.044688) Loss: 0.0074723 (0.044688) 2025-03-20,06:21:24 | INFO | Train Epoch: 24 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 147.877/s, 147.877/s/gpu LR: 0.000016 Logit Scale: 29.864 Contrastive_loss: 0.0019867 (0.044429) Loss: 0.0019867 (0.044429) 2025-03-20,06:21:45 | INFO | Train Epoch: 24 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 148.667/s, 148.667/s/gpu LR: 0.000015 Logit Scale: 29.864 Contrastive_loss: 0.061707 (0.044533) Loss: 0.061707 (0.044533) 2025-03-20,06:22:07 | INFO | Train Epoch: 24 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 151.279/s, 151.279/s/gpu LR: 0.000015 Logit Scale: 29.870 Contrastive_loss: 0.059951 (0.044625) Loss: 0.059951 (0.044625) 2025-03-20,06:22:29 | INFO | Train Epoch: 24 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 147.647/s, 147.647/s/gpu LR: 0.000015 Logit Scale: 29.879 Contrastive_loss: 0.0023775 (0.044374) Loss: 0.0023775 (0.044374) 2025-03-20,06:22:50 | INFO | Train Epoch: 24 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 147.998/s, 147.998/s/gpu LR: 0.000015 Logit Scale: 29.882 Contrastive_loss: 0.041320 (0.044356) Loss: 0.041320 (0.044356) 2025-03-20,06:23:12 | INFO | Train Epoch: 24 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 146.267/s, 146.267/s/gpu LR: 0.000015 Logit Scale: 29.872 Contrastive_loss: 0.070058 (0.044507) Loss: 0.070058 (0.044507) 2025-03-20,06:23:34 | INFO | Train Epoch: 24 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.217, 145.817/s, 145.817/s/gpu LR: 0.000015 Logit Scale: 29.873 Contrastive_loss: 0.012175 (0.044318) Loss: 0.012175 (0.044318) 2025-03-20,06:23:56 | INFO | Train Epoch: 24 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 146.825/s, 146.825/s/gpu LR: 0.000015 Logit Scale: 29.877 Contrastive_loss: 0.00096110 (0.044066) Loss: 0.00096110 (0.044066) 2025-03-20,06:24:18 | INFO | Train Epoch: 24 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 146.869/s, 146.869/s/gpu LR: 0.000015 Logit Scale: 29.882 Contrastive_loss: 0.00050718 (0.043814) Loss: 0.00050718 (0.043814) 2025-03-20,06:24:39 | INFO | Train Epoch: 24 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.214, 147.162/s, 147.162/s/gpu LR: 0.000015 Logit Scale: 29.880 Contrastive_loss: 0.063412 (0.043927) Loss: 0.063412 (0.043927) 2025-03-20,06:25:01 | INFO | Train Epoch: 24 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 149.739/s, 149.739/s/gpu LR: 0.000015 Logit Scale: 29.883 Contrastive_loss: 0.042433 (0.043918) Loss: 0.042433 (0.043918) 2025-03-20,06:25:22 | INFO | Train Epoch: 24 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.214, 147.841/s, 147.841/s/gpu LR: 0.000015 Logit Scale: 29.890 Contrastive_loss: 0.12657 (0.044388) Loss: 0.12657 (0.044388) 2025-03-20,06:25:44 | INFO | Train Epoch: 24 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 147.088/s, 147.088/s/gpu LR: 0.000015 Logit Scale: 29.890 Contrastive_loss: 0.023715 (0.044271) Loss: 0.023715 (0.044271) 2025-03-20,06:26:06 | INFO | Train Epoch: 24 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.221, 146.478/s, 146.478/s/gpu LR: 0.000015 Logit Scale: 29.889 Contrastive_loss: 0.067763 (0.044403) Loss: 0.067763 (0.044403) 2025-03-20,06:26:28 | INFO | Train Epoch: 24 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.220, 146.933/s, 146.933/s/gpu LR: 0.000015 Logit Scale: 29.882 Contrastive_loss: 0.0030450 (0.044172) Loss: 0.0030450 (0.044172) 2025-03-20,06:26:50 | INFO | Train Epoch: 24 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.219, 146.346/s, 146.346/s/gpu LR: 0.000015 Logit Scale: 29.875 Contrastive_loss: 0.027846 (0.044081) Loss: 0.027846 (0.044081) 2025-03-20,06:27:12 | INFO | Train Epoch: 24 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.217, 144.534/s, 144.534/s/gpu LR: 0.000015 Logit Scale: 29.881 Contrastive_loss: 0.13444 (0.044580) Loss: 0.13444 (0.044580) 2025-03-20,06:27:34 | INFO | Train Epoch: 24 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.220, 145.522/s, 145.522/s/gpu LR: 0.000015 Logit Scale: 29.879 Contrastive_loss: 0.044333 (0.044579) Loss: 0.044333 (0.044579) 2025-03-20,06:27:56 | INFO | Train Epoch: 24 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.219, 147.752/s, 147.752/s/gpu LR: 0.000015 Logit Scale: 29.881 Contrastive_loss: 0.088820 (0.044821) Loss: 0.088820 (0.044821) 2025-03-20,06:28:17 | INFO | Train Epoch: 24 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.217, 149.266/s, 149.266/s/gpu LR: 0.000015 Logit Scale: 29.881 Contrastive_loss: 0.015948 (0.044664) Loss: 0.015948 (0.044664) 2025-03-20,06:28:39 | INFO | Train Epoch: 24 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 149.558/s, 149.558/s/gpu LR: 0.000015 Logit Scale: 29.885 Contrastive_loss: 0.020707 (0.044534) Loss: 0.020707 (0.044534) 2025-03-20,06:29:00 | INFO | Train Epoch: 24 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 149.329/s, 149.329/s/gpu LR: 0.000015 Logit Scale: 29.884 Contrastive_loss: 0.16385 (0.045176) Loss: 0.16385 (0.045176) 2025-03-20,06:29:22 | INFO | Train Epoch: 24 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 149.689/s, 149.689/s/gpu LR: 0.000015 Logit Scale: 29.880 Contrastive_loss: 0.058219 (0.045246) Loss: 0.058219 (0.045246) 2025-03-20,06:29:44 | INFO | Train Epoch: 24 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 147.584/s, 147.584/s/gpu LR: 0.000015 Logit Scale: 29.882 Contrastive_loss: 0.030780 (0.045169) Loss: 0.030780 (0.045169) 2025-03-20,06:30:05 | INFO | Train Epoch: 24 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 150.337/s, 150.337/s/gpu LR: 0.000015 Logit Scale: 29.884 Contrastive_loss: 0.0035773 (0.044949) Loss: 0.0035773 (0.044949) 2025-03-20,06:30:26 | INFO | Train Epoch: 24 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.212, 149.852/s, 149.852/s/gpu LR: 0.000015 Logit Scale: 29.892 Contrastive_loss: 0.013419 (0.044783) Loss: 0.013419 (0.044783) 2025-03-20,06:30:48 | INFO | Train Epoch: 24 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 147.919/s, 147.919/s/gpu LR: 0.000015 Logit Scale: 29.893 Contrastive_loss: 0.0014154 (0.044556) Loss: 0.0014154 (0.044556) 2025-03-20,06:31:09 | INFO | Train Epoch: 24 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 151.079/s, 151.079/s/gpu LR: 0.000015 Logit Scale: 29.894 Contrastive_loss: 0.12662 (0.044983) Loss: 0.12662 (0.044983) 2025-03-20,06:31:31 | INFO | Train Epoch: 24 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.214, 147.856/s, 147.856/s/gpu LR: 0.000015 Logit Scale: 29.891 Contrastive_loss: 0.0016737 (0.044759) Loss: 0.0016737 (0.044759) 2025-03-20,06:31:52 | INFO | Train Epoch: 24 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 146.909/s, 146.909/s/gpu LR: 0.000015 Logit Scale: 29.887 Contrastive_loss: 0.0086776 (0.044573) Loss: 0.0086776 (0.044573) 2025-03-20,06:32:14 | INFO | Train Epoch: 24 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 149.647/s, 149.647/s/gpu LR: 0.000015 Logit Scale: 29.887 Contrastive_loss: 0.042798 (0.044564) Loss: 0.042798 (0.044564) 2025-03-20,06:32:35 | INFO | Train Epoch: 24 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 149.800/s, 149.800/s/gpu LR: 0.000015 Logit Scale: 29.883 Contrastive_loss: 0.0093946 (0.044384) Loss: 0.0093946 (0.044384) 2025-03-20,06:32:57 | INFO | Train Epoch: 24 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 150.256/s, 150.256/s/gpu LR: 0.000015 Logit Scale: 29.883 Contrastive_loss: 0.053641 (0.044431) Loss: 0.053641 (0.044431) 2025-03-20,06:33:18 | INFO | Train Epoch: 24 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 150.204/s, 150.204/s/gpu LR: 0.000015 Logit Scale: 29.888 Contrastive_loss: 0.0017062 (0.044215) Loss: 0.0017062 (0.044215) 2025-03-20,06:33:40 | INFO | Train Epoch: 24 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.456/s, 149.456/s/gpu LR: 0.000015 Logit Scale: 29.891 Contrastive_loss: 0.032352 (0.044156) Loss: 0.032352 (0.044156) 2025-03-20,06:34:01 | INFO | Train Epoch: 24 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 149.781/s, 149.781/s/gpu LR: 0.000015 Logit Scale: 29.892 Contrastive_loss: 0.047108 (0.044170) Loss: 0.047108 (0.044170) 2025-03-20,06:34:22 | INFO | Train Epoch: 24 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 151.579/s, 151.579/s/gpu LR: 0.000015 Logit Scale: 29.890 Contrastive_loss: 0.0032422 (0.043967) Loss: 0.0032422 (0.043967) 2025-03-20,06:34:44 | INFO | Train Epoch: 24 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 151.374/s, 151.374/s/gpu LR: 0.000015 Logit Scale: 29.894 Contrastive_loss: 0.058527 (0.044039) Loss: 0.058527 (0.044039) 2025-03-20,06:35:05 | INFO | Train Epoch: 24 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 149.694/s, 149.694/s/gpu LR: 0.000015 Logit Scale: 29.897 Contrastive_loss: 0.0020588 (0.043832) Loss: 0.0020588 (0.043832) 2025-03-20,06:35:27 | INFO | Train Epoch: 24 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 148.623/s, 148.623/s/gpu LR: 0.000015 Logit Scale: 29.901 Contrastive_loss: 0.00010014 (0.043618) Loss: 0.00010014 (0.043618) 2025-03-20,06:35:48 | INFO | Train Epoch: 24 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 148.544/s, 148.544/s/gpu LR: 0.000015 Logit Scale: 29.908 Contrastive_loss: 6.0804e-05 (0.043405) Loss: 6.0804e-05 (0.043405) 2025-03-20,06:36:10 | INFO | Train Epoch: 24 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 150.530/s, 150.530/s/gpu LR: 0.000015 Logit Scale: 29.909 Contrastive_loss: 0.0033707 (0.043211) Loss: 0.0033707 (0.043211) 2025-03-20,06:36:31 | INFO | Train Epoch: 24 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 151.637/s, 151.637/s/gpu LR: 0.000015 Logit Scale: 29.911 Contrastive_loss: 0.12852 (0.043623) Loss: 0.12852 (0.043623) 2025-03-20,06:36:53 | INFO | Train Epoch: 24 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.220, 147.031/s, 147.031/s/gpu LR: 0.000015 Logit Scale: 29.920 Contrastive_loss: 0.015771 (0.043489) Loss: 0.015771 (0.043489) 2025-03-20,06:37:15 | INFO | Train Epoch: 24 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.218, 146.324/s, 146.324/s/gpu LR: 0.000014 Logit Scale: 29.924 Contrastive_loss: 0.045387 (0.043498) Loss: 0.045387 (0.043498) 2025-03-20,06:37:37 | INFO | Train Epoch: 24 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.219, 146.204/s, 146.204/s/gpu LR: 0.000014 Logit Scale: 29.925 Contrastive_loss: 0.14198 (0.043967) Loss: 0.14198 (0.043967) 2025-03-20,06:37:59 | INFO | Train Epoch: 24 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.219, 146.564/s, 146.564/s/gpu LR: 0.000014 Logit Scale: 29.931 Contrastive_loss: 0.070614 (0.044093) Loss: 0.070614 (0.044093) 2025-03-20,06:38:21 | INFO | Train Epoch: 24 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.221, 146.515/s, 146.515/s/gpu LR: 0.000014 Logit Scale: 29.925 Contrastive_loss: 0.029375 (0.044024) Loss: 0.029375 (0.044024) 2025-03-20,06:38:43 | INFO | Train Epoch: 24 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.221, 145.424/s, 145.424/s/gpu LR: 0.000014 Logit Scale: 29.923 Contrastive_loss: 0.011526 (0.043871) Loss: 0.011526 (0.043871) 2025-03-20,06:39:05 | INFO | Train Epoch: 24 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.220, 147.720/s, 147.720/s/gpu LR: 0.000014 Logit Scale: 29.922 Contrastive_loss: 0.040317 (0.043855) Loss: 0.040317 (0.043855) 2025-03-20,06:39:27 | INFO | Train Epoch: 24 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 148.745/s, 148.745/s/gpu LR: 0.000014 Logit Scale: 29.927 Contrastive_loss: 0.0024245 (0.043662) Loss: 0.0024245 (0.043662) 2025-03-20,06:39:48 | INFO | Train Epoch: 24 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 148.084/s, 148.084/s/gpu LR: 0.000014 Logit Scale: 29.932 Contrastive_loss: 0.00090180 (0.043464) Loss: 0.00090180 (0.043464) 2025-03-20,06:40:10 | INFO | Train Epoch: 24 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 148.383/s, 148.383/s/gpu LR: 0.000014 Logit Scale: 29.929 Contrastive_loss: 0.0012301 (0.043270) Loss: 0.0012301 (0.043270) 2025-03-20,06:40:31 | INFO | Train Epoch: 24 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.213, 149.935/s, 149.935/s/gpu LR: 0.000014 Logit Scale: 29.923 Contrastive_loss: 0.016606 (0.043147) Loss: 0.016606 (0.043147) 2025-03-20,06:40:52 | INFO | Train Epoch: 24 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.212, 148.886/s, 148.886/s/gpu LR: 0.000014 Logit Scale: 29.928 Contrastive_loss: 0.022916 (0.043055) Loss: 0.022916 (0.043055) 2025-03-20,06:41:14 | INFO | Train Epoch: 24 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.529/s, 149.529/s/gpu LR: 0.000014 Logit Scale: 29.928 Contrastive_loss: 0.029884 (0.042995) Loss: 0.029884 (0.042995) 2025-03-20,06:41:35 | INFO | Train Epoch: 24 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.216, 147.618/s, 147.618/s/gpu LR: 0.000014 Logit Scale: 29.935 Contrastive_loss: 0.023525 (0.042907) Loss: 0.023525 (0.042907) 2025-03-20,06:41:57 | INFO | Train Epoch: 24 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 151.706/s, 151.706/s/gpu LR: 0.000014 Logit Scale: 29.932 Contrastive_loss: 0.098323 (0.043157) Loss: 0.098323 (0.043157) 2025-03-20,06:42:18 | INFO | Train Epoch: 24 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.213, 149.697/s, 149.697/s/gpu LR: 0.000014 Logit Scale: 29.934 Contrastive_loss: 0.047127 (0.043174) Loss: 0.047127 (0.043174) 2025-03-20,06:42:40 | INFO | Train Epoch: 24 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.214, 147.056/s, 147.056/s/gpu LR: 0.000014 Logit Scale: 29.934 Contrastive_loss: 0.030350 (0.043117) Loss: 0.030350 (0.043117) 2025-03-20,06:43:01 | INFO | Train Epoch: 24 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 148.526/s, 148.526/s/gpu LR: 0.000014 Logit Scale: 29.934 Contrastive_loss: 0.011530 (0.042977) Loss: 0.011530 (0.042977) 2025-03-20,06:43:23 | INFO | Train Epoch: 24 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.217, 148.829/s, 148.829/s/gpu LR: 0.000014 Logit Scale: 29.937 Contrastive_loss: 0.00039921 (0.042788) Loss: 0.00039921 (0.042788) 2025-03-20,06:43:45 | INFO | Train Epoch: 24 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 147.969/s, 147.969/s/gpu LR: 0.000014 Logit Scale: 29.940 Contrastive_loss: 0.016187 (0.042671) Loss: 0.016187 (0.042671) 2025-03-20,06:44:06 | INFO | Train Epoch: 24 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 148.685/s, 148.685/s/gpu LR: 0.000014 Logit Scale: 29.937 Contrastive_loss: 0.083834 (0.042852) Loss: 0.083834 (0.042852) 2025-03-20,06:44:28 | INFO | Train Epoch: 24 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 148.677/s, 148.677/s/gpu LR: 0.000014 Logit Scale: 29.939 Contrastive_loss: 0.0038991 (0.042682) Loss: 0.0038991 (0.042682) 2025-03-20,06:44:49 | INFO | Train Epoch: 24 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.217, 149.190/s, 149.190/s/gpu LR: 0.000014 Logit Scale: 29.942 Contrastive_loss: 0.0010415 (0.042500) Loss: 0.0010415 (0.042500) 2025-03-20,06:45:11 | INFO | Train Epoch: 24 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 149.801/s, 149.801/s/gpu LR: 0.000014 Logit Scale: 29.944 Contrastive_loss: 0.16027 (0.043010) Loss: 0.16027 (0.043010) 2025-03-20,06:45:32 | INFO | Train Epoch: 24 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 150.340/s, 150.340/s/gpu LR: 0.000014 Logit Scale: 29.945 Contrastive_loss: 0.052293 (0.043050) Loss: 0.052293 (0.043050) 2025-03-20,06:45:54 | INFO | Train Epoch: 24 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.216, 147.548/s, 147.548/s/gpu LR: 0.000014 Logit Scale: 29.947 Contrastive_loss: 0.042413 (0.043048) Loss: 0.042413 (0.043048) 2025-03-20,06:46:16 | INFO | Train Epoch: 24 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.218, 145.130/s, 145.130/s/gpu LR: 0.000014 Logit Scale: 29.948 Contrastive_loss: 0.11991 (0.043376) Loss: 0.11991 (0.043376) 2025-03-20,06:46:37 | INFO | Train Epoch: 24 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 147.500/s, 147.500/s/gpu LR: 0.000014 Logit Scale: 29.951 Contrastive_loss: 0.022557 (0.043287) Loss: 0.022557 (0.043287) 2025-03-20,06:46:59 | INFO | Train Epoch: 24 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 146.678/s, 146.678/s/gpu LR: 0.000014 Logit Scale: 29.941 Contrastive_loss: 0.069801 (0.043400) Loss: 0.069801 (0.043400) 2025-03-20,06:47:21 | INFO | Train Epoch: 24 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 148.628/s, 148.628/s/gpu LR: 0.000014 Logit Scale: 29.937 Contrastive_loss: 0.088250 (0.043589) Loss: 0.088250 (0.043589) 2025-03-20,06:47:42 | INFO | Train Epoch: 24 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 148.933/s, 148.933/s/gpu LR: 0.000014 Logit Scale: 29.940 Contrastive_loss: 0.020862 (0.043494) Loss: 0.020862 (0.043494) 2025-03-20,06:48:04 | INFO | Train Epoch: 24 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 151.316/s, 151.316/s/gpu LR: 0.000014 Logit Scale: 29.937 Contrastive_loss: 0.068229 (0.043597) Loss: 0.068229 (0.043597) 2025-03-20,06:48:25 | INFO | Train Epoch: 24 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.212, 148.957/s, 148.957/s/gpu LR: 0.000014 Logit Scale: 29.938 Contrastive_loss: 0.071108 (0.043712) Loss: 0.071108 (0.043712) 2025-03-20,06:48:33 | INFO | Train Epoch: 24 [765984/766009 (100%)] Data (t): 0.003 Batch (t): 0.216, 148.832/s, 148.832/s/gpu LR: 0.000014 Logit Scale: 29.938 Contrastive_loss: 0.11413 (0.044004) Loss: 0.11413 (0.044004) 2025-03-20,06:48:33 | INFO | Eval Epoch: 25 [32 / 7443] Clip Loss: 3.741406 2025-03-20,06:48:39 | INFO | Eval Epoch: 25 [3232 / 7443] Clip Loss: 0.849238 2025-03-20,06:48:45 | INFO | Eval Epoch: 25 [6432 / 7443] Clip Loss: 0.633546 2025-03-20,06:48:48 | INFO | Eval Epoch: 25 image_to_text_mean_rank: 69.5588 image_to_text_median_rank: 5.0000 image_to_text_R@1: 0.1917 image_to_text_R@5: 0.5447 image_to_text_R@10: 0.7086 text_to_image_mean_rank: 46.2322 text_to_image_median_rank: 5.0000 text_to_image_R@1: 0.1963 text_to_image_R@5: 0.5416 text_to_image_R@10: 0.7019 clip_val_loss: 0.5871 epoch: 25.0000 num_samples: 7443.0000 2025-03-20,06:49:20 | INFO | Start epoch 25 2025-03-20,06:49:20 | INFO | Train Epoch: 25 [ 32/766009 (0%)] Data (t): 0.184 Batch (t): 0.391, 81.9149/s, 81.9149/s/gpu LR: 0.000014 Logit Scale: 29.938 Contrastive_loss: 0.047085 (0.047085) Loss: 0.047085 (0.047085) 2025-03-20,06:49:41 | INFO | Train Epoch: 25 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.214, 150.031/s, 150.031/s/gpu LR: 0.000014 Logit Scale: 29.940 Contrastive_loss: 0.024700 (0.035893) Loss: 0.024700 (0.035893) 2025-03-20,06:50:03 | INFO | Train Epoch: 25 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 149.403/s, 149.403/s/gpu LR: 0.000014 Logit Scale: 29.934 Contrastive_loss: 0.044584 (0.038790) Loss: 0.044584 (0.038790) 2025-03-20,06:50:25 | INFO | Train Epoch: 25 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 145.763/s, 145.763/s/gpu LR: 0.000014 Logit Scale: 29.936 Contrastive_loss: 0.10879 (0.056291) Loss: 0.10879 (0.056291) 2025-03-20,06:50:46 | INFO | Train Epoch: 25 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 147.872/s, 147.872/s/gpu LR: 0.000014 Logit Scale: 29.936 Contrastive_loss: 0.029010 (0.050835) Loss: 0.029010 (0.050835) 2025-03-20,06:51:08 | INFO | Train Epoch: 25 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 149.933/s, 149.933/s/gpu LR: 0.000014 Logit Scale: 29.935 Contrastive_loss: 0.13115 (0.064221) Loss: 0.13115 (0.064221) 2025-03-20,06:51:29 | INFO | Train Epoch: 25 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 149.994/s, 149.994/s/gpu LR: 0.000014 Logit Scale: 29.935 Contrastive_loss: 0.00058247 (0.055130) Loss: 0.00058247 (0.055130) 2025-03-20,06:51:51 | INFO | Train Epoch: 25 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 146.007/s, 146.007/s/gpu LR: 0.000014 Logit Scale: 29.937 Contrastive_loss: 0.012053 (0.049745) Loss: 0.012053 (0.049745) 2025-03-20,06:52:12 | INFO | Train Epoch: 25 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 148.363/s, 148.363/s/gpu LR: 0.000014 Logit Scale: 29.945 Contrastive_loss: 0.0016841 (0.044405) Loss: 0.0016841 (0.044405) 2025-03-20,06:52:34 | INFO | Train Epoch: 25 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.855/s, 149.855/s/gpu LR: 0.000014 Logit Scale: 29.946 Contrastive_loss: 0.0096224 (0.040927) Loss: 0.0096224 (0.040927) 2025-03-20,06:52:55 | INFO | Train Epoch: 25 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 148.871/s, 148.871/s/gpu LR: 0.000014 Logit Scale: 29.950 Contrastive_loss: 0.0027995 (0.037461) Loss: 0.0027995 (0.037461) 2025-03-20,06:53:17 | INFO | Train Epoch: 25 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.065/s, 149.065/s/gpu LR: 0.000014 Logit Scale: 29.950 Contrastive_loss: 0.038307 (0.037531) Loss: 0.038307 (0.037531) 2025-03-20,06:53:38 | INFO | Train Epoch: 25 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.104/s, 149.104/s/gpu LR: 0.000014 Logit Scale: 29.950 Contrastive_loss: 0.00010967 (0.034653) Loss: 0.00010967 (0.034653) 2025-03-20,06:54:00 | INFO | Train Epoch: 25 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.352/s, 149.352/s/gpu LR: 0.000013 Logit Scale: 29.950 Contrastive_loss: 0.045411 (0.035421) Loss: 0.045411 (0.035421) 2025-03-20,06:54:21 | INFO | Train Epoch: 25 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 148.117/s, 148.117/s/gpu LR: 0.000013 Logit Scale: 29.958 Contrastive_loss: 0.034483 (0.035359) Loss: 0.034483 (0.035359) 2025-03-20,06:54:43 | INFO | Train Epoch: 25 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 150.989/s, 150.989/s/gpu LR: 0.000013 Logit Scale: 29.965 Contrastive_loss: 0.00038551 (0.033173) Loss: 0.00038551 (0.033173) 2025-03-20,06:55:04 | INFO | Train Epoch: 25 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.079/s, 149.079/s/gpu LR: 0.000013 Logit Scale: 29.968 Contrastive_loss: 0.070914 (0.035393) Loss: 0.070914 (0.035393) 2025-03-20,06:55:26 | INFO | Train Epoch: 25 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.953/s, 149.953/s/gpu LR: 0.000013 Logit Scale: 29.972 Contrastive_loss: 0.044380 (0.035892) Loss: 0.044380 (0.035892) 2025-03-20,06:55:47 | INFO | Train Epoch: 25 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 150.289/s, 150.289/s/gpu LR: 0.000013 Logit Scale: 29.974 Contrastive_loss: 0.13493 (0.041105) Loss: 0.13493 (0.041105) 2025-03-20,06:56:08 | INFO | Train Epoch: 25 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 148.194/s, 148.194/s/gpu LR: 0.000013 Logit Scale: 29.978 Contrastive_loss: 0.066086 (0.042354) Loss: 0.066086 (0.042354) 2025-03-20,06:56:30 | INFO | Train Epoch: 25 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.217, 144.972/s, 144.972/s/gpu LR: 0.000013 Logit Scale: 29.981 Contrastive_loss: 0.0046893 (0.040560) Loss: 0.0046893 (0.040560) 2025-03-20,06:56:51 | INFO | Train Epoch: 25 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.213, 150.125/s, 150.125/s/gpu LR: 0.000013 Logit Scale: 29.981 Contrastive_loss: 0.028852 (0.040028) Loss: 0.028852 (0.040028) 2025-03-20,06:57:13 | INFO | Train Epoch: 25 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.219, 142.646/s, 142.646/s/gpu LR: 0.000013 Logit Scale: 29.987 Contrastive_loss: 0.0062634 (0.038560) Loss: 0.0062634 (0.038560) 2025-03-20,06:57:36 | INFO | Train Epoch: 25 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.224, 143.611/s, 143.611/s/gpu LR: 0.000013 Logit Scale: 29.987 Contrastive_loss: 0.0093963 (0.037345) Loss: 0.0093963 (0.037345) 2025-03-20,06:57:58 | INFO | Train Epoch: 25 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.222, 144.126/s, 144.126/s/gpu LR: 0.000013 Logit Scale: 29.991 Contrastive_loss: 0.055644 (0.038077) Loss: 0.055644 (0.038077) 2025-03-20,06:58:20 | INFO | Train Epoch: 25 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 147.598/s, 147.598/s/gpu LR: 0.000013 Logit Scale: 29.993 Contrastive_loss: 0.095840 (0.040299) Loss: 0.095840 (0.040299) 2025-03-20,06:58:42 | INFO | Train Epoch: 25 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.219, 146.182/s, 146.182/s/gpu LR: 0.000013 Logit Scale: 29.991 Contrastive_loss: 0.00049425 (0.038824) Loss: 0.00049425 (0.038824) 2025-03-20,06:59:04 | INFO | Train Epoch: 25 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.220, 142.820/s, 142.820/s/gpu LR: 0.000013 Logit Scale: 29.990 Contrastive_loss: 0.060020 (0.039581) Loss: 0.060020 (0.039581) 2025-03-20,06:59:26 | INFO | Train Epoch: 25 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.220, 145.356/s, 145.356/s/gpu LR: 0.000013 Logit Scale: 29.989 Contrastive_loss: 0.0020097 (0.038286) Loss: 0.0020097 (0.038286) 2025-03-20,06:59:48 | INFO | Train Epoch: 25 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.220, 147.971/s, 147.971/s/gpu LR: 0.000013 Logit Scale: 29.995 Contrastive_loss: 0.046601 (0.038563) Loss: 0.046601 (0.038563) 2025-03-20,07:00:10 | INFO | Train Epoch: 25 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.219, 143.692/s, 143.692/s/gpu LR: 0.000013 Logit Scale: 29.993 Contrastive_loss: 0.072746 (0.039666) Loss: 0.072746 (0.039666) 2025-03-20,07:00:32 | INFO | Train Epoch: 25 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.219, 147.559/s, 147.559/s/gpu LR: 0.000013 Logit Scale: 29.998 Contrastive_loss: 0.041403 (0.039720) Loss: 0.041403 (0.039720) 2025-03-20,07:00:53 | INFO | Train Epoch: 25 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.219, 147.980/s, 147.980/s/gpu LR: 0.000013 Logit Scale: 29.995 Contrastive_loss: 0.00021421 (0.038523) Loss: 0.00021421 (0.038523) 2025-03-20,07:01:15 | INFO | Train Epoch: 25 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.218, 144.441/s, 144.441/s/gpu LR: 0.000013 Logit Scale: 29.999 Contrastive_loss: 0.011169 (0.037718) Loss: 0.011169 (0.037718) 2025-03-20,07:01:37 | INFO | Train Epoch: 25 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.221, 145.562/s, 145.562/s/gpu LR: 0.000013 Logit Scale: 30.004 Contrastive_loss: 0.10338 (0.039594) Loss: 0.10338 (0.039594) 2025-03-20,07:01:59 | INFO | Train Epoch: 25 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.221, 146.130/s, 146.130/s/gpu LR: 0.000013 Logit Scale: 30.003 Contrastive_loss: 0.00013678 (0.038498) Loss: 0.00013678 (0.038498) 2025-03-20,07:02:22 | INFO | Train Epoch: 25 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.220, 147.650/s, 147.650/s/gpu LR: 0.000013 Logit Scale: 30.006 Contrastive_loss: 0.096144 (0.040056) Loss: 0.096144 (0.040056) 2025-03-20,07:02:43 | INFO | Train Epoch: 25 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 142.866/s, 142.866/s/gpu LR: 0.000013 Logit Scale: 30.012 Contrastive_loss: 0.00010652 (0.039005) Loss: 0.00010652 (0.039005) 2025-03-20,07:03:05 | INFO | Train Epoch: 25 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.216, 147.831/s, 147.831/s/gpu LR: 0.000013 Logit Scale: 30.013 Contrastive_loss: 0.085901 (0.040207) Loss: 0.085901 (0.040207) 2025-03-20,07:03:26 | INFO | Train Epoch: 25 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.217, 147.442/s, 147.442/s/gpu LR: 0.000013 Logit Scale: 30.015 Contrastive_loss: 0.044355 (0.040311) Loss: 0.044355 (0.040311) 2025-03-20,07:03:48 | INFO | Train Epoch: 25 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 150.198/s, 150.198/s/gpu LR: 0.000013 Logit Scale: 30.017 Contrastive_loss: 0.080417 (0.041289) Loss: 0.080417 (0.041289) 2025-03-20,07:04:10 | INFO | Train Epoch: 25 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 149.094/s, 149.094/s/gpu LR: 0.000013 Logit Scale: 30.019 Contrastive_loss: 0.066874 (0.041899) Loss: 0.066874 (0.041899) 2025-03-20,07:04:31 | INFO | Train Epoch: 25 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.385/s, 149.385/s/gpu LR: 0.000013 Logit Scale: 30.024 Contrastive_loss: 0.046673 (0.042010) Loss: 0.046673 (0.042010) 2025-03-20,07:04:53 | INFO | Train Epoch: 25 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.819/s, 149.819/s/gpu LR: 0.000013 Logit Scale: 30.029 Contrastive_loss: 0.074364 (0.042745) Loss: 0.074364 (0.042745) 2025-03-20,07:05:14 | INFO | Train Epoch: 25 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.851/s, 149.851/s/gpu LR: 0.000013 Logit Scale: 30.023 Contrastive_loss: 0.0046541 (0.041898) Loss: 0.0046541 (0.041898) 2025-03-20,07:05:35 | INFO | Train Epoch: 25 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 150.300/s, 150.300/s/gpu LR: 0.000013 Logit Scale: 30.024 Contrastive_loss: 0.045483 (0.041976) Loss: 0.045483 (0.041976) 2025-03-20,07:05:57 | INFO | Train Epoch: 25 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 149.780/s, 149.780/s/gpu LR: 0.000013 Logit Scale: 30.022 Contrastive_loss: 0.0011634 (0.041108) Loss: 0.0011634 (0.041108) 2025-03-20,07:06:18 | INFO | Train Epoch: 25 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 148.927/s, 148.927/s/gpu LR: 0.000013 Logit Scale: 30.025 Contrastive_loss: 0.0031844 (0.040318) Loss: 0.0031844 (0.040318) 2025-03-20,07:06:40 | INFO | Train Epoch: 25 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.215, 150.124/s, 150.124/s/gpu LR: 0.000013 Logit Scale: 30.031 Contrastive_loss: 0.048194 (0.040479) Loss: 0.048194 (0.040479) 2025-03-20,07:07:01 | INFO | Train Epoch: 25 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.679/s, 149.679/s/gpu LR: 0.000013 Logit Scale: 30.036 Contrastive_loss: 0.046624 (0.040602) Loss: 0.046624 (0.040602) 2025-03-20,07:07:23 | INFO | Train Epoch: 25 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 149.799/s, 149.799/s/gpu LR: 0.000013 Logit Scale: 30.035 Contrastive_loss: 0.00022141 (0.039810) Loss: 0.00022141 (0.039810) 2025-03-20,07:07:44 | INFO | Train Epoch: 25 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 148.918/s, 148.918/s/gpu LR: 0.000013 Logit Scale: 30.040 Contrastive_loss: 0.14788 (0.041888) Loss: 0.14788 (0.041888) 2025-03-20,07:08:06 | INFO | Train Epoch: 25 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 149.019/s, 149.019/s/gpu LR: 0.000013 Logit Scale: 30.043 Contrastive_loss: 0.00014943 (0.041100) Loss: 0.00014943 (0.041100) 2025-03-20,07:08:27 | INFO | Train Epoch: 25 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.213, 151.284/s, 151.284/s/gpu LR: 0.000013 Logit Scale: 30.051 Contrastive_loss: 0.017992 (0.040673) Loss: 0.017992 (0.040673) 2025-03-20,07:08:49 | INFO | Train Epoch: 25 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.868/s, 149.868/s/gpu LR: 0.000013 Logit Scale: 30.051 Contrastive_loss: 0.00034271 (0.039939) Loss: 0.00034271 (0.039939) 2025-03-20,07:09:10 | INFO | Train Epoch: 25 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 150.015/s, 150.015/s/gpu LR: 0.000013 Logit Scale: 30.052 Contrastive_loss: 0.00032320 (0.039232) Loss: 0.00032320 (0.039232) 2025-03-20,07:09:32 | INFO | Train Epoch: 25 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.216, 148.065/s, 148.065/s/gpu LR: 0.000013 Logit Scale: 30.058 Contrastive_loss: 0.048089 (0.039387) Loss: 0.048089 (0.039387) 2025-03-20,07:09:53 | INFO | Train Epoch: 25 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 149.344/s, 149.344/s/gpu LR: 0.000013 Logit Scale: 30.054 Contrastive_loss: 0.023674 (0.039116) Loss: 0.023674 (0.039116) 2025-03-20,07:10:15 | INFO | Train Epoch: 25 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.218, 145.658/s, 145.658/s/gpu LR: 0.000012 Logit Scale: 30.058 Contrastive_loss: 0.012284 (0.038662) Loss: 0.012284 (0.038662) 2025-03-20,07:10:37 | INFO | Train Epoch: 25 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.219, 150.892/s, 150.892/s/gpu LR: 0.000012 Logit Scale: 30.053 Contrastive_loss: 0.0079597 (0.038150) Loss: 0.0079597 (0.038150) 2025-03-20,07:10:59 | INFO | Train Epoch: 25 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 145.964/s, 145.964/s/gpu LR: 0.000012 Logit Scale: 30.053 Contrastive_loss: 0.052557 (0.038386) Loss: 0.052557 (0.038386) 2025-03-20,07:11:21 | INFO | Train Epoch: 25 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.220, 146.076/s, 146.076/s/gpu LR: 0.000012 Logit Scale: 30.054 Contrastive_loss: 0.032945 (0.038298) Loss: 0.032945 (0.038298) 2025-03-20,07:11:42 | INFO | Train Epoch: 25 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 142.585/s, 142.585/s/gpu LR: 0.000012 Logit Scale: 30.058 Contrastive_loss: 0.00011762 (0.037692) Loss: 0.00011762 (0.037692) 2025-03-20,07:12:04 | INFO | Train Epoch: 25 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 148.527/s, 148.527/s/gpu LR: 0.000012 Logit Scale: 30.057 Contrastive_loss: 0.050310 (0.037889) Loss: 0.050310 (0.037889) 2025-03-20,07:12:25 | INFO | Train Epoch: 25 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 147.523/s, 147.523/s/gpu LR: 0.000012 Logit Scale: 30.060 Contrastive_loss: 0.00013695 (0.037309) Loss: 0.00013695 (0.037309) 2025-03-20,07:12:47 | INFO | Train Epoch: 25 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 148.836/s, 148.836/s/gpu LR: 0.000012 Logit Scale: 30.060 Contrastive_loss: 0.018379 (0.037022) Loss: 0.018379 (0.037022) 2025-03-20,07:13:08 | INFO | Train Epoch: 25 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 148.842/s, 148.842/s/gpu LR: 0.000012 Logit Scale: 30.059 Contrastive_loss: 0.045974 (0.037155) Loss: 0.045974 (0.037155) 2025-03-20,07:13:30 | INFO | Train Epoch: 25 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.217, 148.361/s, 148.361/s/gpu LR: 0.000012 Logit Scale: 30.065 Contrastive_loss: 0.11878 (0.038356) Loss: 0.11878 (0.038356) 2025-03-20,07:13:51 | INFO | Train Epoch: 25 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.216, 148.582/s, 148.582/s/gpu LR: 0.000012 Logit Scale: 30.068 Contrastive_loss: 0.00031261 (0.037804) Loss: 0.00031261 (0.037804) 2025-03-20,07:14:13 | INFO | Train Epoch: 25 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 150.116/s, 150.116/s/gpu LR: 0.000012 Logit Scale: 30.065 Contrastive_loss: 0.058035 (0.038093) Loss: 0.058035 (0.038093) 2025-03-20,07:14:34 | INFO | Train Epoch: 25 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 147.404/s, 147.404/s/gpu LR: 0.000012 Logit Scale: 30.061 Contrastive_loss: 0.043430 (0.038169) Loss: 0.043430 (0.038169) 2025-03-20,07:14:56 | INFO | Train Epoch: 25 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 149.289/s, 149.289/s/gpu LR: 0.000012 Logit Scale: 30.057 Contrastive_loss: 0.085409 (0.038825) Loss: 0.085409 (0.038825) 2025-03-20,07:15:18 | INFO | Train Epoch: 25 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.614/s, 149.614/s/gpu LR: 0.000012 Logit Scale: 30.060 Contrastive_loss: 0.10170 (0.039686) Loss: 0.10170 (0.039686) 2025-03-20,07:15:39 | INFO | Train Epoch: 25 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 150.018/s, 150.018/s/gpu LR: 0.000012 Logit Scale: 30.063 Contrastive_loss: 0.00030431 (0.039154) Loss: 0.00030431 (0.039154) 2025-03-20,07:16:01 | INFO | Train Epoch: 25 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 149.718/s, 149.718/s/gpu LR: 0.000012 Logit Scale: 30.062 Contrastive_loss: 0.040310 (0.039169) Loss: 0.040310 (0.039169) 2025-03-20,07:16:22 | INFO | Train Epoch: 25 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 145.781/s, 145.781/s/gpu LR: 0.000012 Logit Scale: 30.067 Contrastive_loss: 0.00099752 (0.038667) Loss: 0.00099752 (0.038667) 2025-03-20,07:16:44 | INFO | Train Epoch: 25 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 149.532/s, 149.532/s/gpu LR: 0.000012 Logit Scale: 30.072 Contrastive_loss: 0.0049108 (0.038229) Loss: 0.0049108 (0.038229) 2025-03-20,07:17:05 | INFO | Train Epoch: 25 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 148.371/s, 148.371/s/gpu LR: 0.000012 Logit Scale: 30.069 Contrastive_loss: 0.065387 (0.038577) Loss: 0.065387 (0.038577) 2025-03-20,07:17:27 | INFO | Train Epoch: 25 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 149.744/s, 149.744/s/gpu LR: 0.000012 Logit Scale: 30.074 Contrastive_loss: 0.013630 (0.038261) Loss: 0.013630 (0.038261) 2025-03-20,07:17:48 | INFO | Train Epoch: 25 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 145.612/s, 145.612/s/gpu LR: 0.000012 Logit Scale: 30.080 Contrastive_loss: 0.015439 (0.037976) Loss: 0.015439 (0.037976) 2025-03-20,07:18:10 | INFO | Train Epoch: 25 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 146.890/s, 146.890/s/gpu LR: 0.000012 Logit Scale: 30.084 Contrastive_loss: 0.012491 (0.037661) Loss: 0.012491 (0.037661) 2025-03-20,07:18:32 | INFO | Train Epoch: 25 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 149.206/s, 149.206/s/gpu LR: 0.000012 Logit Scale: 30.091 Contrastive_loss: 0.0093826 (0.037316) Loss: 0.0093826 (0.037316) 2025-03-20,07:18:53 | INFO | Train Epoch: 25 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 149.734/s, 149.734/s/gpu LR: 0.000012 Logit Scale: 30.101 Contrastive_loss: 0.00031899 (0.036871) Loss: 0.00031899 (0.036871) 2025-03-20,07:19:15 | INFO | Train Epoch: 25 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.651/s, 148.651/s/gpu LR: 0.000012 Logit Scale: 30.100 Contrastive_loss: 0.00039953 (0.036436) Loss: 0.00039953 (0.036436) 2025-03-20,07:19:36 | INFO | Train Epoch: 25 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.217, 149.419/s, 149.419/s/gpu LR: 0.000012 Logit Scale: 30.104 Contrastive_loss: 0.055005 (0.036655) Loss: 0.055005 (0.036655) 2025-03-20,07:19:58 | INFO | Train Epoch: 25 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 148.924/s, 148.924/s/gpu LR: 0.000012 Logit Scale: 30.099 Contrastive_loss: 0.056353 (0.036884) Loss: 0.056353 (0.036884) 2025-03-20,07:20:19 | INFO | Train Epoch: 25 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.218, 147.305/s, 147.305/s/gpu LR: 0.000012 Logit Scale: 30.104 Contrastive_loss: 0.081186 (0.037393) Loss: 0.081186 (0.037393) 2025-03-20,07:20:41 | INFO | Train Epoch: 25 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.218, 146.911/s, 146.911/s/gpu LR: 0.000012 Logit Scale: 30.105 Contrastive_loss: 0.0050362 (0.037025) Loss: 0.0050362 (0.037025) 2025-03-20,07:21:03 | INFO | Train Epoch: 25 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.218, 144.853/s, 144.853/s/gpu LR: 0.000012 Logit Scale: 30.100 Contrastive_loss: 0.010982 (0.036733) Loss: 0.010982 (0.036733) 2025-03-20,07:21:25 | INFO | Train Epoch: 25 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.218, 147.236/s, 147.236/s/gpu LR: 0.000012 Logit Scale: 30.100 Contrastive_loss: 0.17384 (0.038256) Loss: 0.17384 (0.038256) 2025-03-20,07:21:47 | INFO | Train Epoch: 25 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.220, 144.838/s, 144.838/s/gpu LR: 0.000012 Logit Scale: 30.102 Contrastive_loss: 0.054852 (0.038439) Loss: 0.054852 (0.038439) 2025-03-20,07:22:09 | INFO | Train Epoch: 25 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.221, 146.017/s, 146.017/s/gpu LR: 0.000012 Logit Scale: 30.097 Contrastive_loss: 0.00098820 (0.038031) Loss: 0.00098820 (0.038031) 2025-03-20,07:22:31 | INFO | Train Epoch: 25 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.217, 148.136/s, 148.136/s/gpu LR: 0.000012 Logit Scale: 30.099 Contrastive_loss: 0.092918 (0.038622) Loss: 0.092918 (0.038622) 2025-03-20,07:22:52 | INFO | Train Epoch: 25 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.217, 146.201/s, 146.201/s/gpu LR: 0.000012 Logit Scale: 30.098 Contrastive_loss: 0.018709 (0.038410) Loss: 0.018709 (0.038410) 2025-03-20,07:23:14 | INFO | Train Epoch: 25 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.216, 149.387/s, 149.387/s/gpu LR: 0.000012 Logit Scale: 30.101 Contrastive_loss: 0.053858 (0.038572) Loss: 0.053858 (0.038572) 2025-03-20,07:23:36 | INFO | Train Epoch: 25 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.218, 146.403/s, 146.403/s/gpu LR: 0.000012 Logit Scale: 30.104 Contrastive_loss: 0.0090540 (0.038265) Loss: 0.0090540 (0.038265) 2025-03-20,07:23:57 | INFO | Train Epoch: 25 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.213, 151.529/s, 151.529/s/gpu LR: 0.000012 Logit Scale: 30.110 Contrastive_loss: 0.064307 (0.038533) Loss: 0.064307 (0.038533) 2025-03-20,07:24:18 | INFO | Train Epoch: 25 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.212, 149.031/s, 149.031/s/gpu LR: 0.000012 Logit Scale: 30.110 Contrastive_loss: 0.014298 (0.038286) Loss: 0.014298 (0.038286) 2025-03-20,07:24:40 | INFO | Train Epoch: 25 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.217, 148.275/s, 148.275/s/gpu LR: 0.000012 Logit Scale: 30.112 Contrastive_loss: 0.0011618 (0.037911) Loss: 0.0011618 (0.037911) 2025-03-20,07:25:02 | INFO | Train Epoch: 25 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.218, 146.625/s, 146.625/s/gpu LR: 0.000012 Logit Scale: 30.116 Contrastive_loss: 0.0073560 (0.037606) Loss: 0.0073560 (0.037606) 2025-03-20,07:25:23 | INFO | Train Epoch: 25 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.218, 147.176/s, 147.176/s/gpu LR: 0.000012 Logit Scale: 30.116 Contrastive_loss: 0.051605 (0.037744) Loss: 0.051605 (0.037744) 2025-03-20,07:25:45 | INFO | Train Epoch: 25 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.218, 145.002/s, 145.002/s/gpu LR: 0.000012 Logit Scale: 30.113 Contrastive_loss: 0.051560 (0.037880) Loss: 0.051560 (0.037880) 2025-03-20,07:26:07 | INFO | Train Epoch: 25 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 150.265/s, 150.265/s/gpu LR: 0.000012 Logit Scale: 30.116 Contrastive_loss: 0.016309 (0.037670) Loss: 0.016309 (0.037670) 2025-03-20,07:26:28 | INFO | Train Epoch: 25 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.939/s, 149.939/s/gpu LR: 0.000012 Logit Scale: 30.119 Contrastive_loss: 0.0016529 (0.037324) Loss: 0.0016529 (0.037324) 2025-03-20,07:26:50 | INFO | Train Epoch: 25 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 150.176/s, 150.176/s/gpu LR: 0.000012 Logit Scale: 30.113 Contrastive_loss: 0.24847 (0.039335) Loss: 0.24847 (0.039335) 2025-03-20,07:27:11 | INFO | Train Epoch: 25 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 149.302/s, 149.302/s/gpu LR: 0.000012 Logit Scale: 30.112 Contrastive_loss: 0.081646 (0.039734) Loss: 0.081646 (0.039734) 2025-03-20,07:27:33 | INFO | Train Epoch: 25 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 151.174/s, 151.174/s/gpu LR: 0.000011 Logit Scale: 30.112 Contrastive_loss: 0.080111 (0.040111) Loss: 0.080111 (0.040111) 2025-03-20,07:27:54 | INFO | Train Epoch: 25 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 148.244/s, 148.244/s/gpu LR: 0.000011 Logit Scale: 30.112 Contrastive_loss: 0.068294 (0.040372) Loss: 0.068294 (0.040372) 2025-03-20,07:28:16 | INFO | Train Epoch: 25 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.220, 148.461/s, 148.461/s/gpu LR: 0.000011 Logit Scale: 30.115 Contrastive_loss: 0.043630 (0.040402) Loss: 0.043630 (0.040402) 2025-03-20,07:28:38 | INFO | Train Epoch: 25 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 149.465/s, 149.465/s/gpu LR: 0.000011 Logit Scale: 30.115 Contrastive_loss: 0.019370 (0.040211) Loss: 0.019370 (0.040211) 2025-03-20,07:29:00 | INFO | Train Epoch: 25 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.220, 144.809/s, 144.809/s/gpu LR: 0.000011 Logit Scale: 30.122 Contrastive_loss: 0.011433 (0.039952) Loss: 0.011433 (0.039952) 2025-03-20,07:29:22 | INFO | Train Epoch: 25 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.222, 144.973/s, 144.973/s/gpu LR: 0.000011 Logit Scale: 30.129 Contrastive_loss: 0.054338 (0.040080) Loss: 0.054338 (0.040080) 2025-03-20,07:29:43 | INFO | Train Epoch: 25 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 151.068/s, 151.068/s/gpu LR: 0.000011 Logit Scale: 30.131 Contrastive_loss: 0.038129 (0.040063) Loss: 0.038129 (0.040063) 2025-03-20,07:30:05 | INFO | Train Epoch: 25 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.213, 150.918/s, 150.918/s/gpu LR: 0.000011 Logit Scale: 30.132 Contrastive_loss: 0.051876 (0.040166) Loss: 0.051876 (0.040166) 2025-03-20,07:30:26 | INFO | Train Epoch: 25 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 150.713/s, 150.713/s/gpu LR: 0.000011 Logit Scale: 30.136 Contrastive_loss: 0.0017845 (0.039833) Loss: 0.0017845 (0.039833) 2025-03-20,07:30:47 | INFO | Train Epoch: 25 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 151.023/s, 151.023/s/gpu LR: 0.000011 Logit Scale: 30.133 Contrastive_loss: 0.054441 (0.039959) Loss: 0.054441 (0.039959) 2025-03-20,07:31:09 | INFO | Train Epoch: 25 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.219, 144.340/s, 144.340/s/gpu LR: 0.000011 Logit Scale: 30.137 Contrastive_loss: 0.10652 (0.040528) Loss: 0.10652 (0.040528) 2025-03-20,07:31:31 | INFO | Train Epoch: 25 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.219, 146.708/s, 146.708/s/gpu LR: 0.000011 Logit Scale: 30.142 Contrastive_loss: 0.025905 (0.040404) Loss: 0.025905 (0.040404) 2025-03-20,07:31:53 | INFO | Train Epoch: 25 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.218, 147.944/s, 147.944/s/gpu LR: 0.000011 Logit Scale: 30.140 Contrastive_loss: 0.022009 (0.040249) Loss: 0.022009 (0.040249) 2025-03-20,07:32:15 | INFO | Train Epoch: 25 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.219, 144.515/s, 144.515/s/gpu LR: 0.000011 Logit Scale: 30.132 Contrastive_loss: 0.069113 (0.040490) Loss: 0.069113 (0.040490) 2025-03-20,07:32:37 | INFO | Train Epoch: 25 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.220, 147.466/s, 147.466/s/gpu LR: 0.000011 Logit Scale: 30.132 Contrastive_loss: 0.0012525 (0.040165) Loss: 0.0012525 (0.040165) 2025-03-20,07:32:58 | INFO | Train Epoch: 25 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 147.747/s, 147.747/s/gpu LR: 0.000011 Logit Scale: 30.133 Contrastive_loss: 0.00020931 (0.039838) Loss: 0.00020931 (0.039838) 2025-03-20,07:33:20 | INFO | Train Epoch: 25 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 151.605/s, 151.605/s/gpu LR: 0.000011 Logit Scale: 30.136 Contrastive_loss: 0.059665 (0.039999) Loss: 0.059665 (0.039999) 2025-03-20,07:33:41 | INFO | Train Epoch: 25 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.855/s, 149.855/s/gpu LR: 0.000011 Logit Scale: 30.135 Contrastive_loss: 0.022099 (0.039855) Loss: 0.022099 (0.039855) 2025-03-20,07:34:03 | INFO | Train Epoch: 25 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 149.792/s, 149.792/s/gpu LR: 0.000011 Logit Scale: 30.137 Contrastive_loss: 0.016140 (0.039665) Loss: 0.016140 (0.039665) 2025-03-20,07:34:24 | INFO | Train Epoch: 25 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 146.544/s, 146.544/s/gpu LR: 0.000011 Logit Scale: 30.139 Contrastive_loss: 0.023087 (0.039533) Loss: 0.023087 (0.039533) 2025-03-20,07:34:46 | INFO | Train Epoch: 25 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 146.918/s, 146.918/s/gpu LR: 0.000011 Logit Scale: 30.142 Contrastive_loss: 0.0049357 (0.039261) Loss: 0.0049357 (0.039261) 2025-03-20,07:35:08 | INFO | Train Epoch: 25 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 145.652/s, 145.652/s/gpu LR: 0.000011 Logit Scale: 30.140 Contrastive_loss: 0.00016953 (0.038956) Loss: 0.00016953 (0.038956) 2025-03-20,07:35:30 | INFO | Train Epoch: 25 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 149.097/s, 149.097/s/gpu LR: 0.000011 Logit Scale: 30.145 Contrastive_loss: 0.020043 (0.038809) Loss: 0.020043 (0.038809) 2025-03-20,07:35:51 | INFO | Train Epoch: 25 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 149.183/s, 149.183/s/gpu LR: 0.000011 Logit Scale: 30.139 Contrastive_loss: 0.10083 (0.039286) Loss: 0.10083 (0.039286) 2025-03-20,07:36:13 | INFO | Train Epoch: 25 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 149.345/s, 149.345/s/gpu LR: 0.000011 Logit Scale: 30.141 Contrastive_loss: 0.042387 (0.039310) Loss: 0.042387 (0.039310) 2025-03-20,07:36:34 | INFO | Train Epoch: 25 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 149.157/s, 149.157/s/gpu LR: 0.000011 Logit Scale: 30.138 Contrastive_loss: 0.0017993 (0.039025) Loss: 0.0017993 (0.039025) 2025-03-20,07:36:56 | INFO | Train Epoch: 25 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 149.393/s, 149.393/s/gpu LR: 0.000011 Logit Scale: 30.138 Contrastive_loss: 0.014314 (0.038840) Loss: 0.014314 (0.038840) 2025-03-20,07:37:17 | INFO | Train Epoch: 25 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 148.479/s, 148.479/s/gpu LR: 0.000011 Logit Scale: 30.139 Contrastive_loss: 0.077768 (0.039130) Loss: 0.077768 (0.039130) 2025-03-20,07:37:39 | INFO | Train Epoch: 25 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 147.586/s, 147.586/s/gpu LR: 0.000011 Logit Scale: 30.139 Contrastive_loss: 0.033733 (0.039090) Loss: 0.033733 (0.039090) 2025-03-20,07:38:00 | INFO | Train Epoch: 25 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 149.408/s, 149.408/s/gpu LR: 0.000011 Logit Scale: 30.144 Contrastive_loss: 0.0033127 (0.038827) Loss: 0.0033127 (0.038827) 2025-03-20,07:38:22 | INFO | Train Epoch: 25 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 147.361/s, 147.361/s/gpu LR: 0.000011 Logit Scale: 30.148 Contrastive_loss: 0.094338 (0.039232) Loss: 0.094338 (0.039232) 2025-03-20,07:38:44 | INFO | Train Epoch: 25 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.217, 148.657/s, 148.657/s/gpu LR: 0.000011 Logit Scale: 30.152 Contrastive_loss: 0.0019786 (0.038962) Loss: 0.0019786 (0.038962) 2025-03-20,07:39:05 | INFO | Train Epoch: 25 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.635/s, 148.635/s/gpu LR: 0.000011 Logit Scale: 30.153 Contrastive_loss: 0.085316 (0.039296) Loss: 0.085316 (0.039296) 2025-03-20,07:39:27 | INFO | Train Epoch: 25 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 150.248/s, 150.248/s/gpu LR: 0.000011 Logit Scale: 30.157 Contrastive_loss: 8.5389e-05 (0.039016) Loss: 8.5389e-05 (0.039016) 2025-03-20,07:39:48 | INFO | Train Epoch: 25 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.808/s, 148.808/s/gpu LR: 0.000011 Logit Scale: 30.157 Contrastive_loss: 0.055323 (0.039131) Loss: 0.055323 (0.039131) 2025-03-20,07:40:10 | INFO | Train Epoch: 25 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 148.125/s, 148.125/s/gpu LR: 0.000011 Logit Scale: 30.161 Contrastive_loss: 0.039042 (0.039131) Loss: 0.039042 (0.039131) 2025-03-20,07:40:31 | INFO | Train Epoch: 25 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 148.788/s, 148.788/s/gpu LR: 0.000011 Logit Scale: 30.160 Contrastive_loss: 0.20352 (0.040280) Loss: 0.20352 (0.040280) 2025-03-20,07:40:53 | INFO | Train Epoch: 25 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 146.370/s, 146.370/s/gpu LR: 0.000011 Logit Scale: 30.159 Contrastive_loss: 0.045643 (0.040318) Loss: 0.045643 (0.040318) 2025-03-20,07:41:15 | INFO | Train Epoch: 25 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 150.559/s, 150.559/s/gpu LR: 0.000011 Logit Scale: 30.161 Contrastive_loss: 0.075279 (0.040559) Loss: 0.075279 (0.040559) 2025-03-20,07:41:36 | INFO | Train Epoch: 25 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.217, 148.283/s, 148.283/s/gpu LR: 0.000011 Logit Scale: 30.165 Contrastive_loss: 0.12212 (0.041117) Loss: 0.12212 (0.041117) 2025-03-20,07:41:58 | INFO | Train Epoch: 25 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 148.066/s, 148.066/s/gpu LR: 0.000011 Logit Scale: 30.167 Contrastive_loss: 0.13407 (0.041750) Loss: 0.13407 (0.041750) 2025-03-20,07:42:19 | INFO | Train Epoch: 25 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.213, 151.322/s, 151.322/s/gpu LR: 0.000011 Logit Scale: 30.164 Contrastive_loss: 0.00076240 (0.041473) Loss: 0.00076240 (0.041473) 2025-03-20,07:42:40 | INFO | Train Epoch: 25 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.213, 149.218/s, 149.218/s/gpu LR: 0.000011 Logit Scale: 30.170 Contrastive_loss: 0.018147 (0.041316) Loss: 0.018147 (0.041316) 2025-03-20,07:43:02 | INFO | Train Epoch: 25 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.099/s, 149.099/s/gpu LR: 0.000011 Logit Scale: 30.172 Contrastive_loss: 0.063408 (0.041464) Loss: 0.063408 (0.041464) 2025-03-20,07:43:23 | INFO | Train Epoch: 25 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.213, 149.711/s, 149.711/s/gpu LR: 0.000011 Logit Scale: 30.178 Contrastive_loss: 0.024823 (0.041353) Loss: 0.024823 (0.041353) 2025-03-20,07:43:45 | INFO | Train Epoch: 25 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.544/s, 148.544/s/gpu LR: 0.000011 Logit Scale: 30.175 Contrastive_loss: 0.0071285 (0.041128) Loss: 0.0071285 (0.041128) 2025-03-20,07:44:06 | INFO | Train Epoch: 25 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 147.652/s, 147.652/s/gpu LR: 0.000011 Logit Scale: 30.177 Contrastive_loss: 8.9020e-05 (0.040860) Loss: 8.9020e-05 (0.040860) 2025-03-20,07:44:28 | INFO | Train Epoch: 25 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.221, 146.149/s, 146.149/s/gpu LR: 0.000011 Logit Scale: 30.174 Contrastive_loss: 0.012809 (0.040678) Loss: 0.012809 (0.040678) 2025-03-20,07:44:50 | INFO | Train Epoch: 25 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.221, 144.769/s, 144.769/s/gpu LR: 0.000011 Logit Scale: 30.181 Contrastive_loss: 0.012325 (0.040495) Loss: 0.012325 (0.040495) 2025-03-20,07:45:13 | INFO | Train Epoch: 25 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.224, 144.233/s, 144.233/s/gpu LR: 0.000010 Logit Scale: 30.182 Contrastive_loss: 0.049043 (0.040550) Loss: 0.049043 (0.040550) 2025-03-20,07:45:35 | INFO | Train Epoch: 25 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.222, 145.576/s, 145.576/s/gpu LR: 0.000010 Logit Scale: 30.183 Contrastive_loss: 0.0020499 (0.040304) Loss: 0.0020499 (0.040304) 2025-03-20,07:45:57 | INFO | Train Epoch: 25 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.218, 150.903/s, 150.903/s/gpu LR: 0.000010 Logit Scale: 30.188 Contrastive_loss: 0.089372 (0.040615) Loss: 0.089372 (0.040615) 2025-03-20,07:46:18 | INFO | Train Epoch: 25 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.028/s, 150.028/s/gpu LR: 0.000010 Logit Scale: 30.186 Contrastive_loss: 0.0017046 (0.040370) Loss: 0.0017046 (0.040370) 2025-03-20,07:46:39 | INFO | Train Epoch: 25 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.998/s, 149.998/s/gpu LR: 0.000010 Logit Scale: 30.182 Contrastive_loss: 4.9978e-05 (0.040118) Loss: 4.9978e-05 (0.040118) 2025-03-20,07:47:01 | INFO | Train Epoch: 25 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.213, 148.730/s, 148.730/s/gpu LR: 0.000010 Logit Scale: 30.182 Contrastive_loss: 0.22097 (0.041242) Loss: 0.22097 (0.041242) 2025-03-20,07:47:22 | INFO | Train Epoch: 25 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 151.622/s, 151.622/s/gpu LR: 0.000010 Logit Scale: 30.186 Contrastive_loss: 0.019580 (0.041108) Loss: 0.019580 (0.041108) 2025-03-20,07:47:44 | INFO | Train Epoch: 25 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.215, 147.270/s, 147.270/s/gpu LR: 0.000010 Logit Scale: 30.187 Contrastive_loss: 0.019011 (0.040972) Loss: 0.019011 (0.040972) 2025-03-20,07:48:05 | INFO | Train Epoch: 25 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 148.544/s, 148.544/s/gpu LR: 0.000010 Logit Scale: 30.193 Contrastive_loss: 0.031589 (0.040915) Loss: 0.031589 (0.040915) 2025-03-20,07:48:27 | INFO | Train Epoch: 25 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 148.408/s, 148.408/s/gpu LR: 0.000010 Logit Scale: 30.192 Contrastive_loss: 0.13209 (0.041468) Loss: 0.13209 (0.041468) 2025-03-20,07:48:49 | INFO | Train Epoch: 25 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 148.244/s, 148.244/s/gpu LR: 0.000010 Logit Scale: 30.200 Contrastive_loss: 0.016352 (0.041316) Loss: 0.016352 (0.041316) 2025-03-20,07:49:10 | INFO | Train Epoch: 25 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 149.898/s, 149.898/s/gpu LR: 0.000010 Logit Scale: 30.202 Contrastive_loss: 0.082914 (0.041565) Loss: 0.082914 (0.041565) 2025-03-20,07:49:32 | INFO | Train Epoch: 25 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.214, 149.762/s, 149.762/s/gpu LR: 0.000010 Logit Scale: 30.203 Contrastive_loss: 4.5277e-05 (0.041318) Loss: 4.5277e-05 (0.041318) 2025-03-20,07:49:53 | INFO | Train Epoch: 25 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 146.567/s, 146.567/s/gpu LR: 0.000010 Logit Scale: 30.205 Contrastive_loss: 0.061239 (0.041436) Loss: 0.061239 (0.041436) 2025-03-20,07:50:15 | INFO | Train Epoch: 25 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 149.661/s, 149.661/s/gpu LR: 0.000010 Logit Scale: 30.210 Contrastive_loss: 0.00056359 (0.041196) Loss: 0.00056359 (0.041196) 2025-03-20,07:50:36 | INFO | Train Epoch: 25 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.213, 150.085/s, 150.085/s/gpu LR: 0.000010 Logit Scale: 30.209 Contrastive_loss: 0.029006 (0.041124) Loss: 0.029006 (0.041124) 2025-03-20,07:50:57 | INFO | Train Epoch: 25 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.213, 150.284/s, 150.284/s/gpu LR: 0.000010 Logit Scale: 30.209 Contrastive_loss: 0.0073602 (0.040928) Loss: 0.0073602 (0.040928) 2025-03-20,07:51:19 | INFO | Train Epoch: 25 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 150.947/s, 150.947/s/gpu LR: 0.000010 Logit Scale: 30.213 Contrastive_loss: 0.046193 (0.040959) Loss: 0.046193 (0.040959) 2025-03-20,07:51:40 | INFO | Train Epoch: 25 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.213, 151.253/s, 151.253/s/gpu LR: 0.000010 Logit Scale: 30.214 Contrastive_loss: 0.10145 (0.041306) Loss: 0.10145 (0.041306) 2025-03-20,07:52:01 | INFO | Train Epoch: 25 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.213, 151.104/s, 151.104/s/gpu LR: 0.000010 Logit Scale: 30.213 Contrastive_loss: 0.00037836 (0.041072) Loss: 0.00037836 (0.041072) 2025-03-20,07:52:23 | INFO | Train Epoch: 25 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.213, 149.671/s, 149.671/s/gpu LR: 0.000010 Logit Scale: 30.214 Contrastive_loss: 0.0032976 (0.040858) Loss: 0.0032976 (0.040858) 2025-03-20,07:52:44 | INFO | Train Epoch: 25 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.213, 152.414/s, 152.414/s/gpu LR: 0.000010 Logit Scale: 30.218 Contrastive_loss: 0.049604 (0.040907) Loss: 0.049604 (0.040907) 2025-03-20,07:53:05 | INFO | Train Epoch: 25 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.213, 151.546/s, 151.546/s/gpu LR: 0.000010 Logit Scale: 30.218 Contrastive_loss: 0.056377 (0.040994) Loss: 0.056377 (0.040994) 2025-03-20,07:53:27 | INFO | Train Epoch: 25 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.214, 148.374/s, 148.374/s/gpu LR: 0.000010 Logit Scale: 30.219 Contrastive_loss: 0.054892 (0.041072) Loss: 0.054892 (0.041072) 2025-03-20,07:53:48 | INFO | Train Epoch: 25 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.214, 146.967/s, 146.967/s/gpu LR: 0.000010 Logit Scale: 30.221 Contrastive_loss: 0.0081385 (0.040889) Loss: 0.0081385 (0.040889) 2025-03-20,07:54:10 | INFO | Train Epoch: 25 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 151.373/s, 151.373/s/gpu LR: 0.000010 Logit Scale: 30.219 Contrastive_loss: 0.00024062 (0.040664) Loss: 0.00024062 (0.040664) 2025-03-20,07:54:31 | INFO | Train Epoch: 25 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 147.899/s, 147.899/s/gpu LR: 0.000010 Logit Scale: 30.222 Contrastive_loss: 0.00093200 (0.040446) Loss: 0.00093200 (0.040446) 2025-03-20,07:54:53 | INFO | Train Epoch: 25 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 146.541/s, 146.541/s/gpu LR: 0.000010 Logit Scale: 30.223 Contrastive_loss: 0.0080738 (0.040269) Loss: 0.0080738 (0.040269) 2025-03-20,07:55:15 | INFO | Train Epoch: 25 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 150.222/s, 150.222/s/gpu LR: 0.000010 Logit Scale: 30.221 Contrastive_loss: 0.00083920 (0.040055) Loss: 0.00083920 (0.040055) 2025-03-20,07:55:36 | INFO | Train Epoch: 25 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.213, 151.334/s, 151.334/s/gpu LR: 0.000010 Logit Scale: 30.224 Contrastive_loss: 0.013337 (0.039910) Loss: 0.013337 (0.039910) 2025-03-20,07:55:57 | INFO | Train Epoch: 25 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.213, 151.646/s, 151.646/s/gpu LR: 0.000010 Logit Scale: 30.225 Contrastive_loss: 0.074977 (0.040099) Loss: 0.074977 (0.040099) 2025-03-20,07:56:18 | INFO | Train Epoch: 25 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.213, 147.824/s, 147.824/s/gpu LR: 0.000010 Logit Scale: 30.226 Contrastive_loss: 0.10367 (0.040439) Loss: 0.10367 (0.040439) 2025-03-20,07:56:40 | INFO | Train Epoch: 25 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 150.217/s, 150.217/s/gpu LR: 0.000010 Logit Scale: 30.224 Contrastive_loss: 0.14845 (0.041013) Loss: 0.14845 (0.041013) 2025-03-20,07:57:01 | INFO | Train Epoch: 25 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 151.639/s, 151.639/s/gpu LR: 0.000010 Logit Scale: 30.220 Contrastive_loss: 0.028638 (0.040948) Loss: 0.028638 (0.040948) 2025-03-20,07:57:23 | INFO | Train Epoch: 25 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 141.435/s, 141.435/s/gpu LR: 0.000010 Logit Scale: 30.224 Contrastive_loss: 0.048191 (0.040986) Loss: 0.048191 (0.040986) 2025-03-20,07:57:44 | INFO | Train Epoch: 25 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 151.049/s, 151.049/s/gpu LR: 0.000010 Logit Scale: 30.228 Contrastive_loss: 0.047713 (0.041021) Loss: 0.047713 (0.041021) 2025-03-20,07:58:05 | INFO | Train Epoch: 25 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.213, 151.620/s, 151.620/s/gpu LR: 0.000010 Logit Scale: 30.227 Contrastive_loss: 0.0012210 (0.040814) Loss: 0.0012210 (0.040814) 2025-03-20,07:58:27 | INFO | Train Epoch: 25 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 146.675/s, 146.675/s/gpu LR: 0.000010 Logit Scale: 30.228 Contrastive_loss: 0.00016652 (0.040603) Loss: 0.00016652 (0.040603) 2025-03-20,07:58:49 | INFO | Train Epoch: 25 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 143.772/s, 143.772/s/gpu LR: 0.000010 Logit Scale: 30.232 Contrastive_loss: 0.00058091 (0.040397) Loss: 0.00058091 (0.040397) 2025-03-20,07:59:11 | INFO | Train Epoch: 25 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.219, 146.766/s, 146.766/s/gpu LR: 0.000010 Logit Scale: 30.233 Contrastive_loss: 0.12741 (0.040843) Loss: 0.12741 (0.040843) 2025-03-20,07:59:33 | INFO | Train Epoch: 25 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.218, 146.478/s, 146.478/s/gpu LR: 0.000010 Logit Scale: 30.229 Contrastive_loss: 0.087787 (0.041083) Loss: 0.087787 (0.041083) 2025-03-20,07:59:55 | INFO | Train Epoch: 25 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.220, 147.366/s, 147.366/s/gpu LR: 0.000010 Logit Scale: 30.230 Contrastive_loss: 0.052742 (0.041142) Loss: 0.052742 (0.041142) 2025-03-20,08:00:17 | INFO | Train Epoch: 25 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.217, 145.333/s, 145.333/s/gpu LR: 0.000010 Logit Scale: 30.232 Contrastive_loss: 0.14989 (0.041691) Loss: 0.14989 (0.041691) 2025-03-20,08:00:39 | INFO | Train Epoch: 25 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.221, 148.602/s, 148.602/s/gpu LR: 0.000010 Logit Scale: 30.237 Contrastive_loss: 0.0032896 (0.041498) Loss: 0.0032896 (0.041498) 2025-03-20,08:01:00 | INFO | Train Epoch: 25 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.212, 151.233/s, 151.233/s/gpu LR: 0.000010 Logit Scale: 30.237 Contrastive_loss: 0.043310 (0.041507) Loss: 0.043310 (0.041507) 2025-03-20,08:01:21 | INFO | Train Epoch: 25 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 147.424/s, 147.424/s/gpu LR: 0.000010 Logit Scale: 30.241 Contrastive_loss: 0.00014897 (0.041301) Loss: 0.00014897 (0.041301) 2025-03-20,08:01:43 | INFO | Train Epoch: 25 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 149.008/s, 149.008/s/gpu LR: 0.000010 Logit Scale: 30.244 Contrastive_loss: 0.024942 (0.041220) Loss: 0.024942 (0.041220) 2025-03-20,08:02:04 | INFO | Train Epoch: 25 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 149.330/s, 149.330/s/gpu LR: 0.000010 Logit Scale: 30.248 Contrastive_loss: 0.057208 (0.041299) Loss: 0.057208 (0.041299) 2025-03-20,08:02:26 | INFO | Train Epoch: 25 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.217, 145.553/s, 145.553/s/gpu LR: 0.000010 Logit Scale: 30.249 Contrastive_loss: 0.020156 (0.041196) Loss: 0.020156 (0.041196) 2025-03-20,08:02:48 | INFO | Train Epoch: 25 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 149.923/s, 149.923/s/gpu LR: 0.000010 Logit Scale: 30.251 Contrastive_loss: 0.00021548 (0.040996) Loss: 0.00021548 (0.040996) 2025-03-20,08:03:10 | INFO | Train Epoch: 25 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 147.692/s, 147.692/s/gpu LR: 0.000010 Logit Scale: 30.251 Contrastive_loss: 0.025786 (0.040922) Loss: 0.025786 (0.040922) 2025-03-20,08:03:32 | INFO | Train Epoch: 25 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.221, 147.184/s, 147.184/s/gpu LR: 0.000010 Logit Scale: 30.253 Contrastive_loss: 0.047258 (0.040952) Loss: 0.047258 (0.040952) 2025-03-20,08:03:54 | INFO | Train Epoch: 25 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.220, 147.211/s, 147.211/s/gpu LR: 0.000009 Logit Scale: 30.253 Contrastive_loss: 0.079384 (0.041137) Loss: 0.079384 (0.041137) 2025-03-20,08:04:15 | INFO | Train Epoch: 25 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 147.717/s, 147.717/s/gpu LR: 0.000009 Logit Scale: 30.253 Contrastive_loss: 0.066576 (0.041259) Loss: 0.066576 (0.041259) 2025-03-20,08:04:37 | INFO | Train Epoch: 25 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 149.475/s, 149.475/s/gpu LR: 0.000009 Logit Scale: 30.251 Contrastive_loss: 0.10568 (0.041566) Loss: 0.10568 (0.041566) 2025-03-20,08:04:58 | INFO | Train Epoch: 25 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 144.278/s, 144.278/s/gpu LR: 0.000009 Logit Scale: 30.252 Contrastive_loss: 0.0051998 (0.041393) Loss: 0.0051998 (0.041393) 2025-03-20,08:05:20 | INFO | Train Epoch: 25 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.219, 148.111/s, 148.111/s/gpu LR: 0.000009 Logit Scale: 30.252 Contrastive_loss: 0.037999 (0.041377) Loss: 0.037999 (0.041377) 2025-03-20,08:05:42 | INFO | Train Epoch: 25 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 151.359/s, 151.359/s/gpu LR: 0.000009 Logit Scale: 30.252 Contrastive_loss: 0.00082868 (0.041187) Loss: 0.00082868 (0.041187) 2025-03-20,08:06:03 | INFO | Train Epoch: 25 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.212, 150.915/s, 150.915/s/gpu LR: 0.000009 Logit Scale: 30.256 Contrastive_loss: 0.061024 (0.041280) Loss: 0.061024 (0.041280) 2025-03-20,08:06:24 | INFO | Train Epoch: 25 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.615/s, 148.615/s/gpu LR: 0.000009 Logit Scale: 30.258 Contrastive_loss: 0.18212 (0.041935) Loss: 0.18212 (0.041935) 2025-03-20,08:06:46 | INFO | Train Epoch: 25 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.217, 145.782/s, 145.782/s/gpu LR: 0.000009 Logit Scale: 30.255 Contrastive_loss: 0.00011790 (0.041741) Loss: 0.00011790 (0.041741) 2025-03-20,08:07:07 | INFO | Train Epoch: 25 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 150.358/s, 150.358/s/gpu LR: 0.000009 Logit Scale: 30.257 Contrastive_loss: 0.028910 (0.041682) Loss: 0.028910 (0.041682) 2025-03-20,08:07:29 | INFO | Train Epoch: 25 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 150.649/s, 150.649/s/gpu LR: 0.000009 Logit Scale: 30.264 Contrastive_loss: 0.015803 (0.041563) Loss: 0.015803 (0.041563) 2025-03-20,08:07:50 | INFO | Train Epoch: 25 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.213, 151.594/s, 151.594/s/gpu LR: 0.000009 Logit Scale: 30.267 Contrastive_loss: 0.11452 (0.041896) Loss: 0.11452 (0.041896) 2025-03-20,08:08:11 | INFO | Train Epoch: 25 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.212, 152.250/s, 152.250/s/gpu LR: 0.000009 Logit Scale: 30.269 Contrastive_loss: 0.0013822 (0.041712) Loss: 0.0013822 (0.041712) 2025-03-20,08:08:33 | INFO | Train Epoch: 25 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.212, 150.711/s, 150.711/s/gpu LR: 0.000009 Logit Scale: 30.266 Contrastive_loss: 0.015760 (0.041595) Loss: 0.015760 (0.041595) 2025-03-20,08:08:54 | INFO | Train Epoch: 25 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 143.361/s, 143.361/s/gpu LR: 0.000009 Logit Scale: 30.269 Contrastive_loss: 0.00080564 (0.041411) Loss: 0.00080564 (0.041411) 2025-03-20,08:09:15 | INFO | Train Epoch: 25 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.213, 148.473/s, 148.473/s/gpu LR: 0.000009 Logit Scale: 30.273 Contrastive_loss: 0.055743 (0.041475) Loss: 0.055743 (0.041475) 2025-03-20,08:09:36 | INFO | Train Epoch: 25 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.212, 150.674/s, 150.674/s/gpu LR: 0.000009 Logit Scale: 30.274 Contrastive_loss: 0.17000 (0.042049) Loss: 0.17000 (0.042049) 2025-03-20,08:09:58 | INFO | Train Epoch: 25 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 151.211/s, 151.211/s/gpu LR: 0.000009 Logit Scale: 30.274 Contrastive_loss: 0.030251 (0.041997) Loss: 0.030251 (0.041997) 2025-03-20,08:10:19 | INFO | Train Epoch: 25 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 149.641/s, 149.641/s/gpu LR: 0.000009 Logit Scale: 30.282 Contrastive_loss: 0.00048124 (0.041813) Loss: 0.00048124 (0.041813) 2025-03-20,08:10:41 | INFO | Train Epoch: 25 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 149.556/s, 149.556/s/gpu LR: 0.000009 Logit Scale: 30.284 Contrastive_loss: 0.056427 (0.041877) Loss: 0.056427 (0.041877) 2025-03-20,08:11:02 | INFO | Train Epoch: 25 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 150.185/s, 150.185/s/gpu LR: 0.000009 Logit Scale: 30.288 Contrastive_loss: 0.0017422 (0.041701) Loss: 0.0017422 (0.041701) 2025-03-20,08:11:24 | INFO | Train Epoch: 25 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.218, 143.873/s, 143.873/s/gpu LR: 0.000009 Logit Scale: 30.288 Contrastive_loss: 0.00054931 (0.041522) Loss: 0.00054931 (0.041522) 2025-03-20,08:11:46 | INFO | Train Epoch: 25 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 147.845/s, 147.845/s/gpu LR: 0.000009 Logit Scale: 30.284 Contrastive_loss: 0.096496 (0.041761) Loss: 0.096496 (0.041761) 2025-03-20,08:12:07 | INFO | Train Epoch: 25 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 149.432/s, 149.432/s/gpu LR: 0.000009 Logit Scale: 30.285 Contrastive_loss: 0.0021771 (0.041589) Loss: 0.0021771 (0.041589) 2025-03-20,08:12:29 | INFO | Train Epoch: 25 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.158/s, 149.158/s/gpu LR: 0.000009 Logit Scale: 30.284 Contrastive_loss: 0.076855 (0.041741) Loss: 0.076855 (0.041741) 2025-03-20,08:12:50 | INFO | Train Epoch: 25 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 147.613/s, 147.613/s/gpu LR: 0.000009 Logit Scale: 30.281 Contrastive_loss: 0.00041911 (0.041564) Loss: 0.00041911 (0.041564) 2025-03-20,08:13:12 | INFO | Train Epoch: 25 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.219, 149.295/s, 149.295/s/gpu LR: 0.000009 Logit Scale: 30.282 Contrastive_loss: 0.068081 (0.041677) Loss: 0.068081 (0.041677) 2025-03-20,08:13:34 | INFO | Train Epoch: 25 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 148.268/s, 148.268/s/gpu LR: 0.000009 Logit Scale: 30.284 Contrastive_loss: 0.0032806 (0.041514) Loss: 0.0032806 (0.041514) 2025-03-20,08:13:55 | INFO | Train Epoch: 25 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 148.662/s, 148.662/s/gpu LR: 0.000009 Logit Scale: 30.287 Contrastive_loss: 0.029641 (0.041464) Loss: 0.029641 (0.041464) 2025-03-20,08:14:17 | INFO | Train Epoch: 25 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 149.430/s, 149.430/s/gpu LR: 0.000009 Logit Scale: 30.286 Contrastive_loss: 0.043838 (0.041474) Loss: 0.043838 (0.041474) 2025-03-20,08:14:38 | INFO | Train Epoch: 25 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.571/s, 149.571/s/gpu LR: 0.000009 Logit Scale: 30.287 Contrastive_loss: 0.012156 (0.041350) Loss: 0.012156 (0.041350) 2025-03-20,08:14:59 | INFO | Train Epoch: 25 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 148.080/s, 148.080/s/gpu LR: 0.000009 Logit Scale: 30.286 Contrastive_loss: 0.097560 (0.041586) Loss: 0.097560 (0.041586) 2025-03-20,08:15:21 | INFO | Train Epoch: 25 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.213, 151.471/s, 151.471/s/gpu LR: 0.000009 Logit Scale: 30.290 Contrastive_loss: 0.015737 (0.041478) Loss: 0.015737 (0.041478) 2025-03-20,08:15:28 | INFO | Train Epoch: 25 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.213, 150.793/s, 150.793/s/gpu LR: 0.000009 Logit Scale: 30.290 Contrastive_loss: 0.0011697 (0.041311) Loss: 0.0011697 (0.041311) 2025-03-20,08:15:29 | INFO | Eval Epoch: 26 [32 / 7443] Clip Loss: 4.046157 2025-03-20,08:15:34 | INFO | Eval Epoch: 26 [3232 / 7443] Clip Loss: 0.851843 2025-03-20,08:15:40 | INFO | Eval Epoch: 26 [6432 / 7443] Clip Loss: 0.632490 2025-03-20,08:15:43 | INFO | Eval Epoch: 26 image_to_text_mean_rank: 79.4115 image_to_text_median_rank: 4.0000 image_to_text_R@1: 0.2010 image_to_text_R@5: 0.5603 image_to_text_R@10: 0.7255 text_to_image_mean_rank: 48.7193 text_to_image_median_rank: 5.0000 text_to_image_R@1: 0.2049 text_to_image_R@5: 0.5538 text_to_image_R@10: 0.7129 clip_val_loss: 0.5864 epoch: 26.0000 num_samples: 7443.0000 2025-03-20,08:16:15 | INFO | Start epoch 26 2025-03-20,08:16:15 | INFO | Train Epoch: 26 [ 32/766009 (0%)] Data (t): 0.176 Batch (t): 0.384, 83.2428/s, 83.2428/s/gpu LR: 0.000009 Logit Scale: 30.290 Contrastive_loss: 0.0026919 (0.0026919) Loss: 0.0026919 (0.0026919) 2025-03-20,08:16:37 | INFO | Train Epoch: 26 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.220, 148.345/s, 148.345/s/gpu LR: 0.000009 Logit Scale: 30.293 Contrastive_loss: 0.0012305 (0.0019612) Loss: 0.0012305 (0.0019612) 2025-03-20,08:16:59 | INFO | Train Epoch: 26 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.218, 147.005/s, 147.005/s/gpu LR: 0.000009 Logit Scale: 30.296 Contrastive_loss: 0.081997 (0.028640) Loss: 0.081997 (0.028640) 2025-03-20,08:17:21 | INFO | Train Epoch: 26 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.218, 145.485/s, 145.485/s/gpu LR: 0.000009 Logit Scale: 30.299 Contrastive_loss: 0.043745 (0.032416) Loss: 0.043745 (0.032416) 2025-03-20,08:17:43 | INFO | Train Epoch: 26 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.219, 146.627/s, 146.627/s/gpu LR: 0.000009 Logit Scale: 30.301 Contrastive_loss: 0.013190 (0.028571) Loss: 0.013190 (0.028571) 2025-03-20,08:18:05 | INFO | Train Epoch: 26 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.217, 147.473/s, 147.473/s/gpu LR: 0.000009 Logit Scale: 30.302 Contrastive_loss: 0.041236 (0.030682) Loss: 0.041236 (0.030682) 2025-03-20,08:18:26 | INFO | Train Epoch: 26 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 146.013/s, 146.013/s/gpu LR: 0.000009 Logit Scale: 30.303 Contrastive_loss: 0.028057 (0.030307) Loss: 0.028057 (0.030307) 2025-03-20,08:18:48 | INFO | Train Epoch: 26 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 147.788/s, 147.788/s/gpu LR: 0.000009 Logit Scale: 30.307 Contrastive_loss: 0.0046536 (0.027100) Loss: 0.0046536 (0.027100) 2025-03-20,08:19:09 | INFO | Train Epoch: 26 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.216, 149.094/s, 149.094/s/gpu LR: 0.000009 Logit Scale: 30.307 Contrastive_loss: 0.10343 (0.035581) Loss: 0.10343 (0.035581) 2025-03-20,08:19:31 | INFO | Train Epoch: 26 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.217, 149.936/s, 149.936/s/gpu LR: 0.000009 Logit Scale: 30.310 Contrastive_loss: 0.021890 (0.034212) Loss: 0.021890 (0.034212) 2025-03-20,08:19:53 | INFO | Train Epoch: 26 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.218, 150.880/s, 150.880/s/gpu LR: 0.000009 Logit Scale: 30.308 Contrastive_loss: 0.0010306 (0.031196) Loss: 0.0010306 (0.031196) 2025-03-20,08:20:15 | INFO | Train Epoch: 26 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.218, 149.966/s, 149.966/s/gpu LR: 0.000009 Logit Scale: 30.305 Contrastive_loss: 0.10267 (0.037152) Loss: 0.10267 (0.037152) 2025-03-20,08:20:36 | INFO | Train Epoch: 26 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 150.237/s, 150.237/s/gpu LR: 0.000009 Logit Scale: 30.308 Contrastive_loss: 0.011349 (0.035167) Loss: 0.011349 (0.035167) 2025-03-20,08:20:58 | INFO | Train Epoch: 26 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.220, 142.211/s, 142.211/s/gpu LR: 0.000009 Logit Scale: 30.310 Contrastive_loss: 0.099470 (0.039760) Loss: 0.099470 (0.039760) 2025-03-20,08:21:21 | INFO | Train Epoch: 26 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.223, 141.956/s, 141.956/s/gpu LR: 0.000009 Logit Scale: 30.309 Contrastive_loss: 0.021603 (0.038550) Loss: 0.021603 (0.038550) 2025-03-20,08:21:43 | INFO | Train Epoch: 26 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.220, 146.528/s, 146.528/s/gpu LR: 0.000009 Logit Scale: 30.316 Contrastive_loss: 0.010520 (0.036798) Loss: 0.010520 (0.036798) 2025-03-20,08:22:04 | INFO | Train Epoch: 26 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 149.486/s, 149.486/s/gpu LR: 0.000009 Logit Scale: 30.317 Contrastive_loss: 0.044309 (0.037240) Loss: 0.044309 (0.037240) 2025-03-20,08:22:26 | INFO | Train Epoch: 26 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.214, 149.126/s, 149.126/s/gpu LR: 0.000009 Logit Scale: 30.317 Contrastive_loss: 0.0022996 (0.035299) Loss: 0.0022996 (0.035299) 2025-03-20,08:22:48 | INFO | Train Epoch: 26 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.438/s, 149.438/s/gpu LR: 0.000009 Logit Scale: 30.317 Contrastive_loss: 0.050608 (0.036104) Loss: 0.050608 (0.036104) 2025-03-20,08:23:09 | INFO | Train Epoch: 26 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.927/s, 149.927/s/gpu LR: 0.000009 Logit Scale: 30.323 Contrastive_loss: 0.0012808 (0.034363) Loss: 0.0012808 (0.034363) 2025-03-20,08:23:30 | INFO | Train Epoch: 26 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 150.326/s, 150.326/s/gpu LR: 0.000009 Logit Scale: 30.326 Contrastive_loss: 0.0024917 (0.032846) Loss: 0.0024917 (0.032846) 2025-03-20,08:23:52 | INFO | Train Epoch: 26 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.214, 149.196/s, 149.196/s/gpu LR: 0.000009 Logit Scale: 30.328 Contrastive_loss: 0.0013954 (0.031416) Loss: 0.0013954 (0.031416) 2025-03-20,08:24:13 | INFO | Train Epoch: 26 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.215, 149.181/s, 149.181/s/gpu LR: 0.000008 Logit Scale: 30.330 Contrastive_loss: 0.076622 (0.033381) Loss: 0.076622 (0.033381) 2025-03-20,08:24:35 | INFO | Train Epoch: 26 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 148.279/s, 148.279/s/gpu LR: 0.000008 Logit Scale: 30.332 Contrastive_loss: 0.0057265 (0.032229) Loss: 0.0057265 (0.032229) 2025-03-20,08:24:56 | INFO | Train Epoch: 26 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.215, 148.300/s, 148.300/s/gpu LR: 0.000008 Logit Scale: 30.336 Contrastive_loss: 0.093342 (0.034674) Loss: 0.093342 (0.034674) 2025-03-20,08:25:18 | INFO | Train Epoch: 26 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.216, 147.726/s, 147.726/s/gpu LR: 0.000008 Logit Scale: 30.339 Contrastive_loss: 0.036228 (0.034733) Loss: 0.036228 (0.034733) 2025-03-20,08:25:40 | INFO | Train Epoch: 26 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 146.016/s, 146.016/s/gpu LR: 0.000008 Logit Scale: 30.344 Contrastive_loss: 0.061367 (0.035720) Loss: 0.061367 (0.035720) 2025-03-20,08:26:01 | INFO | Train Epoch: 26 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 145.723/s, 145.723/s/gpu LR: 0.000008 Logit Scale: 30.348 Contrastive_loss: 0.00056514 (0.034464) Loss: 0.00056514 (0.034464) 2025-03-20,08:26:23 | INFO | Train Epoch: 26 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.216, 147.769/s, 147.769/s/gpu LR: 0.000008 Logit Scale: 30.354 Contrastive_loss: 0.11942 (0.037394) Loss: 0.11942 (0.037394) 2025-03-20,08:26:44 | INFO | Train Epoch: 26 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 148.715/s, 148.715/s/gpu LR: 0.000008 Logit Scale: 30.357 Contrastive_loss: 0.046356 (0.037693) Loss: 0.046356 (0.037693) 2025-03-20,08:27:06 | INFO | Train Epoch: 26 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.165/s, 148.165/s/gpu LR: 0.000008 Logit Scale: 30.361 Contrastive_loss: 0.00099239 (0.036509) Loss: 0.00099239 (0.036509) 2025-03-20,08:27:28 | INFO | Train Epoch: 26 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 146.720/s, 146.720/s/gpu LR: 0.000008 Logit Scale: 30.361 Contrastive_loss: 0.019685 (0.035983) Loss: 0.019685 (0.035983) 2025-03-20,08:27:49 | INFO | Train Epoch: 26 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.217, 146.280/s, 146.280/s/gpu LR: 0.000008 Logit Scale: 30.366 Contrastive_loss: 0.097887 (0.037859) Loss: 0.097887 (0.037859) 2025-03-20,08:28:11 | INFO | Train Epoch: 26 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.221, 146.139/s, 146.139/s/gpu LR: 0.000008 Logit Scale: 30.367 Contrastive_loss: 0.013269 (0.037136) Loss: 0.013269 (0.037136) 2025-03-20,08:28:34 | INFO | Train Epoch: 26 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.223, 147.282/s, 147.282/s/gpu LR: 0.000008 Logit Scale: 30.369 Contrastive_loss: 0.060718 (0.037809) Loss: 0.060718 (0.037809) 2025-03-20,08:28:55 | INFO | Train Epoch: 26 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.214, 147.019/s, 147.019/s/gpu LR: 0.000008 Logit Scale: 30.370 Contrastive_loss: 0.041082 (0.037900) Loss: 0.041082 (0.037900) 2025-03-20,08:29:17 | INFO | Train Epoch: 26 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.221, 147.030/s, 147.030/s/gpu LR: 0.000008 Logit Scale: 30.371 Contrastive_loss: 0.043889 (0.038062) Loss: 0.043889 (0.038062) 2025-03-20,08:29:39 | INFO | Train Epoch: 26 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 148.691/s, 148.691/s/gpu LR: 0.000008 Logit Scale: 30.369 Contrastive_loss: 0.069371 (0.038886) Loss: 0.069371 (0.038886) 2025-03-20,08:30:01 | INFO | Train Epoch: 26 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.217, 146.691/s, 146.691/s/gpu LR: 0.000008 Logit Scale: 30.370 Contrastive_loss: 0.11087 (0.040732) Loss: 0.11087 (0.040732) 2025-03-20,08:30:22 | INFO | Train Epoch: 26 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.219, 145.429/s, 145.429/s/gpu LR: 0.000008 Logit Scale: 30.373 Contrastive_loss: 0.0050207 (0.039839) Loss: 0.0050207 (0.039839) 2025-03-20,08:30:44 | INFO | Train Epoch: 26 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 151.439/s, 151.439/s/gpu LR: 0.000008 Logit Scale: 30.372 Contrastive_loss: 0.00023004 (0.038873) Loss: 0.00023004 (0.038873) 2025-03-20,08:31:06 | INFO | Train Epoch: 26 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.219, 147.029/s, 147.029/s/gpu LR: 0.000008 Logit Scale: 30.372 Contrastive_loss: 0.046812 (0.039062) Loss: 0.046812 (0.039062) 2025-03-20,08:31:28 | INFO | Train Epoch: 26 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 149.937/s, 149.937/s/gpu LR: 0.000008 Logit Scale: 30.374 Contrastive_loss: 0.00097614 (0.038176) Loss: 0.00097614 (0.038176) 2025-03-20,08:31:49 | INFO | Train Epoch: 26 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 147.773/s, 147.773/s/gpu LR: 0.000008 Logit Scale: 30.375 Contrastive_loss: 0.00014907 (0.037312) Loss: 0.00014907 (0.037312) 2025-03-20,08:32:11 | INFO | Train Epoch: 26 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.787/s, 149.787/s/gpu LR: 0.000008 Logit Scale: 30.377 Contrastive_loss: 0.062453 (0.037871) Loss: 0.062453 (0.037871) 2025-03-20,08:32:32 | INFO | Train Epoch: 26 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.216, 149.562/s, 149.562/s/gpu LR: 0.000008 Logit Scale: 30.380 Contrastive_loss: 0.040669 (0.037932) Loss: 0.040669 (0.037932) 2025-03-20,08:32:54 | INFO | Train Epoch: 26 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 150.128/s, 150.128/s/gpu LR: 0.000008 Logit Scale: 30.384 Contrastive_loss: 0.00022447 (0.037129) Loss: 0.00022447 (0.037129) 2025-03-20,08:33:15 | INFO | Train Epoch: 26 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 150.051/s, 150.051/s/gpu LR: 0.000008 Logit Scale: 30.388 Contrastive_loss: 0.0010040 (0.036377) Loss: 0.0010040 (0.036377) 2025-03-20,08:33:36 | INFO | Train Epoch: 26 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 149.496/s, 149.496/s/gpu LR: 0.000008 Logit Scale: 30.388 Contrastive_loss: 0.012979 (0.035899) Loss: 0.012979 (0.035899) 2025-03-20,08:33:58 | INFO | Train Epoch: 26 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.213, 149.101/s, 149.101/s/gpu LR: 0.000008 Logit Scale: 30.388 Contrastive_loss: 0.043627 (0.036054) Loss: 0.043627 (0.036054) 2025-03-20,08:34:19 | INFO | Train Epoch: 26 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 149.877/s, 149.877/s/gpu LR: 0.000008 Logit Scale: 30.391 Contrastive_loss: 0.00016478 (0.035350) Loss: 0.00016478 (0.035350) 2025-03-20,08:34:41 | INFO | Train Epoch: 26 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.215, 149.645/s, 149.645/s/gpu LR: 0.000008 Logit Scale: 30.396 Contrastive_loss: 0.10056 (0.036604) Loss: 0.10056 (0.036604) 2025-03-20,08:35:02 | INFO | Train Epoch: 26 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 146.490/s, 146.490/s/gpu LR: 0.000008 Logit Scale: 30.401 Contrastive_loss: 0.052750 (0.036909) Loss: 0.052750 (0.036909) 2025-03-20,08:35:24 | INFO | Train Epoch: 26 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.218, 149.299/s, 149.299/s/gpu LR: 0.000008 Logit Scale: 30.403 Contrastive_loss: 0.015231 (0.036507) Loss: 0.015231 (0.036507) 2025-03-20,08:35:45 | INFO | Train Epoch: 26 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.517/s, 149.517/s/gpu LR: 0.000008 Logit Scale: 30.403 Contrastive_loss: 0.042488 (0.036616) Loss: 0.042488 (0.036616) 2025-03-20,08:36:07 | INFO | Train Epoch: 26 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.678/s, 149.678/s/gpu LR: 0.000008 Logit Scale: 30.404 Contrastive_loss: 0.045492 (0.036775) Loss: 0.045492 (0.036775) 2025-03-20,08:36:28 | INFO | Train Epoch: 26 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.496/s, 149.496/s/gpu LR: 0.000008 Logit Scale: 30.407 Contrastive_loss: 0.058703 (0.037159) Loss: 0.058703 (0.037159) 2025-03-20,08:36:50 | INFO | Train Epoch: 26 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.217, 144.569/s, 144.569/s/gpu LR: 0.000008 Logit Scale: 30.408 Contrastive_loss: 0.0059833 (0.036622) Loss: 0.0059833 (0.036622) 2025-03-20,08:37:12 | INFO | Train Epoch: 26 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.221, 147.319/s, 147.319/s/gpu LR: 0.000008 Logit Scale: 30.406 Contrastive_loss: 0.045445 (0.036771) Loss: 0.045445 (0.036771) 2025-03-20,08:37:34 | INFO | Train Epoch: 26 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.219, 147.023/s, 147.023/s/gpu LR: 0.000008 Logit Scale: 30.409 Contrastive_loss: 0.00013603 (0.036161) Loss: 0.00013603 (0.036161) 2025-03-20,08:37:56 | INFO | Train Epoch: 26 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.220, 145.261/s, 145.261/s/gpu LR: 0.000008 Logit Scale: 30.415 Contrastive_loss: 0.00064095 (0.035578) Loss: 0.00064095 (0.035578) 2025-03-20,08:38:18 | INFO | Train Epoch: 26 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.221, 145.118/s, 145.118/s/gpu LR: 0.000008 Logit Scale: 30.416 Contrastive_loss: 0.075831 (0.036228) Loss: 0.075831 (0.036228) 2025-03-20,08:38:40 | INFO | Train Epoch: 26 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.224, 144.035/s, 144.035/s/gpu LR: 0.000008 Logit Scale: 30.419 Contrastive_loss: 0.011775 (0.035839) Loss: 0.011775 (0.035839) 2025-03-20,08:39:03 | INFO | Train Epoch: 26 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.220, 147.698/s, 147.698/s/gpu LR: 0.000008 Logit Scale: 30.417 Contrastive_loss: 0.12272 (0.037197) Loss: 0.12272 (0.037197) 2025-03-20,08:39:25 | INFO | Train Epoch: 26 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.220, 143.123/s, 143.123/s/gpu LR: 0.000008 Logit Scale: 30.419 Contrastive_loss: 0.0010346 (0.036641) Loss: 0.0010346 (0.036641) 2025-03-20,08:39:46 | INFO | Train Epoch: 26 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.219, 146.578/s, 146.578/s/gpu LR: 0.000008 Logit Scale: 30.423 Contrastive_loss: 0.00032252 (0.036090) Loss: 0.00032252 (0.036090) 2025-03-20,08:40:08 | INFO | Train Epoch: 26 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 151.226/s, 151.226/s/gpu LR: 0.000008 Logit Scale: 30.423 Contrastive_loss: 0.033424 (0.036051) Loss: 0.033424 (0.036051) 2025-03-20,08:40:29 | INFO | Train Epoch: 26 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.213, 148.104/s, 148.104/s/gpu LR: 0.000008 Logit Scale: 30.423 Contrastive_loss: 0.17901 (0.038153) Loss: 0.17901 (0.038153) 2025-03-20,08:40:51 | INFO | Train Epoch: 26 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.580/s, 149.580/s/gpu LR: 0.000008 Logit Scale: 30.425 Contrastive_loss: 0.10056 (0.039057) Loss: 0.10056 (0.039057) 2025-03-20,08:41:12 | INFO | Train Epoch: 26 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 150.072/s, 150.072/s/gpu LR: 0.000008 Logit Scale: 30.426 Contrastive_loss: 0.00068196 (0.038509) Loss: 0.00068196 (0.038509) 2025-03-20,08:41:33 | INFO | Train Epoch: 26 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 150.586/s, 150.586/s/gpu LR: 0.000008 Logit Scale: 30.425 Contrastive_loss: 0.060616 (0.038821) Loss: 0.060616 (0.038821) 2025-03-20,08:41:55 | INFO | Train Epoch: 26 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 149.495/s, 149.495/s/gpu LR: 0.000008 Logit Scale: 30.426 Contrastive_loss: 0.041816 (0.038862) Loss: 0.041816 (0.038862) 2025-03-20,08:42:16 | INFO | Train Epoch: 26 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 150.661/s, 150.661/s/gpu LR: 0.000008 Logit Scale: 30.429 Contrastive_loss: 0.044464 (0.038939) Loss: 0.044464 (0.038939) 2025-03-20,08:42:38 | INFO | Train Epoch: 26 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 149.603/s, 149.603/s/gpu LR: 0.000008 Logit Scale: 30.434 Contrastive_loss: 0.00055051 (0.038420) Loss: 0.00055051 (0.038420) 2025-03-20,08:43:00 | INFO | Train Epoch: 26 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.221, 145.043/s, 145.043/s/gpu LR: 0.000008 Logit Scale: 30.437 Contrastive_loss: 0.022825 (0.038212) Loss: 0.022825 (0.038212) 2025-03-20,08:43:22 | INFO | Train Epoch: 26 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.221, 144.572/s, 144.572/s/gpu LR: 0.000008 Logit Scale: 30.438 Contrastive_loss: 0.056635 (0.038455) Loss: 0.056635 (0.038455) 2025-03-20,08:43:44 | INFO | Train Epoch: 26 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.222, 144.726/s, 144.726/s/gpu LR: 0.000008 Logit Scale: 30.441 Contrastive_loss: 0.0038429 (0.038005) Loss: 0.0038429 (0.038005) 2025-03-20,08:44:06 | INFO | Train Epoch: 26 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.219, 145.786/s, 145.786/s/gpu LR: 0.000008 Logit Scale: 30.442 Contrastive_loss: 2.3853e-05 (0.037518) Loss: 2.3853e-05 (0.037518) 2025-03-20,08:44:28 | INFO | Train Epoch: 26 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 147.796/s, 147.796/s/gpu LR: 0.000008 Logit Scale: 30.443 Contrastive_loss: 0.093711 (0.038229) Loss: 0.093711 (0.038229) 2025-03-20,08:44:50 | INFO | Train Epoch: 26 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.220, 145.582/s, 145.582/s/gpu LR: 0.000007 Logit Scale: 30.443 Contrastive_loss: 0.065257 (0.038567) Loss: 0.065257 (0.038567) 2025-03-20,08:45:12 | INFO | Train Epoch: 26 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 149.669/s, 149.669/s/gpu LR: 0.000007 Logit Scale: 30.444 Contrastive_loss: 0.051970 (0.038733) Loss: 0.051970 (0.038733) 2025-03-20,08:45:33 | INFO | Train Epoch: 26 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 147.246/s, 147.246/s/gpu LR: 0.000007 Logit Scale: 30.445 Contrastive_loss: 0.055626 (0.038939) Loss: 0.055626 (0.038939) 2025-03-20,08:45:54 | INFO | Train Epoch: 26 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.214, 152.732/s, 152.732/s/gpu LR: 0.000007 Logit Scale: 30.446 Contrastive_loss: 0.010511 (0.038596) Loss: 0.010511 (0.038596) 2025-03-20,08:46:16 | INFO | Train Epoch: 26 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.212, 151.485/s, 151.485/s/gpu LR: 0.000007 Logit Scale: 30.447 Contrastive_loss: 0.0081740 (0.038234) Loss: 0.0081740 (0.038234) 2025-03-20,08:46:38 | INFO | Train Epoch: 26 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.219, 148.498/s, 148.498/s/gpu LR: 0.000007 Logit Scale: 30.450 Contrastive_loss: 0.0083950 (0.037883) Loss: 0.0083950 (0.037883) 2025-03-20,08:47:00 | INFO | Train Epoch: 26 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.220, 146.583/s, 146.583/s/gpu LR: 0.000007 Logit Scale: 30.449 Contrastive_loss: 0.032982 (0.037826) Loss: 0.032982 (0.037826) 2025-03-20,08:47:21 | INFO | Train Epoch: 26 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 148.316/s, 148.316/s/gpu LR: 0.000007 Logit Scale: 30.453 Contrastive_loss: 0.00087320 (0.037401) Loss: 0.00087320 (0.037401) 2025-03-20,08:47:43 | INFO | Train Epoch: 26 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.082/s, 149.082/s/gpu LR: 0.000007 Logit Scale: 30.454 Contrastive_loss: 0.039696 (0.037427) Loss: 0.039696 (0.037427) 2025-03-20,08:48:04 | INFO | Train Epoch: 26 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.216, 150.118/s, 150.118/s/gpu LR: 0.000007 Logit Scale: 30.458 Contrastive_loss: 0.071519 (0.037810) Loss: 0.071519 (0.037810) 2025-03-20,08:48:26 | INFO | Train Epoch: 26 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.214, 150.115/s, 150.115/s/gpu LR: 0.000007 Logit Scale: 30.459 Contrastive_loss: 0.00058395 (0.037397) Loss: 0.00058395 (0.037397) 2025-03-20,08:48:47 | INFO | Train Epoch: 26 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 150.461/s, 150.461/s/gpu LR: 0.000007 Logit Scale: 30.461 Contrastive_loss: 0.0012306 (0.036999) Loss: 0.0012306 (0.036999) 2025-03-20,08:49:09 | INFO | Train Epoch: 26 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 149.405/s, 149.405/s/gpu LR: 0.000007 Logit Scale: 30.461 Contrastive_loss: 0.00026789 (0.036600) Loss: 0.00026789 (0.036600) 2025-03-20,08:49:30 | INFO | Train Epoch: 26 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.214, 150.105/s, 150.105/s/gpu LR: 0.000007 Logit Scale: 30.463 Contrastive_loss: 0.089688 (0.037171) Loss: 0.089688 (0.037171) 2025-03-20,08:49:51 | INFO | Train Epoch: 26 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 149.244/s, 149.244/s/gpu LR: 0.000007 Logit Scale: 30.464 Contrastive_loss: 0.00074901 (0.036783) Loss: 0.00074901 (0.036783) 2025-03-20,08:50:13 | INFO | Train Epoch: 26 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 150.752/s, 150.752/s/gpu LR: 0.000007 Logit Scale: 30.466 Contrastive_loss: 0.0047507 (0.036446) Loss: 0.0047507 (0.036446) 2025-03-20,08:50:34 | INFO | Train Epoch: 26 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.213, 149.316/s, 149.316/s/gpu LR: 0.000007 Logit Scale: 30.461 Contrastive_loss: 0.039658 (0.036480) Loss: 0.039658 (0.036480) 2025-03-20,08:50:56 | INFO | Train Epoch: 26 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.214, 149.520/s, 149.520/s/gpu LR: 0.000007 Logit Scale: 30.463 Contrastive_loss: 0.037947 (0.036495) Loss: 0.037947 (0.036495) 2025-03-20,08:51:17 | INFO | Train Epoch: 26 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 147.358/s, 147.358/s/gpu LR: 0.000007 Logit Scale: 30.466 Contrastive_loss: 0.00024697 (0.036125) Loss: 0.00024697 (0.036125) 2025-03-20,08:51:39 | INFO | Train Epoch: 26 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 149.314/s, 149.314/s/gpu LR: 0.000007 Logit Scale: 30.468 Contrastive_loss: 0.00025814 (0.035763) Loss: 0.00025814 (0.035763) 2025-03-20,08:52:00 | INFO | Train Epoch: 26 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 150.094/s, 150.094/s/gpu LR: 0.000007 Logit Scale: 30.471 Contrastive_loss: 0.056511 (0.035970) Loss: 0.056511 (0.035970) 2025-03-20,08:52:21 | INFO | Train Epoch: 26 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.214, 150.039/s, 150.039/s/gpu LR: 0.000007 Logit Scale: 30.475 Contrastive_loss: 0.043914 (0.036049) Loss: 0.043914 (0.036049) 2025-03-20,08:52:43 | INFO | Train Epoch: 26 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.213, 150.936/s, 150.936/s/gpu LR: 0.000007 Logit Scale: 30.477 Contrastive_loss: 0.019691 (0.035888) Loss: 0.019691 (0.035888) 2025-03-20,08:53:04 | INFO | Train Epoch: 26 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 149.785/s, 149.785/s/gpu LR: 0.000007 Logit Scale: 30.479 Contrastive_loss: 0.055901 (0.036083) Loss: 0.055901 (0.036083) 2025-03-20,08:53:26 | INFO | Train Epoch: 26 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 148.740/s, 148.740/s/gpu LR: 0.000007 Logit Scale: 30.479 Contrastive_loss: 0.080208 (0.036507) Loss: 0.080208 (0.036507) 2025-03-20,08:53:47 | INFO | Train Epoch: 26 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 148.055/s, 148.055/s/gpu LR: 0.000007 Logit Scale: 30.482 Contrastive_loss: 0.091769 (0.037033) Loss: 0.091769 (0.037033) 2025-03-20,08:54:09 | INFO | Train Epoch: 26 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 149.177/s, 149.177/s/gpu LR: 0.000007 Logit Scale: 30.483 Contrastive_loss: 0.0053059 (0.036734) Loss: 0.0053059 (0.036734) 2025-03-20,08:54:30 | INFO | Train Epoch: 26 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.160/s, 148.160/s/gpu LR: 0.000007 Logit Scale: 30.482 Contrastive_loss: 0.0053734 (0.036441) Loss: 0.0053734 (0.036441) 2025-03-20,08:54:52 | INFO | Train Epoch: 26 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 150.014/s, 150.014/s/gpu LR: 0.000007 Logit Scale: 30.483 Contrastive_loss: 0.10773 (0.037101) Loss: 0.10773 (0.037101) 2025-03-20,08:55:13 | INFO | Train Epoch: 26 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.214, 148.569/s, 148.569/s/gpu LR: 0.000007 Logit Scale: 30.484 Contrastive_loss: 0.0060839 (0.036816) Loss: 0.0060839 (0.036816) 2025-03-20,08:55:35 | INFO | Train Epoch: 26 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.216, 143.439/s, 143.439/s/gpu LR: 0.000007 Logit Scale: 30.487 Contrastive_loss: 0.00019460 (0.036484) Loss: 0.00019460 (0.036484) 2025-03-20,08:55:56 | INFO | Train Epoch: 26 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.213, 149.064/s, 149.064/s/gpu LR: 0.000007 Logit Scale: 30.489 Contrastive_loss: 0.011638 (0.036260) Loss: 0.011638 (0.036260) 2025-03-20,08:56:18 | INFO | Train Epoch: 26 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.218, 146.706/s, 146.706/s/gpu LR: 0.000007 Logit Scale: 30.494 Contrastive_loss: 0.070484 (0.036565) Loss: 0.070484 (0.036565) 2025-03-20,08:56:40 | INFO | Train Epoch: 26 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.220, 144.817/s, 144.817/s/gpu LR: 0.000007 Logit Scale: 30.491 Contrastive_loss: 0.00025134 (0.036244) Loss: 0.00025134 (0.036244) 2025-03-20,08:57:02 | INFO | Train Epoch: 26 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 147.247/s, 147.247/s/gpu LR: 0.000007 Logit Scale: 30.492 Contrastive_loss: 0.041535 (0.036290) Loss: 0.041535 (0.036290) 2025-03-20,08:57:23 | INFO | Train Epoch: 26 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.217, 149.385/s, 149.385/s/gpu LR: 0.000007 Logit Scale: 30.495 Contrastive_loss: 6.1268e-05 (0.035975) Loss: 6.1268e-05 (0.035975) 2025-03-20,08:57:45 | INFO | Train Epoch: 26 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 148.643/s, 148.643/s/gpu LR: 0.000007 Logit Scale: 30.497 Contrastive_loss: 0.00064738 (0.035671) Loss: 0.00064738 (0.035671) 2025-03-20,08:58:06 | INFO | Train Epoch: 26 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 148.876/s, 148.876/s/gpu LR: 0.000007 Logit Scale: 30.501 Contrastive_loss: 0.052149 (0.035812) Loss: 0.052149 (0.035812) 2025-03-20,08:58:28 | INFO | Train Epoch: 26 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 149.364/s, 149.364/s/gpu LR: 0.000007 Logit Scale: 30.498 Contrastive_loss: 0.043393 (0.035876) Loss: 0.043393 (0.035876) 2025-03-20,08:58:49 | INFO | Train Epoch: 26 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 149.104/s, 149.104/s/gpu LR: 0.000007 Logit Scale: 30.500 Contrastive_loss: 0.046272 (0.035963) Loss: 0.046272 (0.035963) 2025-03-20,08:59:11 | INFO | Train Epoch: 26 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 148.713/s, 148.713/s/gpu LR: 0.000007 Logit Scale: 30.501 Contrastive_loss: 0.087329 (0.036391) Loss: 0.087329 (0.036391) 2025-03-20,08:59:32 | INFO | Train Epoch: 26 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.217, 147.262/s, 147.262/s/gpu LR: 0.000007 Logit Scale: 30.502 Contrastive_loss: 0.00085597 (0.036098) Loss: 0.00085597 (0.036098) 2025-03-20,08:59:54 | INFO | Train Epoch: 26 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 145.708/s, 145.708/s/gpu LR: 0.000007 Logit Scale: 30.504 Contrastive_loss: 0.00045856 (0.035805) Loss: 0.00045856 (0.035805) 2025-03-20,09:00:16 | INFO | Train Epoch: 26 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.219, 146.876/s, 146.876/s/gpu LR: 0.000007 Logit Scale: 30.507 Contrastive_loss: 0.13339 (0.036599) Loss: 0.13339 (0.036599) 2025-03-20,09:00:38 | INFO | Train Epoch: 26 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.219, 146.911/s, 146.911/s/gpu LR: 0.000007 Logit Scale: 30.507 Contrastive_loss: 0.00010704 (0.036305) Loss: 0.00010704 (0.036305) 2025-03-20,09:01:00 | INFO | Train Epoch: 26 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.220, 145.619/s, 145.619/s/gpu LR: 0.000007 Logit Scale: 30.507 Contrastive_loss: 0.025979 (0.036222) Loss: 0.025979 (0.036222) 2025-03-20,09:01:22 | INFO | Train Epoch: 26 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.223, 144.854/s, 144.854/s/gpu LR: 0.000007 Logit Scale: 30.508 Contrastive_loss: 0.0022414 (0.035952) Loss: 0.0022414 (0.035952) 2025-03-20,09:01:44 | INFO | Train Epoch: 26 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.219, 146.219/s, 146.219/s/gpu LR: 0.000007 Logit Scale: 30.507 Contrastive_loss: 1.8436e-05 (0.035669) Loss: 1.8436e-05 (0.035669) 2025-03-20,09:02:05 | INFO | Train Epoch: 26 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.799/s, 148.799/s/gpu LR: 0.000007 Logit Scale: 30.509 Contrastive_loss: 0.0046711 (0.035427) Loss: 0.0046711 (0.035427) 2025-03-20,09:02:27 | INFO | Train Epoch: 26 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 149.323/s, 149.323/s/gpu LR: 0.000007 Logit Scale: 30.508 Contrastive_loss: 0.055539 (0.035583) Loss: 0.055539 (0.035583) 2025-03-20,09:02:48 | INFO | Train Epoch: 26 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 149.965/s, 149.965/s/gpu LR: 0.000007 Logit Scale: 30.509 Contrastive_loss: 0.00016160 (0.035311) Loss: 0.00016160 (0.035311) 2025-03-20,09:03:10 | INFO | Train Epoch: 26 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 148.480/s, 148.480/s/gpu LR: 0.000007 Logit Scale: 30.512 Contrastive_loss: 0.098320 (0.035792) Loss: 0.098320 (0.035792) 2025-03-20,09:03:31 | INFO | Train Epoch: 26 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 149.192/s, 149.192/s/gpu LR: 0.000007 Logit Scale: 30.514 Contrastive_loss: 0.019039 (0.035665) Loss: 0.019039 (0.035665) 2025-03-20,09:03:53 | INFO | Train Epoch: 26 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 149.531/s, 149.531/s/gpu LR: 0.000007 Logit Scale: 30.512 Contrastive_loss: 0.10537 (0.036189) Loss: 0.10537 (0.036189) 2025-03-20,09:04:15 | INFO | Train Epoch: 26 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 142.843/s, 142.843/s/gpu LR: 0.000007 Logit Scale: 30.515 Contrastive_loss: 0.00065668 (0.035924) Loss: 0.00065668 (0.035924) 2025-03-20,09:04:37 | INFO | Train Epoch: 26 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.220, 149.367/s, 149.367/s/gpu LR: 0.000007 Logit Scale: 30.512 Contrastive_loss: 0.00051882 (0.035661) Loss: 0.00051882 (0.035661) 2025-03-20,09:04:58 | INFO | Train Epoch: 26 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 147.294/s, 147.294/s/gpu LR: 0.000007 Logit Scale: 30.514 Contrastive_loss: 0.0014080 (0.035409) Loss: 0.0014080 (0.035409) 2025-03-20,09:05:20 | INFO | Train Epoch: 26 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 148.914/s, 148.914/s/gpu LR: 0.000007 Logit Scale: 30.515 Contrastive_loss: 0.046326 (0.035489) Loss: 0.046326 (0.035489) 2025-03-20,09:05:41 | INFO | Train Epoch: 26 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.214, 149.784/s, 149.784/s/gpu LR: 0.000007 Logit Scale: 30.515 Contrastive_loss: 0.056539 (0.035642) Loss: 0.056539 (0.035642) 2025-03-20,09:06:03 | INFO | Train Epoch: 26 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 150.091/s, 150.091/s/gpu LR: 0.000007 Logit Scale: 30.516 Contrastive_loss: 0.043667 (0.035699) Loss: 0.043667 (0.035699) 2025-03-20,09:06:24 | INFO | Train Epoch: 26 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.214, 148.553/s, 148.553/s/gpu LR: 0.000007 Logit Scale: 30.521 Contrastive_loss: 0.074332 (0.035975) Loss: 0.074332 (0.035975) 2025-03-20,09:06:46 | INFO | Train Epoch: 26 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 151.108/s, 151.108/s/gpu LR: 0.000007 Logit Scale: 30.520 Contrastive_loss: 0.0058083 (0.035761) Loss: 0.0058083 (0.035761) 2025-03-20,09:07:07 | INFO | Train Epoch: 26 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 149.150/s, 149.150/s/gpu LR: 0.000006 Logit Scale: 30.523 Contrastive_loss: 0.038061 (0.035778) Loss: 0.038061 (0.035778) 2025-03-20,09:07:29 | INFO | Train Epoch: 26 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 147.983/s, 147.983/s/gpu LR: 0.000006 Logit Scale: 30.524 Contrastive_loss: 0.063838 (0.035974) Loss: 0.063838 (0.035974) 2025-03-20,09:07:50 | INFO | Train Epoch: 26 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.214, 149.562/s, 149.562/s/gpu LR: 0.000006 Logit Scale: 30.527 Contrastive_loss: 0.063791 (0.036167) Loss: 0.063791 (0.036167) 2025-03-20,09:08:12 | INFO | Train Epoch: 26 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.298/s, 149.298/s/gpu LR: 0.000006 Logit Scale: 30.528 Contrastive_loss: 0.0014843 (0.035928) Loss: 0.0014843 (0.035928) 2025-03-20,09:08:33 | INFO | Train Epoch: 26 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 149.515/s, 149.515/s/gpu LR: 0.000006 Logit Scale: 30.530 Contrastive_loss: 0.012268 (0.035766) Loss: 0.012268 (0.035766) 2025-03-20,09:08:54 | INFO | Train Epoch: 26 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.214, 151.237/s, 151.237/s/gpu LR: 0.000006 Logit Scale: 30.530 Contrastive_loss: 8.6274e-05 (0.035523) Loss: 8.6274e-05 (0.035523) 2025-03-20,09:09:16 | INFO | Train Epoch: 26 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.212, 150.843/s, 150.843/s/gpu LR: 0.000006 Logit Scale: 30.531 Contrastive_loss: 0.0015455 (0.035293) Loss: 0.0015455 (0.035293) 2025-03-20,09:09:37 | INFO | Train Epoch: 26 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.214, 149.049/s, 149.049/s/gpu LR: 0.000006 Logit Scale: 30.531 Contrastive_loss: 0.0098656 (0.035123) Loss: 0.0098656 (0.035123) 2025-03-20,09:09:59 | INFO | Train Epoch: 26 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.218, 147.047/s, 147.047/s/gpu LR: 0.000006 Logit Scale: 30.534 Contrastive_loss: 0.0017539 (0.034900) Loss: 0.0017539 (0.034900) 2025-03-20,09:10:20 | INFO | Train Epoch: 26 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.214, 149.295/s, 149.295/s/gpu LR: 0.000006 Logit Scale: 30.533 Contrastive_loss: 4.4368e-05 (0.034670) Loss: 4.4368e-05 (0.034670) 2025-03-20,09:10:42 | INFO | Train Epoch: 26 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 147.788/s, 147.788/s/gpu LR: 0.000006 Logit Scale: 30.533 Contrastive_loss: 0.052332 (0.034786) Loss: 0.052332 (0.034786) 2025-03-20,09:11:04 | INFO | Train Epoch: 26 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.220, 147.247/s, 147.247/s/gpu LR: 0.000006 Logit Scale: 30.532 Contrastive_loss: 0.00088303 (0.034564) Loss: 0.00088303 (0.034564) 2025-03-20,09:11:25 | INFO | Train Epoch: 26 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 151.773/s, 151.773/s/gpu LR: 0.000006 Logit Scale: 30.534 Contrastive_loss: 0.022780 (0.034488) Loss: 0.022780 (0.034488) 2025-03-20,09:11:47 | INFO | Train Epoch: 26 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.212, 151.446/s, 151.446/s/gpu LR: 0.000006 Logit Scale: 30.535 Contrastive_loss: 0.044102 (0.034550) Loss: 0.044102 (0.034550) 2025-03-20,09:12:08 | INFO | Train Epoch: 26 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.213, 145.138/s, 145.138/s/gpu LR: 0.000006 Logit Scale: 30.539 Contrastive_loss: 0.11955 (0.035095) Loss: 0.11955 (0.035095) 2025-03-20,09:12:29 | INFO | Train Epoch: 26 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 148.447/s, 148.447/s/gpu LR: 0.000006 Logit Scale: 30.539 Contrastive_loss: 0.0046487 (0.034901) Loss: 0.0046487 (0.034901) 2025-03-20,09:12:51 | INFO | Train Epoch: 26 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 149.182/s, 149.182/s/gpu LR: 0.000006 Logit Scale: 30.542 Contrastive_loss: 0.00038576 (0.034682) Loss: 0.00038576 (0.034682) 2025-03-20,09:13:12 | INFO | Train Epoch: 26 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 149.453/s, 149.453/s/gpu LR: 0.000006 Logit Scale: 30.543 Contrastive_loss: 0.057144 (0.034823) Loss: 0.057144 (0.034823) 2025-03-20,09:13:34 | INFO | Train Epoch: 26 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 148.304/s, 148.304/s/gpu LR: 0.000006 Logit Scale: 30.545 Contrastive_loss: 0.044331 (0.034883) Loss: 0.044331 (0.034883) 2025-03-20,09:13:56 | INFO | Train Epoch: 26 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 148.104/s, 148.104/s/gpu LR: 0.000006 Logit Scale: 30.547 Contrastive_loss: 0.075538 (0.035135) Loss: 0.075538 (0.035135) 2025-03-20,09:14:17 | INFO | Train Epoch: 26 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 147.536/s, 147.536/s/gpu LR: 0.000006 Logit Scale: 30.548 Contrastive_loss: 0.12307 (0.035678) Loss: 0.12307 (0.035678) 2025-03-20,09:14:39 | INFO | Train Epoch: 26 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.218, 147.530/s, 147.530/s/gpu LR: 0.000006 Logit Scale: 30.550 Contrastive_loss: 0.0035207 (0.035481) Loss: 0.0035207 (0.035481) 2025-03-20,09:15:01 | INFO | Train Epoch: 26 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.217, 144.092/s, 144.092/s/gpu LR: 0.000006 Logit Scale: 30.553 Contrastive_loss: 0.0010177 (0.035271) Loss: 0.0010177 (0.035271) 2025-03-20,09:15:23 | INFO | Train Epoch: 26 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 147.024/s, 147.024/s/gpu LR: 0.000006 Logit Scale: 30.555 Contrastive_loss: 0.00028706 (0.035059) Loss: 0.00028706 (0.035059) 2025-03-20,09:15:44 | INFO | Train Epoch: 26 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.218, 147.236/s, 147.236/s/gpu LR: 0.000006 Logit Scale: 30.556 Contrastive_loss: 0.19156 (0.036002) Loss: 0.19156 (0.036002) 2025-03-20,09:16:06 | INFO | Train Epoch: 26 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.215, 148.971/s, 148.971/s/gpu LR: 0.000006 Logit Scale: 30.559 Contrastive_loss: 0.010218 (0.035847) Loss: 0.010218 (0.035847) 2025-03-20,09:16:28 | INFO | Train Epoch: 26 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.218, 148.290/s, 148.290/s/gpu LR: 0.000006 Logit Scale: 30.557 Contrastive_loss: 0.010651 (0.035697) Loss: 0.010651 (0.035697) 2025-03-20,09:16:49 | INFO | Train Epoch: 26 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.219, 143.775/s, 143.775/s/gpu LR: 0.000006 Logit Scale: 30.558 Contrastive_loss: 0.067177 (0.035883) Loss: 0.067177 (0.035883) 2025-03-20,09:17:12 | INFO | Train Epoch: 26 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.220, 149.704/s, 149.704/s/gpu LR: 0.000006 Logit Scale: 30.557 Contrastive_loss: 0.10339 (0.036281) Loss: 0.10339 (0.036281) 2025-03-20,09:17:33 | INFO | Train Epoch: 26 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 144.865/s, 144.865/s/gpu LR: 0.000006 Logit Scale: 30.557 Contrastive_loss: 0.10604 (0.036688) Loss: 0.10604 (0.036688) 2025-03-20,09:17:55 | INFO | Train Epoch: 26 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.219, 148.239/s, 148.239/s/gpu LR: 0.000006 Logit Scale: 30.557 Contrastive_loss: 0.0015804 (0.036484) Loss: 0.0015804 (0.036484) 2025-03-20,09:18:17 | INFO | Train Epoch: 26 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.217, 147.101/s, 147.101/s/gpu LR: 0.000006 Logit Scale: 30.557 Contrastive_loss: 0.0084799 (0.036322) Loss: 0.0084799 (0.036322) 2025-03-20,09:18:39 | INFO | Train Epoch: 26 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 151.067/s, 151.067/s/gpu LR: 0.000006 Logit Scale: 30.560 Contrastive_loss: 0.040674 (0.036347) Loss: 0.040674 (0.036347) 2025-03-20,09:19:00 | INFO | Train Epoch: 26 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.212, 149.667/s, 149.667/s/gpu LR: 0.000006 Logit Scale: 30.562 Contrastive_loss: 0.030101 (0.036312) Loss: 0.030101 (0.036312) 2025-03-20,09:19:22 | INFO | Train Epoch: 26 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.221, 146.711/s, 146.711/s/gpu LR: 0.000006 Logit Scale: 30.563 Contrastive_loss: 0.00081084 (0.036110) Loss: 0.00081084 (0.036110) 2025-03-20,09:19:44 | INFO | Train Epoch: 26 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.219, 148.693/s, 148.693/s/gpu LR: 0.000006 Logit Scale: 30.560 Contrastive_loss: 0.00010767 (0.035907) Loss: 0.00010767 (0.035907) 2025-03-20,09:20:05 | INFO | Train Epoch: 26 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.217, 148.402/s, 148.402/s/gpu LR: 0.000006 Logit Scale: 30.564 Contrastive_loss: 0.00047676 (0.035708) Loss: 0.00047676 (0.035708) 2025-03-20,09:20:27 | INFO | Train Epoch: 26 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 148.419/s, 148.419/s/gpu LR: 0.000006 Logit Scale: 30.559 Contrastive_loss: 0.036340 (0.035711) Loss: 0.036340 (0.035711) 2025-03-20,09:20:49 | INFO | Train Epoch: 26 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.219, 143.253/s, 143.253/s/gpu LR: 0.000006 Logit Scale: 30.559 Contrastive_loss: 0.0088569 (0.035562) Loss: 0.0088569 (0.035562) 2025-03-20,09:21:11 | INFO | Train Epoch: 26 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.219, 148.264/s, 148.264/s/gpu LR: 0.000006 Logit Scale: 30.560 Contrastive_loss: 0.00093409 (0.035371) Loss: 0.00093409 (0.035371) 2025-03-20,09:21:33 | INFO | Train Epoch: 26 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.218, 144.406/s, 144.406/s/gpu LR: 0.000006 Logit Scale: 30.563 Contrastive_loss: 0.0084472 (0.035223) Loss: 0.0084472 (0.035223) 2025-03-20,09:21:54 | INFO | Train Epoch: 26 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.679/s, 149.679/s/gpu LR: 0.000006 Logit Scale: 30.564 Contrastive_loss: 0.00067404 (0.035034) Loss: 0.00067404 (0.035034) 2025-03-20,09:22:15 | INFO | Train Epoch: 26 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 149.280/s, 149.280/s/gpu LR: 0.000006 Logit Scale: 30.566 Contrastive_loss: 0.043952 (0.035082) Loss: 0.043952 (0.035082) 2025-03-20,09:22:37 | INFO | Train Epoch: 26 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.215, 152.341/s, 152.341/s/gpu LR: 0.000006 Logit Scale: 30.564 Contrastive_loss: 0.081454 (0.035333) Loss: 0.081454 (0.035333) 2025-03-20,09:22:58 | INFO | Train Epoch: 26 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 150.148/s, 150.148/s/gpu LR: 0.000006 Logit Scale: 30.565 Contrastive_loss: 0.031086 (0.035310) Loss: 0.031086 (0.035310) 2025-03-20,09:23:20 | INFO | Train Epoch: 26 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.217, 146.763/s, 146.763/s/gpu LR: 0.000006 Logit Scale: 30.565 Contrastive_loss: 0.060330 (0.035444) Loss: 0.060330 (0.035444) 2025-03-20,09:23:41 | INFO | Train Epoch: 26 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.213, 151.062/s, 151.062/s/gpu LR: 0.000006 Logit Scale: 30.569 Contrastive_loss: 0.0012118 (0.035262) Loss: 0.0012118 (0.035262) 2025-03-20,09:24:03 | INFO | Train Epoch: 26 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 149.897/s, 149.897/s/gpu LR: 0.000006 Logit Scale: 30.568 Contrastive_loss: 0.023514 (0.035200) Loss: 0.023514 (0.035200) 2025-03-20,09:24:24 | INFO | Train Epoch: 26 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 149.804/s, 149.804/s/gpu LR: 0.000006 Logit Scale: 30.569 Contrastive_loss: 0.010656 (0.035071) Loss: 0.010656 (0.035071) 2025-03-20,09:24:46 | INFO | Train Epoch: 26 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 146.549/s, 146.549/s/gpu LR: 0.000006 Logit Scale: 30.572 Contrastive_loss: 0.050604 (0.035152) Loss: 0.050604 (0.035152) 2025-03-20,09:25:07 | INFO | Train Epoch: 26 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.213, 151.949/s, 151.949/s/gpu LR: 0.000006 Logit Scale: 30.572 Contrastive_loss: 0.00017319 (0.034970) Loss: 0.00017319 (0.034970) 2025-03-20,09:25:29 | INFO | Train Epoch: 26 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.215, 148.498/s, 148.498/s/gpu LR: 0.000006 Logit Scale: 30.572 Contrastive_loss: 0.0080638 (0.034830) Loss: 0.0080638 (0.034830) 2025-03-20,09:25:50 | INFO | Train Epoch: 26 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.672/s, 149.672/s/gpu LR: 0.000006 Logit Scale: 30.574 Contrastive_loss: 0.070572 (0.035015) Loss: 0.070572 (0.035015) 2025-03-20,09:26:12 | INFO | Train Epoch: 26 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.855/s, 148.855/s/gpu LR: 0.000006 Logit Scale: 30.578 Contrastive_loss: 0.043763 (0.035059) Loss: 0.043763 (0.035059) 2025-03-20,09:26:33 | INFO | Train Epoch: 26 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 148.929/s, 148.929/s/gpu LR: 0.000006 Logit Scale: 30.577 Contrastive_loss: 0.071549 (0.035246) Loss: 0.071549 (0.035246) 2025-03-20,09:26:55 | INFO | Train Epoch: 26 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.292/s, 149.292/s/gpu LR: 0.000006 Logit Scale: 30.577 Contrastive_loss: 0.044842 (0.035294) Loss: 0.044842 (0.035294) 2025-03-20,09:27:16 | INFO | Train Epoch: 26 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 150.211/s, 150.211/s/gpu LR: 0.000006 Logit Scale: 30.578 Contrastive_loss: 0.00016868 (0.035117) Loss: 0.00016868 (0.035117) 2025-03-20,09:27:38 | INFO | Train Epoch: 26 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 150.260/s, 150.260/s/gpu LR: 0.000006 Logit Scale: 30.582 Contrastive_loss: 0.017875 (0.035030) Loss: 0.017875 (0.035030) 2025-03-20,09:27:59 | INFO | Train Epoch: 26 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.214, 148.245/s, 148.245/s/gpu LR: 0.000006 Logit Scale: 30.584 Contrastive_loss: 0.11492 (0.035430) Loss: 0.11492 (0.035430) 2025-03-20,09:28:21 | INFO | Train Epoch: 26 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.218, 146.010/s, 146.010/s/gpu LR: 0.000006 Logit Scale: 30.585 Contrastive_loss: 0.023136 (0.035369) Loss: 0.023136 (0.035369) 2025-03-20,09:28:43 | INFO | Train Epoch: 26 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.219, 148.434/s, 148.434/s/gpu LR: 0.000006 Logit Scale: 30.586 Contrastive_loss: 0.049725 (0.035440) Loss: 0.049725 (0.035440) 2025-03-20,09:29:04 | INFO | Train Epoch: 26 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 148.928/s, 148.928/s/gpu LR: 0.000006 Logit Scale: 30.587 Contrastive_loss: 0.0089377 (0.035309) Loss: 0.0089377 (0.035309) 2025-03-20,09:29:26 | INFO | Train Epoch: 26 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.218, 144.728/s, 144.728/s/gpu LR: 0.000006 Logit Scale: 30.590 Contrastive_loss: 0.00019465 (0.035137) Loss: 0.00019465 (0.035137) 2025-03-20,09:29:47 | INFO | Train Epoch: 26 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.214, 148.874/s, 148.874/s/gpu LR: 0.000006 Logit Scale: 30.592 Contrastive_loss: 0.030160 (0.035113) Loss: 0.030160 (0.035113) 2025-03-20,09:30:09 | INFO | Train Epoch: 26 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.218, 146.071/s, 146.071/s/gpu LR: 0.000006 Logit Scale: 30.596 Contrastive_loss: 0.016731 (0.035023) Loss: 0.016731 (0.035023) 2025-03-20,09:30:31 | INFO | Train Epoch: 26 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.219, 148.284/s, 148.284/s/gpu LR: 0.000006 Logit Scale: 30.598 Contrastive_loss: 0.038763 (0.035041) Loss: 0.038763 (0.035041) 2025-03-20,09:30:53 | INFO | Train Epoch: 26 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 146.324/s, 146.324/s/gpu LR: 0.000005 Logit Scale: 30.600 Contrastive_loss: 0.078121 (0.035249) Loss: 0.078121 (0.035249) 2025-03-20,09:31:14 | INFO | Train Epoch: 26 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 147.391/s, 147.391/s/gpu LR: 0.000005 Logit Scale: 30.599 Contrastive_loss: 0.0016856 (0.035088) Loss: 0.0016856 (0.035088) 2025-03-20,09:31:36 | INFO | Train Epoch: 26 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 151.111/s, 151.111/s/gpu LR: 0.000005 Logit Scale: 30.600 Contrastive_loss: 0.053457 (0.035175) Loss: 0.053457 (0.035175) 2025-03-20,09:31:58 | INFO | Train Epoch: 26 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 149.861/s, 149.861/s/gpu LR: 0.000005 Logit Scale: 30.602 Contrastive_loss: 0.012247 (0.035067) Loss: 0.012247 (0.035067) 2025-03-20,09:32:19 | INFO | Train Epoch: 26 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 151.799/s, 151.799/s/gpu LR: 0.000005 Logit Scale: 30.601 Contrastive_loss: 0.0065420 (0.034932) Loss: 0.0065420 (0.034932) 2025-03-20,09:32:41 | INFO | Train Epoch: 26 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.211, 151.031/s, 151.031/s/gpu LR: 0.000005 Logit Scale: 30.602 Contrastive_loss: 0.068583 (0.035090) Loss: 0.068583 (0.035090) 2025-03-20,09:33:02 | INFO | Train Epoch: 26 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 149.379/s, 149.379/s/gpu LR: 0.000005 Logit Scale: 30.604 Contrastive_loss: 0.014042 (0.034992) Loss: 0.014042 (0.034992) 2025-03-20,09:33:24 | INFO | Train Epoch: 26 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 148.023/s, 148.023/s/gpu LR: 0.000005 Logit Scale: 30.605 Contrastive_loss: 0.089586 (0.035246) Loss: 0.089586 (0.035246) 2025-03-20,09:33:45 | INFO | Train Epoch: 26 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 148.245/s, 148.245/s/gpu LR: 0.000005 Logit Scale: 30.608 Contrastive_loss: 0.0038655 (0.035101) Loss: 0.0038655 (0.035101) 2025-03-20,09:34:07 | INFO | Train Epoch: 26 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 149.653/s, 149.653/s/gpu LR: 0.000005 Logit Scale: 30.610 Contrastive_loss: 0.019994 (0.035031) Loss: 0.019994 (0.035031) 2025-03-20,09:34:28 | INFO | Train Epoch: 26 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 148.082/s, 148.082/s/gpu LR: 0.000005 Logit Scale: 30.612 Contrastive_loss: 0.073377 (0.035207) Loss: 0.073377 (0.035207) 2025-03-20,09:34:50 | INFO | Train Epoch: 26 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.607/s, 149.607/s/gpu LR: 0.000005 Logit Scale: 30.616 Contrastive_loss: 0.051639 (0.035282) Loss: 0.051639 (0.035282) 2025-03-20,09:35:11 | INFO | Train Epoch: 26 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.216, 149.089/s, 149.089/s/gpu LR: 0.000005 Logit Scale: 30.614 Contrastive_loss: 3.7789e-05 (0.035122) Loss: 3.7789e-05 (0.035122) 2025-03-20,09:35:33 | INFO | Train Epoch: 26 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.214, 149.229/s, 149.229/s/gpu LR: 0.000005 Logit Scale: 30.614 Contrastive_loss: 0.12788 (0.035541) Loss: 0.12788 (0.035541) 2025-03-20,09:35:54 | INFO | Train Epoch: 26 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 143.224/s, 143.224/s/gpu LR: 0.000005 Logit Scale: 30.617 Contrastive_loss: 0.0019786 (0.035390) Loss: 0.0019786 (0.035390) 2025-03-20,09:36:16 | INFO | Train Epoch: 26 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.220, 146.228/s, 146.228/s/gpu LR: 0.000005 Logit Scale: 30.620 Contrastive_loss: 0.010662 (0.035279) Loss: 0.010662 (0.035279) 2025-03-20,09:36:38 | INFO | Train Epoch: 26 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.219, 151.391/s, 151.391/s/gpu LR: 0.000005 Logit Scale: 30.622 Contrastive_loss: 0.0011084 (0.035127) Loss: 0.0011084 (0.035127) 2025-03-20,09:37:00 | INFO | Train Epoch: 26 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.213, 150.207/s, 150.207/s/gpu LR: 0.000005 Logit Scale: 30.624 Contrastive_loss: 0.012316 (0.035025) Loss: 0.012316 (0.035025) 2025-03-20,09:37:21 | INFO | Train Epoch: 26 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.219, 150.208/s, 150.208/s/gpu LR: 0.000005 Logit Scale: 30.621 Contrastive_loss: 0.031531 (0.035010) Loss: 0.031531 (0.035010) 2025-03-20,09:37:43 | INFO | Train Epoch: 26 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 151.908/s, 151.908/s/gpu LR: 0.000005 Logit Scale: 30.621 Contrastive_loss: 0.054080 (0.035094) Loss: 0.054080 (0.035094) 2025-03-20,09:38:04 | INFO | Train Epoch: 26 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.214, 149.722/s, 149.722/s/gpu LR: 0.000005 Logit Scale: 30.620 Contrastive_loss: 0.073710 (0.035263) Loss: 0.073710 (0.035263) 2025-03-20,09:38:26 | INFO | Train Epoch: 26 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.217, 149.861/s, 149.861/s/gpu LR: 0.000005 Logit Scale: 30.623 Contrastive_loss: 0.00022726 (0.035110) Loss: 0.00022726 (0.035110) 2025-03-20,09:38:47 | INFO | Train Epoch: 26 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 149.600/s, 149.600/s/gpu LR: 0.000005 Logit Scale: 30.623 Contrastive_loss: 0.012394 (0.035012) Loss: 0.012394 (0.035012) 2025-03-20,09:39:09 | INFO | Train Epoch: 26 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 150.028/s, 150.028/s/gpu LR: 0.000005 Logit Scale: 30.624 Contrastive_loss: 0.0016636 (0.034867) Loss: 0.0016636 (0.034867) 2025-03-20,09:39:30 | INFO | Train Epoch: 26 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.480/s, 149.480/s/gpu LR: 0.000005 Logit Scale: 30.622 Contrastive_loss: 0.0033136 (0.034731) Loss: 0.0033136 (0.034731) 2025-03-20,09:39:51 | INFO | Train Epoch: 26 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 151.338/s, 151.338/s/gpu LR: 0.000005 Logit Scale: 30.622 Contrastive_loss: 0.0048172 (0.034603) Loss: 0.0048172 (0.034603) 2025-03-20,09:40:13 | INFO | Train Epoch: 26 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 147.357/s, 147.357/s/gpu LR: 0.000005 Logit Scale: 30.622 Contrastive_loss: 0.015143 (0.034520) Loss: 0.015143 (0.034520) 2025-03-20,09:40:35 | INFO | Train Epoch: 26 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.218, 145.571/s, 145.571/s/gpu LR: 0.000005 Logit Scale: 30.622 Contrastive_loss: 0.079775 (0.034712) Loss: 0.079775 (0.034712) 2025-03-20,09:40:56 | INFO | Train Epoch: 26 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.352/s, 147.352/s/gpu LR: 0.000005 Logit Scale: 30.624 Contrastive_loss: 0.013010 (0.034620) Loss: 0.013010 (0.034620) 2025-03-20,09:41:18 | INFO | Train Epoch: 26 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.220, 149.385/s, 149.385/s/gpu LR: 0.000005 Logit Scale: 30.625 Contrastive_loss: 0.00014846 (0.034475) Loss: 0.00014846 (0.034475) 2025-03-20,09:41:40 | INFO | Train Epoch: 26 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.218, 149.791/s, 149.791/s/gpu LR: 0.000005 Logit Scale: 30.627 Contrastive_loss: 0.022791 (0.034426) Loss: 0.022791 (0.034426) 2025-03-20,09:42:01 | INFO | Train Epoch: 26 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 154.840/s, 154.840/s/gpu LR: 0.000005 Logit Scale: 30.627 Contrastive_loss: 0.0014667 (0.034288) Loss: 0.0014667 (0.034288) 2025-03-20,09:42:23 | INFO | Train Epoch: 26 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.214, 151.203/s, 151.203/s/gpu LR: 0.000005 Logit Scale: 30.628 Contrastive_loss: 0.031461 (0.034276) Loss: 0.031461 (0.034276) 2025-03-20,09:42:31 | INFO | Train Epoch: 26 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.217, 149.169/s, 149.169/s/gpu LR: 0.000005 Logit Scale: 30.629 Contrastive_loss: 0.051875 (0.034349) Loss: 0.051875 (0.034349) 2025-03-20,09:42:31 | INFO | Eval Epoch: 27 [32 / 7443] Clip Loss: 4.238996 2025-03-20,09:42:37 | INFO | Eval Epoch: 27 [3232 / 7443] Clip Loss: 0.846038 2025-03-20,09:42:43 | INFO | Eval Epoch: 27 [6432 / 7443] Clip Loss: 0.630858 2025-03-20,09:42:45 | INFO | Eval Epoch: 27 image_to_text_mean_rank: 75.5820 image_to_text_median_rank: 4.0000 image_to_text_R@1: 0.2113 image_to_text_R@5: 0.5690 image_to_text_R@10: 0.7242 text_to_image_mean_rank: 49.8412 text_to_image_median_rank: 4.0000 text_to_image_R@1: 0.2111 text_to_image_R@5: 0.5597 text_to_image_R@10: 0.7150 clip_val_loss: 0.5865 epoch: 27.0000 num_samples: 7443.0000 2025-03-20,09:43:16 | INFO | Start epoch 27 2025-03-20,09:43:16 | INFO | Train Epoch: 27 [ 32/766009 (0%)] Data (t): 0.171 Batch (t): 0.372, 85.9983/s, 85.9983/s/gpu LR: 0.000005 Logit Scale: 30.629 Contrastive_loss: 0.00034860 (0.00034860) Loss: 0.00034860 (0.00034860) 2025-03-20,09:43:38 | INFO | Train Epoch: 27 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.217, 145.961/s, 145.961/s/gpu LR: 0.000005 Logit Scale: 30.630 Contrastive_loss: 0.0096741 (0.0050113) Loss: 0.0096741 (0.0050113) 2025-03-20,09:44:00 | INFO | Train Epoch: 27 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.217, 145.708/s, 145.708/s/gpu LR: 0.000005 Logit Scale: 30.633 Contrastive_loss: 0.050911 (0.020311) Loss: 0.050911 (0.020311) 2025-03-20,09:44:21 | INFO | Train Epoch: 27 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.218, 146.775/s, 146.775/s/gpu LR: 0.000005 Logit Scale: 30.636 Contrastive_loss: 0.00040890 (0.015336) Loss: 0.00040890 (0.015336) 2025-03-20,09:44:43 | INFO | Train Epoch: 27 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.218, 149.051/s, 149.051/s/gpu LR: 0.000005 Logit Scale: 30.638 Contrastive_loss: 0.0090244 (0.014073) Loss: 0.0090244 (0.014073) 2025-03-20,09:45:05 | INFO | Train Epoch: 27 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 149.221/s, 149.221/s/gpu LR: 0.000005 Logit Scale: 30.642 Contrastive_loss: 0.0043780 (0.012458) Loss: 0.0043780 (0.012458) 2025-03-20,09:45:26 | INFO | Train Epoch: 27 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 150.900/s, 150.900/s/gpu LR: 0.000005 Logit Scale: 30.645 Contrastive_loss: 0.12349 (0.028320) Loss: 0.12349 (0.028320) 2025-03-20,09:45:48 | INFO | Train Epoch: 27 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 147.646/s, 147.646/s/gpu LR: 0.000005 Logit Scale: 30.648 Contrastive_loss: 2.3583e-05 (0.024783) Loss: 2.3583e-05 (0.024783) 2025-03-20,09:46:09 | INFO | Train Epoch: 27 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 148.966/s, 148.966/s/gpu LR: 0.000005 Logit Scale: 30.652 Contrastive_loss: 0.00025305 (0.022057) Loss: 0.00025305 (0.022057) 2025-03-20,09:46:31 | INFO | Train Epoch: 27 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.216, 149.001/s, 149.001/s/gpu LR: 0.000005 Logit Scale: 30.650 Contrastive_loss: 0.037731 (0.023625) Loss: 0.037731 (0.023625) 2025-03-20,09:46:52 | INFO | Train Epoch: 27 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.216, 149.825/s, 149.825/s/gpu LR: 0.000005 Logit Scale: 30.652 Contrastive_loss: 0.029122 (0.024124) Loss: 0.029122 (0.024124) 2025-03-20,09:47:14 | INFO | Train Epoch: 27 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.807/s, 149.807/s/gpu LR: 0.000005 Logit Scale: 30.654 Contrastive_loss: 5.5421e-05 (0.022119) Loss: 5.5421e-05 (0.022119) 2025-03-20,09:47:35 | INFO | Train Epoch: 27 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.214, 149.640/s, 149.640/s/gpu LR: 0.000005 Logit Scale: 30.656 Contrastive_loss: 0.14278 (0.031400) Loss: 0.14278 (0.031400) 2025-03-20,09:47:57 | INFO | Train Epoch: 27 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.182/s, 148.182/s/gpu LR: 0.000005 Logit Scale: 30.656 Contrastive_loss: 0.0071383 (0.029667) Loss: 0.0071383 (0.029667) 2025-03-20,09:48:18 | INFO | Train Epoch: 27 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 149.296/s, 149.296/s/gpu LR: 0.000005 Logit Scale: 30.656 Contrastive_loss: 0.00072013 (0.027737) Loss: 0.00072013 (0.027737) 2025-03-20,09:48:40 | INFO | Train Epoch: 27 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 149.166/s, 149.166/s/gpu LR: 0.000005 Logit Scale: 30.660 Contrastive_loss: 0.053911 (0.029373) Loss: 0.053911 (0.029373) 2025-03-20,09:49:01 | INFO | Train Epoch: 27 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 150.003/s, 150.003/s/gpu LR: 0.000005 Logit Scale: 30.661 Contrastive_loss: 0.027733 (0.029277) Loss: 0.027733 (0.029277) 2025-03-20,09:49:23 | INFO | Train Epoch: 27 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 147.381/s, 147.381/s/gpu LR: 0.000005 Logit Scale: 30.663 Contrastive_loss: 8.9174e-05 (0.027655) Loss: 8.9174e-05 (0.027655) 2025-03-20,09:49:44 | INFO | Train Epoch: 27 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.539/s, 149.539/s/gpu LR: 0.000005 Logit Scale: 30.664 Contrastive_loss: 0.044950 (0.028565) Loss: 0.044950 (0.028565) 2025-03-20,09:50:06 | INFO | Train Epoch: 27 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.213, 152.334/s, 152.334/s/gpu LR: 0.000005 Logit Scale: 30.666 Contrastive_loss: 0.014039 (0.027839) Loss: 0.014039 (0.027839) 2025-03-20,09:50:27 | INFO | Train Epoch: 27 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.214, 148.709/s, 148.709/s/gpu LR: 0.000005 Logit Scale: 30.667 Contrastive_loss: 0.0072749 (0.026860) Loss: 0.0072749 (0.026860) 2025-03-20,09:50:49 | INFO | Train Epoch: 27 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 149.483/s, 149.483/s/gpu LR: 0.000005 Logit Scale: 30.669 Contrastive_loss: 0.064552 (0.028573) Loss: 0.064552 (0.028573) 2025-03-20,09:51:10 | INFO | Train Epoch: 27 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.212, 150.644/s, 150.644/s/gpu LR: 0.000005 Logit Scale: 30.670 Contrastive_loss: 0.0090541 (0.027724) Loss: 0.0090541 (0.027724) 2025-03-20,09:51:31 | INFO | Train Epoch: 27 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 148.959/s, 148.959/s/gpu LR: 0.000005 Logit Scale: 30.670 Contrastive_loss: 0.046406 (0.028503) Loss: 0.046406 (0.028503) 2025-03-20,09:51:53 | INFO | Train Epoch: 27 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 149.608/s, 149.608/s/gpu LR: 0.000005 Logit Scale: 30.672 Contrastive_loss: 0.058353 (0.029697) Loss: 0.058353 (0.029697) 2025-03-20,09:52:14 | INFO | Train Epoch: 27 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.214, 149.263/s, 149.263/s/gpu LR: 0.000005 Logit Scale: 30.674 Contrastive_loss: 0.015782 (0.029162) Loss: 0.015782 (0.029162) 2025-03-20,09:52:36 | INFO | Train Epoch: 27 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 149.363/s, 149.363/s/gpu LR: 0.000005 Logit Scale: 30.675 Contrastive_loss: 0.00018590 (0.028088) Loss: 0.00018590 (0.028088) 2025-03-20,09:52:57 | INFO | Train Epoch: 27 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 148.029/s, 148.029/s/gpu LR: 0.000005 Logit Scale: 30.677 Contrastive_loss: 0.059318 (0.029204) Loss: 0.059318 (0.029204) 2025-03-20,09:53:19 | INFO | Train Epoch: 27 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 147.666/s, 147.666/s/gpu LR: 0.000005 Logit Scale: 30.680 Contrastive_loss: 0.044333 (0.029726) Loss: 0.044333 (0.029726) 2025-03-20,09:53:41 | INFO | Train Epoch: 27 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 146.561/s, 146.561/s/gpu LR: 0.000005 Logit Scale: 30.681 Contrastive_loss: 0.021079 (0.029437) Loss: 0.021079 (0.029437) 2025-03-20,09:54:02 | INFO | Train Epoch: 27 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 151.284/s, 151.284/s/gpu LR: 0.000005 Logit Scale: 30.685 Contrastive_loss: 0.066080 (0.030619) Loss: 0.066080 (0.030619) 2025-03-20,09:54:24 | INFO | Train Epoch: 27 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.213, 149.135/s, 149.135/s/gpu LR: 0.000005 Logit Scale: 30.686 Contrastive_loss: 0.054672 (0.031371) Loss: 0.054672 (0.031371) 2025-03-20,09:54:45 | INFO | Train Epoch: 27 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.214, 149.028/s, 149.028/s/gpu LR: 0.000005 Logit Scale: 30.686 Contrastive_loss: 0.093187 (0.033244) Loss: 0.093187 (0.033244) 2025-03-20,09:55:07 | INFO | Train Epoch: 27 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 147.542/s, 147.542/s/gpu LR: 0.000005 Logit Scale: 30.688 Contrastive_loss: 0.00016659 (0.032271) Loss: 0.00016659 (0.032271) 2025-03-20,09:55:28 | INFO | Train Epoch: 27 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.215, 150.222/s, 150.222/s/gpu LR: 0.000005 Logit Scale: 30.690 Contrastive_loss: 0.00064710 (0.031368) Loss: 0.00064710 (0.031368) 2025-03-20,09:55:50 | INFO | Train Epoch: 27 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 147.692/s, 147.692/s/gpu LR: 0.000005 Logit Scale: 30.693 Contrastive_loss: 0.10672 (0.033461) Loss: 0.10672 (0.033461) 2025-03-20,09:56:11 | INFO | Train Epoch: 27 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.298/s, 149.298/s/gpu LR: 0.000005 Logit Scale: 30.695 Contrastive_loss: 0.10816 (0.035480) Loss: 0.10816 (0.035480) 2025-03-20,09:56:33 | INFO | Train Epoch: 27 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.217, 148.586/s, 148.586/s/gpu LR: 0.000005 Logit Scale: 30.693 Contrastive_loss: 0.017802 (0.035015) Loss: 0.017802 (0.035015) 2025-03-20,09:56:55 | INFO | Train Epoch: 27 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.218, 145.730/s, 145.730/s/gpu LR: 0.000005 Logit Scale: 30.693 Contrastive_loss: 0.060968 (0.035680) Loss: 0.060968 (0.035680) 2025-03-20,09:57:17 | INFO | Train Epoch: 27 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.221, 145.893/s, 145.893/s/gpu LR: 0.000005 Logit Scale: 30.696 Contrastive_loss: 0.0058234 (0.034934) Loss: 0.0058234 (0.034934) 2025-03-20,09:57:38 | INFO | Train Epoch: 27 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 149.648/s, 149.648/s/gpu LR: 0.000004 Logit Scale: 30.697 Contrastive_loss: 0.017834 (0.034517) Loss: 0.017834 (0.034517) 2025-03-20,09:58:00 | INFO | Train Epoch: 27 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.214, 148.797/s, 148.797/s/gpu LR: 0.000004 Logit Scale: 30.698 Contrastive_loss: 0.068392 (0.035323) Loss: 0.068392 (0.035323) 2025-03-20,09:58:21 | INFO | Train Epoch: 27 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 149.781/s, 149.781/s/gpu LR: 0.000004 Logit Scale: 30.701 Contrastive_loss: 0.081679 (0.036401) Loss: 0.081679 (0.036401) 2025-03-20,09:58:43 | INFO | Train Epoch: 27 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 148.183/s, 148.183/s/gpu LR: 0.000004 Logit Scale: 30.701 Contrastive_loss: 0.045631 (0.036611) Loss: 0.045631 (0.036611) 2025-03-20,09:59:04 | INFO | Train Epoch: 27 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 148.542/s, 148.542/s/gpu LR: 0.000004 Logit Scale: 30.702 Contrastive_loss: 0.14827 (0.039092) Loss: 0.14827 (0.039092) 2025-03-20,09:59:26 | INFO | Train Epoch: 27 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.214, 149.709/s, 149.709/s/gpu LR: 0.000004 Logit Scale: 30.705 Contrastive_loss: 0.00096971 (0.038264) Loss: 0.00096971 (0.038264) 2025-03-20,09:59:47 | INFO | Train Epoch: 27 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.219, 145.199/s, 145.199/s/gpu LR: 0.000004 Logit Scale: 30.706 Contrastive_loss: 0.0032261 (0.037518) Loss: 0.0032261 (0.037518) 2025-03-20,10:00:10 | INFO | Train Epoch: 27 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.221, 143.719/s, 143.719/s/gpu LR: 0.000004 Logit Scale: 30.707 Contrastive_loss: 0.024377 (0.037244) Loss: 0.024377 (0.037244) 2025-03-20,10:00:31 | INFO | Train Epoch: 27 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.218, 146.239/s, 146.239/s/gpu LR: 0.000004 Logit Scale: 30.711 Contrastive_loss: 0.0051200 (0.036589) Loss: 0.0051200 (0.036589) 2025-03-20,10:00:53 | INFO | Train Epoch: 27 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.214, 148.712/s, 148.712/s/gpu LR: 0.000004 Logit Scale: 30.710 Contrastive_loss: 0.040566 (0.036668) Loss: 0.040566 (0.036668) 2025-03-20,10:01:14 | INFO | Train Epoch: 27 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.213, 149.779/s, 149.779/s/gpu LR: 0.000004 Logit Scale: 30.710 Contrastive_loss: 0.049001 (0.036910) Loss: 0.049001 (0.036910) 2025-03-20,10:01:36 | INFO | Train Epoch: 27 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.214, 149.376/s, 149.376/s/gpu LR: 0.000004 Logit Scale: 30.711 Contrastive_loss: 0.046638 (0.037097) Loss: 0.046638 (0.037097) 2025-03-20,10:01:57 | INFO | Train Epoch: 27 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.816/s, 149.816/s/gpu LR: 0.000004 Logit Scale: 30.714 Contrastive_loss: 0.087337 (0.038045) Loss: 0.087337 (0.038045) 2025-03-20,10:02:18 | INFO | Train Epoch: 27 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.964/s, 149.964/s/gpu LR: 0.000004 Logit Scale: 30.714 Contrastive_loss: 6.6813e-05 (0.037342) Loss: 6.6813e-05 (0.037342) 2025-03-20,10:02:40 | INFO | Train Epoch: 27 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 150.782/s, 150.782/s/gpu LR: 0.000004 Logit Scale: 30.717 Contrastive_loss: 0.044582 (0.037474) Loss: 0.044582 (0.037474) 2025-03-20,10:03:01 | INFO | Train Epoch: 27 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 147.404/s, 147.404/s/gpu LR: 0.000004 Logit Scale: 30.719 Contrastive_loss: 4.1312e-05 (0.036805) Loss: 4.1312e-05 (0.036805) 2025-03-20,10:03:23 | INFO | Train Epoch: 27 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.217, 148.933/s, 148.933/s/gpu LR: 0.000004 Logit Scale: 30.721 Contrastive_loss: 0.098768 (0.037892) Loss: 0.098768 (0.037892) 2025-03-20,10:03:44 | INFO | Train Epoch: 27 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 148.525/s, 148.525/s/gpu LR: 0.000004 Logit Scale: 30.720 Contrastive_loss: 0.049915 (0.038099) Loss: 0.049915 (0.038099) 2025-03-20,10:04:06 | INFO | Train Epoch: 27 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 149.444/s, 149.444/s/gpu LR: 0.000004 Logit Scale: 30.722 Contrastive_loss: 0.043377 (0.038189) Loss: 0.043377 (0.038189) 2025-03-20,10:04:28 | INFO | Train Epoch: 27 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.215, 148.175/s, 148.175/s/gpu LR: 0.000004 Logit Scale: 30.724 Contrastive_loss: 0.032922 (0.038101) Loss: 0.032922 (0.038101) 2025-03-20,10:04:49 | INFO | Train Epoch: 27 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.213, 151.190/s, 151.190/s/gpu LR: 0.000004 Logit Scale: 30.725 Contrastive_loss: 0.00014154 (0.037479) Loss: 0.00014154 (0.037479) 2025-03-20,10:05:10 | INFO | Train Epoch: 27 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.213, 148.665/s, 148.665/s/gpu LR: 0.000004 Logit Scale: 30.726 Contrastive_loss: 0.00011917 (0.036876) Loss: 0.00011917 (0.036876) 2025-03-20,10:05:32 | INFO | Train Epoch: 27 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.214, 149.848/s, 149.848/s/gpu LR: 0.000004 Logit Scale: 30.728 Contrastive_loss: 0.020689 (0.036619) Loss: 0.020689 (0.036619) 2025-03-20,10:05:53 | INFO | Train Epoch: 27 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.213, 149.925/s, 149.925/s/gpu LR: 0.000004 Logit Scale: 30.731 Contrastive_loss: 7.2964e-05 (0.036048) Loss: 7.2964e-05 (0.036048) 2025-03-20,10:06:14 | INFO | Train Epoch: 27 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 150.371/s, 150.371/s/gpu LR: 0.000004 Logit Scale: 30.733 Contrastive_loss: 0.042769 (0.036152) Loss: 0.042769 (0.036152) 2025-03-20,10:06:36 | INFO | Train Epoch: 27 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.214, 152.067/s, 152.067/s/gpu LR: 0.000004 Logit Scale: 30.734 Contrastive_loss: 0.00050737 (0.035612) Loss: 0.00050737 (0.035612) 2025-03-20,10:06:57 | INFO | Train Epoch: 27 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 149.462/s, 149.462/s/gpu LR: 0.000004 Logit Scale: 30.737 Contrastive_loss: 0.056092 (0.035917) Loss: 0.056092 (0.035917) 2025-03-20,10:07:19 | INFO | Train Epoch: 27 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 150.012/s, 150.012/s/gpu LR: 0.000004 Logit Scale: 30.739 Contrastive_loss: 0.043824 (0.036034) Loss: 0.043824 (0.036034) 2025-03-20,10:07:40 | INFO | Train Epoch: 27 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 150.183/s, 150.183/s/gpu LR: 0.000004 Logit Scale: 30.741 Contrastive_loss: 0.22371 (0.038753) Loss: 0.22371 (0.038753) 2025-03-20,10:08:01 | INFO | Train Epoch: 27 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.215, 149.228/s, 149.228/s/gpu LR: 0.000004 Logit Scale: 30.743 Contrastive_loss: 0.0087290 (0.038325) Loss: 0.0087290 (0.038325) 2025-03-20,10:08:23 | INFO | Train Epoch: 27 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.214, 149.820/s, 149.820/s/gpu LR: 0.000004 Logit Scale: 30.745 Contrastive_loss: 0.00017006 (0.037787) Loss: 0.00017006 (0.037787) 2025-03-20,10:08:44 | INFO | Train Epoch: 27 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.215, 149.034/s, 149.034/s/gpu LR: 0.000004 Logit Scale: 30.747 Contrastive_loss: 4.4838e-05 (0.037263) Loss: 4.4838e-05 (0.037263) 2025-03-20,10:09:06 | INFO | Train Epoch: 27 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.214, 150.306/s, 150.306/s/gpu LR: 0.000004 Logit Scale: 30.748 Contrastive_loss: 0.097125 (0.038083) Loss: 0.097125 (0.038083) 2025-03-20,10:09:27 | INFO | Train Epoch: 27 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 151.150/s, 151.150/s/gpu LR: 0.000004 Logit Scale: 30.749 Contrastive_loss: 0.10881 (0.039039) Loss: 0.10881 (0.039039) 2025-03-20,10:09:49 | INFO | Train Epoch: 27 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 149.002/s, 149.002/s/gpu LR: 0.000004 Logit Scale: 30.750 Contrastive_loss: 0.00010979 (0.038520) Loss: 0.00010979 (0.038520) 2025-03-20,10:10:10 | INFO | Train Epoch: 27 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.244/s, 148.244/s/gpu LR: 0.000004 Logit Scale: 30.752 Contrastive_loss: 0.00020958 (0.038016) Loss: 0.00020958 (0.038016) 2025-03-20,10:10:32 | INFO | Train Epoch: 27 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.217, 147.275/s, 147.275/s/gpu LR: 0.000004 Logit Scale: 30.753 Contrastive_loss: 0.018807 (0.037766) Loss: 0.018807 (0.037766) 2025-03-20,10:10:54 | INFO | Train Epoch: 27 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 149.422/s, 149.422/s/gpu LR: 0.000004 Logit Scale: 30.754 Contrastive_loss: 0.051124 (0.037937) Loss: 0.051124 (0.037937) 2025-03-20,10:11:15 | INFO | Train Epoch: 27 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 150.013/s, 150.013/s/gpu LR: 0.000004 Logit Scale: 30.755 Contrastive_loss: 0.053824 (0.038138) Loss: 0.053824 (0.038138) 2025-03-20,10:11:36 | INFO | Train Epoch: 27 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.214, 149.862/s, 149.862/s/gpu LR: 0.000004 Logit Scale: 30.757 Contrastive_loss: 0.092266 (0.038815) Loss: 0.092266 (0.038815) 2025-03-20,10:11:58 | INFO | Train Epoch: 27 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.215, 148.421/s, 148.421/s/gpu LR: 0.000004 Logit Scale: 30.760 Contrastive_loss: 0.034091 (0.038757) Loss: 0.034091 (0.038757) 2025-03-20,10:12:19 | INFO | Train Epoch: 27 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 149.791/s, 149.791/s/gpu LR: 0.000004 Logit Scale: 30.760 Contrastive_loss: 0.00015920 (0.038286) Loss: 0.00015920 (0.038286) 2025-03-20,10:12:41 | INFO | Train Epoch: 27 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.215, 150.033/s, 150.033/s/gpu LR: 0.000004 Logit Scale: 30.761 Contrastive_loss: 0.036678 (0.038267) Loss: 0.036678 (0.038267) 2025-03-20,10:13:02 | INFO | Train Epoch: 27 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.486/s, 148.486/s/gpu LR: 0.000004 Logit Scale: 30.761 Contrastive_loss: 0.00049072 (0.037817) Loss: 0.00049072 (0.037817) 2025-03-20,10:13:24 | INFO | Train Epoch: 27 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.216, 150.354/s, 150.354/s/gpu LR: 0.000004 Logit Scale: 30.763 Contrastive_loss: 0.00012576 (0.037374) Loss: 0.00012576 (0.037374) 2025-03-20,10:13:45 | INFO | Train Epoch: 27 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 148.460/s, 148.460/s/gpu LR: 0.000004 Logit Scale: 30.761 Contrastive_loss: 0.10254 (0.038131) Loss: 0.10254 (0.038131) 2025-03-20,10:14:07 | INFO | Train Epoch: 27 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.214, 148.987/s, 148.987/s/gpu LR: 0.000004 Logit Scale: 30.759 Contrastive_loss: 0.0021841 (0.037718) Loss: 0.0021841 (0.037718) 2025-03-20,10:14:28 | INFO | Train Epoch: 27 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.575/s, 149.575/s/gpu LR: 0.000004 Logit Scale: 30.757 Contrastive_loss: 0.045802 (0.037810) Loss: 0.045802 (0.037810) 2025-03-20,10:14:50 | INFO | Train Epoch: 27 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 149.240/s, 149.240/s/gpu LR: 0.000004 Logit Scale: 30.758 Contrastive_loss: 0.046804 (0.037911) Loss: 0.046804 (0.037911) 2025-03-20,10:15:11 | INFO | Train Epoch: 27 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.217, 146.686/s, 146.686/s/gpu LR: 0.000004 Logit Scale: 30.759 Contrastive_loss: 0.098071 (0.038579) Loss: 0.098071 (0.038579) 2025-03-20,10:15:33 | INFO | Train Epoch: 27 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.219, 145.289/s, 145.289/s/gpu LR: 0.000004 Logit Scale: 30.758 Contrastive_loss: 0.051777 (0.038724) Loss: 0.051777 (0.038724) 2025-03-20,10:15:55 | INFO | Train Epoch: 27 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.219, 151.061/s, 151.061/s/gpu LR: 0.000004 Logit Scale: 30.759 Contrastive_loss: 0.063061 (0.038989) Loss: 0.063061 (0.038989) 2025-03-20,10:16:17 | INFO | Train Epoch: 27 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.218, 150.033/s, 150.033/s/gpu LR: 0.000004 Logit Scale: 30.761 Contrastive_loss: 0.00023849 (0.038572) Loss: 0.00023849 (0.038572) 2025-03-20,10:16:38 | INFO | Train Epoch: 27 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.213, 150.771/s, 150.771/s/gpu LR: 0.000004 Logit Scale: 30.761 Contrastive_loss: 0.013433 (0.038305) Loss: 0.013433 (0.038305) 2025-03-20,10:17:00 | INFO | Train Epoch: 27 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.214, 150.000/s, 150.000/s/gpu LR: 0.000004 Logit Scale: 30.762 Contrastive_loss: 0.091507 (0.038865) Loss: 0.091507 (0.038865) 2025-03-20,10:17:21 | INFO | Train Epoch: 27 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.217, 143.056/s, 143.056/s/gpu LR: 0.000004 Logit Scale: 30.764 Contrastive_loss: 0.0015757 (0.038476) Loss: 0.0015757 (0.038476) 2025-03-20,10:17:44 | INFO | Train Epoch: 27 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.221, 147.448/s, 147.448/s/gpu LR: 0.000004 Logit Scale: 30.763 Contrastive_loss: 0.026947 (0.038358) Loss: 0.026947 (0.038358) 2025-03-20,10:18:05 | INFO | Train Epoch: 27 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.219, 140.751/s, 140.751/s/gpu LR: 0.000004 Logit Scale: 30.763 Contrastive_loss: 0.16261 (0.039626) Loss: 0.16261 (0.039626) 2025-03-20,10:18:28 | INFO | Train Epoch: 27 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.222, 146.823/s, 146.823/s/gpu LR: 0.000004 Logit Scale: 30.763 Contrastive_loss: 0.00026077 (0.039228) Loss: 0.00026077 (0.039228) 2025-03-20,10:18:50 | INFO | Train Epoch: 27 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.221, 146.669/s, 146.669/s/gpu LR: 0.000004 Logit Scale: 30.764 Contrastive_loss: 0.0063789 (0.038899) Loss: 0.0063789 (0.038899) 2025-03-20,10:19:11 | INFO | Train Epoch: 27 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 148.577/s, 148.577/s/gpu LR: 0.000004 Logit Scale: 30.764 Contrastive_loss: 0.062152 (0.039130) Loss: 0.062152 (0.039130) 2025-03-20,10:19:34 | INFO | Train Epoch: 27 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.223, 145.896/s, 145.896/s/gpu LR: 0.000004 Logit Scale: 30.765 Contrastive_loss: 0.0080821 (0.038825) Loss: 0.0080821 (0.038825) 2025-03-20,10:19:55 | INFO | Train Epoch: 27 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.005/s, 149.005/s/gpu LR: 0.000004 Logit Scale: 30.768 Contrastive_loss: 0.00011965 (0.038449) Loss: 0.00011965 (0.038449) 2025-03-20,10:20:17 | INFO | Train Epoch: 27 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 151.682/s, 151.682/s/gpu LR: 0.000004 Logit Scale: 30.768 Contrastive_loss: 0.0011903 (0.038091) Loss: 0.0011903 (0.038091) 2025-03-20,10:20:38 | INFO | Train Epoch: 27 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.214, 148.963/s, 148.963/s/gpu LR: 0.000004 Logit Scale: 30.771 Contrastive_loss: 0.0011668 (0.037740) Loss: 0.0011668 (0.037740) 2025-03-20,10:21:00 | INFO | Train Epoch: 27 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 151.749/s, 151.749/s/gpu LR: 0.000004 Logit Scale: 30.772 Contrastive_loss: 0.00067559 (0.037390) Loss: 0.00067559 (0.037390) 2025-03-20,10:21:21 | INFO | Train Epoch: 27 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.214, 148.292/s, 148.292/s/gpu LR: 0.000004 Logit Scale: 30.774 Contrastive_loss: 0.029782 (0.037319) Loss: 0.029782 (0.037319) 2025-03-20,10:21:42 | INFO | Train Epoch: 27 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 148.866/s, 148.866/s/gpu LR: 0.000004 Logit Scale: 30.774 Contrastive_loss: 0.00020343 (0.036975) Loss: 0.00020343 (0.036975) 2025-03-20,10:22:04 | INFO | Train Epoch: 27 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.216, 150.136/s, 150.136/s/gpu LR: 0.000004 Logit Scale: 30.775 Contrastive_loss: 0.045311 (0.037052) Loss: 0.045311 (0.037052) 2025-03-20,10:22:26 | INFO | Train Epoch: 27 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.217, 149.714/s, 149.714/s/gpu LR: 0.000004 Logit Scale: 30.776 Contrastive_loss: 0.062659 (0.037284) Loss: 0.062659 (0.037284) 2025-03-20,10:22:47 | INFO | Train Epoch: 27 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.509/s, 149.509/s/gpu LR: 0.000004 Logit Scale: 30.777 Contrastive_loss: 0.025836 (0.037181) Loss: 0.025836 (0.037181) 2025-03-20,10:23:09 | INFO | Train Epoch: 27 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.683/s, 149.683/s/gpu LR: 0.000004 Logit Scale: 30.777 Contrastive_loss: 0.00014058 (0.036851) Loss: 0.00014058 (0.036851) 2025-03-20,10:23:30 | INFO | Train Epoch: 27 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 145.346/s, 145.346/s/gpu LR: 0.000004 Logit Scale: 30.778 Contrastive_loss: 0.043613 (0.036910) Loss: 0.043613 (0.036910) 2025-03-20,10:23:52 | INFO | Train Epoch: 27 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.217, 149.480/s, 149.480/s/gpu LR: 0.000004 Logit Scale: 30.779 Contrastive_loss: 0.00025318 (0.036589) Loss: 0.00025318 (0.036589) 2025-03-20,10:24:13 | INFO | Train Epoch: 27 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.720/s, 149.720/s/gpu LR: 0.000004 Logit Scale: 30.779 Contrastive_loss: 0.020228 (0.036447) Loss: 0.020228 (0.036447) 2025-03-20,10:24:35 | INFO | Train Epoch: 27 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 150.013/s, 150.013/s/gpu LR: 0.000004 Logit Scale: 30.780 Contrastive_loss: 0.00041997 (0.036136) Loss: 0.00041997 (0.036136) 2025-03-20,10:24:56 | INFO | Train Epoch: 27 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.215, 149.349/s, 149.349/s/gpu LR: 0.000004 Logit Scale: 30.780 Contrastive_loss: 0.00065196 (0.035833) Loss: 0.00065196 (0.035833) 2025-03-20,10:25:18 | INFO | Train Epoch: 27 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 148.562/s, 148.562/s/gpu LR: 0.000004 Logit Scale: 30.780 Contrastive_loss: 1.1837e-05 (0.035529) Loss: 1.1837e-05 (0.035529) 2025-03-20,10:25:39 | INFO | Train Epoch: 27 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.215, 149.283/s, 149.283/s/gpu LR: 0.000004 Logit Scale: 30.782 Contrastive_loss: 0.0014525 (0.035243) Loss: 0.0014525 (0.035243) 2025-03-20,10:26:01 | INFO | Train Epoch: 27 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 148.687/s, 148.687/s/gpu LR: 0.000004 Logit Scale: 30.782 Contrastive_loss: 0.011100 (0.035042) Loss: 0.011100 (0.035042) 2025-03-20,10:26:22 | INFO | Train Epoch: 27 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 149.649/s, 149.649/s/gpu LR: 0.000004 Logit Scale: 30.784 Contrastive_loss: 0.056738 (0.035221) Loss: 0.056738 (0.035221) 2025-03-20,10:26:44 | INFO | Train Epoch: 27 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.514/s, 149.514/s/gpu LR: 0.000003 Logit Scale: 30.785 Contrastive_loss: 0.00064072 (0.034937) Loss: 0.00064072 (0.034937) 2025-03-20,10:27:05 | INFO | Train Epoch: 27 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.174/s, 149.174/s/gpu LR: 0.000003 Logit Scale: 30.784 Contrastive_loss: 0.00084570 (0.034660) Loss: 0.00084570 (0.034660) 2025-03-20,10:27:27 | INFO | Train Epoch: 27 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.215, 149.479/s, 149.479/s/gpu LR: 0.000003 Logit Scale: 30.784 Contrastive_loss: 0.010967 (0.034469) Loss: 0.010967 (0.034469) 2025-03-20,10:27:48 | INFO | Train Epoch: 27 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.214, 149.612/s, 149.612/s/gpu LR: 0.000003 Logit Scale: 30.785 Contrastive_loss: 0.00015909 (0.034195) Loss: 0.00015909 (0.034195) 2025-03-20,10:28:10 | INFO | Train Epoch: 27 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 148.785/s, 148.785/s/gpu LR: 0.000003 Logit Scale: 30.784 Contrastive_loss: 0.00028854 (0.033926) Loss: 0.00028854 (0.033926) 2025-03-20,10:28:31 | INFO | Train Epoch: 27 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 150.820/s, 150.820/s/gpu LR: 0.000003 Logit Scale: 30.785 Contrastive_loss: 6.0091e-05 (0.033659) Loss: 6.0091e-05 (0.033659) 2025-03-20,10:28:53 | INFO | Train Epoch: 27 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.215, 144.337/s, 144.337/s/gpu LR: 0.000003 Logit Scale: 30.787 Contrastive_loss: 0.15208 (0.034584) Loss: 0.15208 (0.034584) 2025-03-20,10:29:14 | INFO | Train Epoch: 27 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.753/s, 148.753/s/gpu LR: 0.000003 Logit Scale: 30.789 Contrastive_loss: 0.00026034 (0.034318) Loss: 0.00026034 (0.034318) 2025-03-20,10:29:36 | INFO | Train Epoch: 27 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 149.772/s, 149.772/s/gpu LR: 0.000003 Logit Scale: 30.791 Contrastive_loss: 0.17266 (0.035382) Loss: 0.17266 (0.035382) 2025-03-20,10:29:57 | INFO | Train Epoch: 27 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 145.858/s, 145.858/s/gpu LR: 0.000003 Logit Scale: 30.790 Contrastive_loss: 0.15794 (0.036318) Loss: 0.15794 (0.036318) 2025-03-20,10:30:19 | INFO | Train Epoch: 27 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 147.636/s, 147.636/s/gpu LR: 0.000003 Logit Scale: 30.790 Contrastive_loss: 0.011745 (0.036132) Loss: 0.011745 (0.036132) 2025-03-20,10:30:41 | INFO | Train Epoch: 27 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.217, 148.128/s, 148.128/s/gpu LR: 0.000003 Logit Scale: 30.790 Contrastive_loss: 0.065077 (0.036349) Loss: 0.065077 (0.036349) 2025-03-20,10:31:02 | INFO | Train Epoch: 27 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 149.872/s, 149.872/s/gpu LR: 0.000003 Logit Scale: 30.792 Contrastive_loss: 0.028447 (0.036290) Loss: 0.028447 (0.036290) 2025-03-20,10:31:24 | INFO | Train Epoch: 27 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 149.934/s, 149.934/s/gpu LR: 0.000003 Logit Scale: 30.793 Contrastive_loss: 0.00024046 (0.036023) Loss: 0.00024046 (0.036023) 2025-03-20,10:31:45 | INFO | Train Epoch: 27 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.216, 148.500/s, 148.500/s/gpu LR: 0.000003 Logit Scale: 30.794 Contrastive_loss: 0.17350 (0.037034) Loss: 0.17350 (0.037034) 2025-03-20,10:32:07 | INFO | Train Epoch: 27 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 149.637/s, 149.637/s/gpu LR: 0.000003 Logit Scale: 30.794 Contrastive_loss: 0.00047781 (0.036767) Loss: 0.00047781 (0.036767) 2025-03-20,10:32:28 | INFO | Train Epoch: 27 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.216, 148.455/s, 148.455/s/gpu LR: 0.000003 Logit Scale: 30.795 Contrastive_loss: 0.047874 (0.036848) Loss: 0.047874 (0.036848) 2025-03-20,10:32:50 | INFO | Train Epoch: 27 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.305/s, 148.305/s/gpu LR: 0.000003 Logit Scale: 30.795 Contrastive_loss: 0.14087 (0.037596) Loss: 0.14087 (0.037596) 2025-03-20,10:33:12 | INFO | Train Epoch: 27 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 146.157/s, 146.157/s/gpu LR: 0.000003 Logit Scale: 30.794 Contrastive_loss: 0.00020082 (0.037329) Loss: 0.00020082 (0.037329) 2025-03-20,10:33:33 | INFO | Train Epoch: 27 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.217, 149.077/s, 149.077/s/gpu LR: 0.000003 Logit Scale: 30.795 Contrastive_loss: 0.11925 (0.037910) Loss: 0.11925 (0.037910) 2025-03-20,10:33:55 | INFO | Train Epoch: 27 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 149.108/s, 149.108/s/gpu LR: 0.000003 Logit Scale: 30.796 Contrastive_loss: 0.00097625 (0.037650) Loss: 0.00097625 (0.037650) 2025-03-20,10:34:16 | INFO | Train Epoch: 27 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.216, 148.555/s, 148.555/s/gpu LR: 0.000003 Logit Scale: 30.797 Contrastive_loss: 0.082327 (0.037962) Loss: 0.082327 (0.037962) 2025-03-20,10:34:38 | INFO | Train Epoch: 27 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.217, 149.083/s, 149.083/s/gpu LR: 0.000003 Logit Scale: 30.798 Contrastive_loss: 0.0017033 (0.037711) Loss: 0.0017033 (0.037711) 2025-03-20,10:35:00 | INFO | Train Epoch: 27 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.215, 149.179/s, 149.179/s/gpu LR: 0.000003 Logit Scale: 30.800 Contrastive_loss: 0.038578 (0.037716) Loss: 0.038578 (0.037716) 2025-03-20,10:35:21 | INFO | Train Epoch: 27 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 149.646/s, 149.646/s/gpu LR: 0.000003 Logit Scale: 30.802 Contrastive_loss: 0.098702 (0.038134) Loss: 0.098702 (0.038134) 2025-03-20,10:35:43 | INFO | Train Epoch: 27 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 147.750/s, 147.750/s/gpu LR: 0.000003 Logit Scale: 30.803 Contrastive_loss: 0.0040501 (0.037902) Loss: 0.0040501 (0.037902) 2025-03-20,10:36:04 | INFO | Train Epoch: 27 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.144/s, 149.144/s/gpu LR: 0.000003 Logit Scale: 30.804 Contrastive_loss: 0.033463 (0.037872) Loss: 0.033463 (0.037872) 2025-03-20,10:36:26 | INFO | Train Epoch: 27 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.562/s, 149.562/s/gpu LR: 0.000003 Logit Scale: 30.806 Contrastive_loss: 0.023945 (0.037779) Loss: 0.023945 (0.037779) 2025-03-20,10:36:47 | INFO | Train Epoch: 27 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 149.366/s, 149.366/s/gpu LR: 0.000003 Logit Scale: 30.809 Contrastive_loss: 0.0012768 (0.037536) Loss: 0.0012768 (0.037536) 2025-03-20,10:37:09 | INFO | Train Epoch: 27 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 147.462/s, 147.462/s/gpu LR: 0.000003 Logit Scale: 30.810 Contrastive_loss: 0.051672 (0.037629) Loss: 0.051672 (0.037629) 2025-03-20,10:37:30 | INFO | Train Epoch: 27 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.440/s, 148.440/s/gpu LR: 0.000003 Logit Scale: 30.809 Contrastive_loss: 0.0028631 (0.037400) Loss: 0.0028631 (0.037400) 2025-03-20,10:37:52 | INFO | Train Epoch: 27 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 149.525/s, 149.525/s/gpu LR: 0.000003 Logit Scale: 30.810 Contrastive_loss: 0.00053100 (0.037159) Loss: 0.00053100 (0.037159) 2025-03-20,10:38:14 | INFO | Train Epoch: 27 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.217, 147.422/s, 147.422/s/gpu LR: 0.000003 Logit Scale: 30.809 Contrastive_loss: 0.013458 (0.037006) Loss: 0.013458 (0.037006) 2025-03-20,10:38:35 | INFO | Train Epoch: 27 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.328/s, 148.328/s/gpu LR: 0.000003 Logit Scale: 30.808 Contrastive_loss: 0.032940 (0.036979) Loss: 0.032940 (0.036979) 2025-03-20,10:38:57 | INFO | Train Epoch: 27 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 149.328/s, 149.328/s/gpu LR: 0.000003 Logit Scale: 30.809 Contrastive_loss: 0.00077225 (0.036747) Loss: 0.00077225 (0.036747) 2025-03-20,10:39:19 | INFO | Train Epoch: 27 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.218, 148.394/s, 148.394/s/gpu LR: 0.000003 Logit Scale: 30.811 Contrastive_loss: 0.024540 (0.036669) Loss: 0.024540 (0.036669) 2025-03-20,10:39:40 | INFO | Train Epoch: 27 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.218, 146.620/s, 146.620/s/gpu LR: 0.000003 Logit Scale: 30.811 Contrastive_loss: 0.042909 (0.036709) Loss: 0.042909 (0.036709) 2025-03-20,10:40:02 | INFO | Train Epoch: 27 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.220, 146.124/s, 146.124/s/gpu LR: 0.000003 Logit Scale: 30.812 Contrastive_loss: 0.00043455 (0.036481) Loss: 0.00043455 (0.036481) 2025-03-20,10:40:24 | INFO | Train Epoch: 27 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.219, 145.900/s, 145.900/s/gpu LR: 0.000003 Logit Scale: 30.814 Contrastive_loss: 0.036184 (0.036479) Loss: 0.036184 (0.036479) 2025-03-20,10:40:46 | INFO | Train Epoch: 27 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.222, 143.494/s, 143.494/s/gpu LR: 0.000003 Logit Scale: 30.816 Contrastive_loss: 0.025258 (0.036409) Loss: 0.025258 (0.036409) 2025-03-20,10:41:08 | INFO | Train Epoch: 27 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.220, 151.450/s, 151.450/s/gpu LR: 0.000003 Logit Scale: 30.816 Contrastive_loss: 0.048932 (0.036487) Loss: 0.048932 (0.036487) 2025-03-20,10:41:30 | INFO | Train Epoch: 27 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 151.039/s, 151.039/s/gpu LR: 0.000003 Logit Scale: 30.816 Contrastive_loss: 0.0055121 (0.036297) Loss: 0.0055121 (0.036297) 2025-03-20,10:41:51 | INFO | Train Epoch: 27 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.214, 149.035/s, 149.035/s/gpu LR: 0.000003 Logit Scale: 30.818 Contrastive_loss: 7.4493e-05 (0.036076) Loss: 7.4493e-05 (0.036076) 2025-03-20,10:42:13 | INFO | Train Epoch: 27 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 149.943/s, 149.943/s/gpu LR: 0.000003 Logit Scale: 30.818 Contrastive_loss: 0.010889 (0.035923) Loss: 0.010889 (0.035923) 2025-03-20,10:42:34 | INFO | Train Epoch: 27 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 148.368/s, 148.368/s/gpu LR: 0.000003 Logit Scale: 30.819 Contrastive_loss: 0.044285 (0.035973) Loss: 0.044285 (0.035973) 2025-03-20,10:42:55 | INFO | Train Epoch: 27 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.214, 149.407/s, 149.407/s/gpu LR: 0.000003 Logit Scale: 30.819 Contrastive_loss: 0.0049280 (0.035787) Loss: 0.0049280 (0.035787) 2025-03-20,10:43:17 | INFO | Train Epoch: 27 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.215, 147.695/s, 147.695/s/gpu LR: 0.000003 Logit Scale: 30.821 Contrastive_loss: 0.0083889 (0.035624) Loss: 0.0083889 (0.035624) 2025-03-20,10:43:38 | INFO | Train Epoch: 27 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.214, 151.152/s, 151.152/s/gpu LR: 0.000003 Logit Scale: 30.822 Contrastive_loss: 0.048193 (0.035699) Loss: 0.048193 (0.035699) 2025-03-20,10:44:00 | INFO | Train Epoch: 27 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.213, 147.068/s, 147.068/s/gpu LR: 0.000003 Logit Scale: 30.823 Contrastive_loss: 0.0046870 (0.035516) Loss: 0.0046870 (0.035516) 2025-03-20,10:44:21 | INFO | Train Epoch: 27 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 150.115/s, 150.115/s/gpu LR: 0.000003 Logit Scale: 30.823 Contrastive_loss: 0.00027137 (0.035310) Loss: 0.00027137 (0.035310) 2025-03-20,10:44:43 | INFO | Train Epoch: 27 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.218, 144.485/s, 144.485/s/gpu LR: 0.000003 Logit Scale: 30.823 Contrastive_loss: 0.028087 (0.035268) Loss: 0.028087 (0.035268) 2025-03-20,10:45:05 | INFO | Train Epoch: 27 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 143.849/s, 143.849/s/gpu LR: 0.000003 Logit Scale: 30.825 Contrastive_loss: 0.0019879 (0.035076) Loss: 0.0019879 (0.035076) 2025-03-20,10:45:27 | INFO | Train Epoch: 27 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.221, 143.214/s, 143.214/s/gpu LR: 0.000003 Logit Scale: 30.825 Contrastive_loss: 0.029058 (0.035041) Loss: 0.029058 (0.035041) 2025-03-20,10:45:49 | INFO | Train Epoch: 27 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.219, 144.262/s, 144.262/s/gpu LR: 0.000003 Logit Scale: 30.825 Contrastive_loss: 6.5621e-05 (0.034841) Loss: 6.5621e-05 (0.034841) 2025-03-20,10:46:11 | INFO | Train Epoch: 27 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.221, 144.714/s, 144.714/s/gpu LR: 0.000003 Logit Scale: 30.825 Contrastive_loss: 0.00048128 (0.034646) Loss: 0.00048128 (0.034646) 2025-03-20,10:46:33 | INFO | Train Epoch: 27 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.222, 144.010/s, 144.010/s/gpu LR: 0.000003 Logit Scale: 30.826 Contrastive_loss: 5.9713e-05 (0.034451) Loss: 5.9713e-05 (0.034451) 2025-03-20,10:46:55 | INFO | Train Epoch: 27 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 141.573/s, 141.573/s/gpu LR: 0.000003 Logit Scale: 30.828 Contrastive_loss: 0.0026354 (0.034272) Loss: 0.0026354 (0.034272) 2025-03-20,10:47:18 | INFO | Train Epoch: 27 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.227, 140.160/s, 140.160/s/gpu LR: 0.000003 Logit Scale: 30.830 Contrastive_loss: 0.11153 (0.034704) Loss: 0.11153 (0.034704) 2025-03-20,10:47:39 | INFO | Train Epoch: 27 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 152.074/s, 152.074/s/gpu LR: 0.000003 Logit Scale: 30.831 Contrastive_loss: 0.043933 (0.034755) Loss: 0.043933 (0.034755) 2025-03-20,10:48:00 | INFO | Train Epoch: 27 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.212, 150.137/s, 150.137/s/gpu LR: 0.000003 Logit Scale: 30.832 Contrastive_loss: 0.00011089 (0.034564) Loss: 0.00011089 (0.034564) 2025-03-20,10:48:21 | INFO | Train Epoch: 27 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.212, 150.706/s, 150.706/s/gpu LR: 0.000003 Logit Scale: 30.830 Contrastive_loss: 0.00012146 (0.034374) Loss: 0.00012146 (0.034374) 2025-03-20,10:48:43 | INFO | Train Epoch: 27 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.213, 145.397/s, 145.397/s/gpu LR: 0.000003 Logit Scale: 30.832 Contrastive_loss: 5.8968e-05 (0.034187) Loss: 5.8968e-05 (0.034187) 2025-03-20,10:49:04 | INFO | Train Epoch: 27 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 147.010/s, 147.010/s/gpu LR: 0.000003 Logit Scale: 30.832 Contrastive_loss: 0.047703 (0.034260) Loss: 0.047703 (0.034260) 2025-03-20,10:49:25 | INFO | Train Epoch: 27 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.213, 150.936/s, 150.936/s/gpu LR: 0.000003 Logit Scale: 30.833 Contrastive_loss: 0.0037643 (0.034095) Loss: 0.0037643 (0.034095) 2025-03-20,10:49:47 | INFO | Train Epoch: 27 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 151.992/s, 151.992/s/gpu LR: 0.000003 Logit Scale: 30.836 Contrastive_loss: 0.055542 (0.034211) Loss: 0.055542 (0.034211) 2025-03-20,10:50:08 | INFO | Train Epoch: 27 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.212, 151.008/s, 151.008/s/gpu LR: 0.000003 Logit Scale: 30.837 Contrastive_loss: 0.056530 (0.034330) Loss: 0.056530 (0.034330) 2025-03-20,10:50:29 | INFO | Train Epoch: 27 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 151.566/s, 151.566/s/gpu LR: 0.000003 Logit Scale: 30.838 Contrastive_loss: 0.043242 (0.034377) Loss: 0.043242 (0.034377) 2025-03-20,10:50:51 | INFO | Train Epoch: 27 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 149.909/s, 149.909/s/gpu LR: 0.000003 Logit Scale: 30.839 Contrastive_loss: 0.00032958 (0.034197) Loss: 0.00032958 (0.034197) 2025-03-20,10:51:12 | INFO | Train Epoch: 27 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.215, 148.460/s, 148.460/s/gpu LR: 0.000003 Logit Scale: 30.840 Contrastive_loss: 9.0480e-05 (0.034018) Loss: 9.0480e-05 (0.034018) 2025-03-20,10:51:34 | INFO | Train Epoch: 27 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 150.410/s, 150.410/s/gpu LR: 0.000003 Logit Scale: 30.841 Contrastive_loss: 0.046487 (0.034083) Loss: 0.046487 (0.034083) 2025-03-20,10:51:55 | INFO | Train Epoch: 27 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.213, 149.995/s, 149.995/s/gpu LR: 0.000003 Logit Scale: 30.841 Contrastive_loss: 0.014961 (0.033983) Loss: 0.014961 (0.033983) 2025-03-20,10:52:16 | INFO | Train Epoch: 27 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.213, 149.244/s, 149.244/s/gpu LR: 0.000003 Logit Scale: 30.842 Contrastive_loss: 0.017760 (0.033899) Loss: 0.017760 (0.033899) 2025-03-20,10:52:38 | INFO | Train Epoch: 27 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.213, 150.185/s, 150.185/s/gpu LR: 0.000003 Logit Scale: 30.842 Contrastive_loss: 0.12279 (0.034358) Loss: 0.12279 (0.034358) 2025-03-20,10:52:59 | INFO | Train Epoch: 27 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.421/s, 148.421/s/gpu LR: 0.000003 Logit Scale: 30.843 Contrastive_loss: 0.096894 (0.034678) Loss: 0.096894 (0.034678) 2025-03-20,10:53:21 | INFO | Train Epoch: 27 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 149.663/s, 149.663/s/gpu LR: 0.000003 Logit Scale: 30.843 Contrastive_loss: 0.044336 (0.034728) Loss: 0.044336 (0.034728) 2025-03-20,10:53:42 | INFO | Train Epoch: 27 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.218, 144.738/s, 144.738/s/gpu LR: 0.000003 Logit Scale: 30.843 Contrastive_loss: 0.094106 (0.035029) Loss: 0.094106 (0.035029) 2025-03-20,10:54:04 | INFO | Train Epoch: 27 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.216, 152.763/s, 152.763/s/gpu LR: 0.000003 Logit Scale: 30.844 Contrastive_loss: 0.0039545 (0.034872) Loss: 0.0039545 (0.034872) 2025-03-20,10:54:25 | INFO | Train Epoch: 27 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.213, 146.089/s, 146.089/s/gpu LR: 0.000003 Logit Scale: 30.845 Contrastive_loss: 0.011489 (0.034755) Loss: 0.011489 (0.034755) 2025-03-20,10:54:47 | INFO | Train Epoch: 27 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.218, 149.108/s, 149.108/s/gpu LR: 0.000003 Logit Scale: 30.847 Contrastive_loss: 0.0063235 (0.034612) Loss: 0.0063235 (0.034612) 2025-03-20,10:55:09 | INFO | Train Epoch: 27 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.213, 151.209/s, 151.209/s/gpu LR: 0.000003 Logit Scale: 30.848 Contrastive_loss: 0.048881 (0.034683) Loss: 0.048881 (0.034683) 2025-03-20,10:55:30 | INFO | Train Epoch: 27 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.215, 148.613/s, 148.613/s/gpu LR: 0.000003 Logit Scale: 30.848 Contrastive_loss: 0.00025791 (0.034513) Loss: 0.00025791 (0.034513) 2025-03-20,10:55:51 | INFO | Train Epoch: 27 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 148.995/s, 148.995/s/gpu LR: 0.000003 Logit Scale: 30.847 Contrastive_loss: 0.12136 (0.034941) Loss: 0.12136 (0.034941) 2025-03-20,10:56:13 | INFO | Train Epoch: 27 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 151.182/s, 151.182/s/gpu LR: 0.000003 Logit Scale: 30.849 Contrastive_loss: 0.0032711 (0.034786) Loss: 0.0032711 (0.034786) 2025-03-20,10:56:34 | INFO | Train Epoch: 27 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 149.905/s, 149.905/s/gpu LR: 0.000003 Logit Scale: 30.851 Contrastive_loss: 0.011210 (0.034671) Loss: 0.011210 (0.034671) 2025-03-20,10:56:56 | INFO | Train Epoch: 27 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.217, 147.375/s, 147.375/s/gpu LR: 0.000003 Logit Scale: 30.852 Contrastive_loss: 0.0060142 (0.034531) Loss: 0.0060142 (0.034531) 2025-03-20,10:57:18 | INFO | Train Epoch: 27 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.507/s, 149.507/s/gpu LR: 0.000003 Logit Scale: 30.853 Contrastive_loss: 0.044411 (0.034579) Loss: 0.044411 (0.034579) 2025-03-20,10:57:39 | INFO | Train Epoch: 27 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 147.080/s, 147.080/s/gpu LR: 0.000003 Logit Scale: 30.853 Contrastive_loss: 0.059155 (0.034697) Loss: 0.059155 (0.034697) 2025-03-20,10:58:01 | INFO | Train Epoch: 27 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.219, 147.978/s, 147.978/s/gpu LR: 0.000003 Logit Scale: 30.854 Contrastive_loss: 0.038180 (0.034714) Loss: 0.038180 (0.034714) 2025-03-20,10:58:23 | INFO | Train Epoch: 27 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 149.482/s, 149.482/s/gpu LR: 0.000003 Logit Scale: 30.855 Contrastive_loss: 0.056616 (0.034818) Loss: 0.056616 (0.034818) 2025-03-20,10:58:44 | INFO | Train Epoch: 27 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.212, 148.563/s, 148.563/s/gpu LR: 0.000003 Logit Scale: 30.856 Contrastive_loss: 0.070037 (0.034985) Loss: 0.070037 (0.034985) 2025-03-20,10:59:05 | INFO | Train Epoch: 27 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.215, 148.977/s, 148.977/s/gpu LR: 0.000003 Logit Scale: 30.858 Contrastive_loss: 9.9691e-05 (0.034821) Loss: 9.9691e-05 (0.034821) 2025-03-20,10:59:27 | INFO | Train Epoch: 27 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 149.682/s, 149.682/s/gpu LR: 0.000003 Logit Scale: 30.860 Contrastive_loss: 0.098855 (0.035121) Loss: 0.098855 (0.035121) 2025-03-20,10:59:49 | INFO | Train Epoch: 27 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 148.136/s, 148.136/s/gpu LR: 0.000003 Logit Scale: 30.859 Contrastive_loss: 0.0071527 (0.034991) Loss: 0.0071527 (0.034991) 2025-03-20,11:00:10 | INFO | Train Epoch: 27 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 147.983/s, 147.983/s/gpu LR: 0.000002 Logit Scale: 30.861 Contrastive_loss: 0.11519 (0.035364) Loss: 0.11519 (0.035364) 2025-03-20,11:00:32 | INFO | Train Epoch: 27 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.219, 149.108/s, 149.108/s/gpu LR: 0.000002 Logit Scale: 30.862 Contrastive_loss: 0.055853 (0.035458) Loss: 0.055853 (0.035458) 2025-03-20,11:00:54 | INFO | Train Epoch: 27 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 145.664/s, 145.664/s/gpu LR: 0.000002 Logit Scale: 30.862 Contrastive_loss: 0.0062106 (0.035324) Loss: 0.0062106 (0.035324) 2025-03-20,11:01:16 | INFO | Train Epoch: 27 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.218, 151.615/s, 151.615/s/gpu LR: 0.000002 Logit Scale: 30.863 Contrastive_loss: 0.00010365 (0.035162) Loss: 0.00010365 (0.035162) 2025-03-20,11:01:37 | INFO | Train Epoch: 27 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.392/s, 149.392/s/gpu LR: 0.000002 Logit Scale: 30.864 Contrastive_loss: 0.00041958 (0.035003) Loss: 0.00041958 (0.035003) 2025-03-20,11:01:59 | INFO | Train Epoch: 27 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.215, 149.151/s, 149.151/s/gpu LR: 0.000002 Logit Scale: 30.865 Contrastive_loss: 0.040920 (0.035030) Loss: 0.040920 (0.035030) 2025-03-20,11:02:20 | INFO | Train Epoch: 27 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 149.150/s, 149.150/s/gpu LR: 0.000002 Logit Scale: 30.866 Contrastive_loss: 0.0012277 (0.034877) Loss: 0.0012277 (0.034877) 2025-03-20,11:02:42 | INFO | Train Epoch: 27 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 145.246/s, 145.246/s/gpu LR: 0.000002 Logit Scale: 30.868 Contrastive_loss: 0.00026563 (0.034722) Loss: 0.00026563 (0.034722) 2025-03-20,11:03:04 | INFO | Train Epoch: 27 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.219, 146.357/s, 146.357/s/gpu LR: 0.000002 Logit Scale: 30.868 Contrastive_loss: 0.056791 (0.034820) Loss: 0.056791 (0.034820) 2025-03-20,11:03:25 | INFO | Train Epoch: 27 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 147.791/s, 147.791/s/gpu LR: 0.000002 Logit Scale: 30.869 Contrastive_loss: 0.019913 (0.034754) Loss: 0.019913 (0.034754) 2025-03-20,11:03:47 | INFO | Train Epoch: 27 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 146.191/s, 146.191/s/gpu LR: 0.000002 Logit Scale: 30.869 Contrastive_loss: 0.046089 (0.034804) Loss: 0.046089 (0.034804) 2025-03-20,11:04:08 | INFO | Train Epoch: 27 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 149.075/s, 149.075/s/gpu LR: 0.000002 Logit Scale: 30.870 Contrastive_loss: 0.063491 (0.034931) Loss: 0.063491 (0.034931) 2025-03-20,11:04:30 | INFO | Train Epoch: 27 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 150.200/s, 150.200/s/gpu LR: 0.000002 Logit Scale: 30.871 Contrastive_loss: 0.019947 (0.034865) Loss: 0.019947 (0.034865) 2025-03-20,11:04:51 | INFO | Train Epoch: 27 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 148.075/s, 148.075/s/gpu LR: 0.000002 Logit Scale: 30.872 Contrastive_loss: 0.011239 (0.034762) Loss: 0.011239 (0.034762) 2025-03-20,11:05:13 | INFO | Train Epoch: 27 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 148.687/s, 148.687/s/gpu LR: 0.000002 Logit Scale: 30.873 Contrastive_loss: 5.7131e-05 (0.034610) Loss: 5.7131e-05 (0.034610) 2025-03-20,11:05:35 | INFO | Train Epoch: 27 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 151.669/s, 151.669/s/gpu LR: 0.000002 Logit Scale: 30.874 Contrastive_loss: 0.029439 (0.034588) Loss: 0.029439 (0.034588) 2025-03-20,11:05:56 | INFO | Train Epoch: 27 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 147.597/s, 147.597/s/gpu LR: 0.000002 Logit Scale: 30.875 Contrastive_loss: 0.0046255 (0.034458) Loss: 0.0046255 (0.034458) 2025-03-20,11:06:18 | INFO | Train Epoch: 27 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.217, 149.000/s, 149.000/s/gpu LR: 0.000002 Logit Scale: 30.876 Contrastive_loss: 0.010065 (0.034353) Loss: 0.010065 (0.034353) 2025-03-20,11:06:39 | INFO | Train Epoch: 27 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.444/s, 149.444/s/gpu LR: 0.000002 Logit Scale: 30.877 Contrastive_loss: 0.012243 (0.034258) Loss: 0.012243 (0.034258) 2025-03-20,11:07:01 | INFO | Train Epoch: 27 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 149.689/s, 149.689/s/gpu LR: 0.000002 Logit Scale: 30.878 Contrastive_loss: 0.00018639 (0.034112) Loss: 0.00018639 (0.034112) 2025-03-20,11:07:22 | INFO | Train Epoch: 27 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 146.630/s, 146.630/s/gpu LR: 0.000002 Logit Scale: 30.877 Contrastive_loss: 0.045673 (0.034161) Loss: 0.045673 (0.034161) 2025-03-20,11:07:45 | INFO | Train Epoch: 27 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.223, 145.915/s, 145.915/s/gpu LR: 0.000002 Logit Scale: 30.878 Contrastive_loss: 2.9281e-05 (0.034017) Loss: 2.9281e-05 (0.034017) 2025-03-20,11:08:06 | INFO | Train Epoch: 27 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 149.556/s, 149.556/s/gpu LR: 0.000002 Logit Scale: 30.879 Contrastive_loss: 0.064203 (0.034144) Loss: 0.064203 (0.034144) 2025-03-20,11:08:28 | INFO | Train Epoch: 27 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.215, 146.611/s, 146.611/s/gpu LR: 0.000002 Logit Scale: 30.879 Contrastive_loss: 0.00093319 (0.034005) Loss: 0.00093319 (0.034005) 2025-03-20,11:08:49 | INFO | Train Epoch: 27 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 147.872/s, 147.872/s/gpu LR: 0.000002 Logit Scale: 30.879 Contrastive_loss: 0.00026591 (0.033863) Loss: 0.00026591 (0.033863) 2025-03-20,11:09:11 | INFO | Train Epoch: 27 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.218, 149.398/s, 149.398/s/gpu LR: 0.000002 Logit Scale: 30.880 Contrastive_loss: 0.0017290 (0.033730) Loss: 0.0017290 (0.033730) 2025-03-20,11:09:19 | INFO | Train Epoch: 27 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.216, 149.874/s, 149.874/s/gpu LR: 0.000002 Logit Scale: 30.880 Contrastive_loss: 0.00013990 (0.033590) Loss: 0.00013990 (0.033590) 2025-03-20,11:09:19 | INFO | Eval Epoch: 28 [32 / 7443] Clip Loss: 4.007534 2025-03-20,11:09:25 | INFO | Eval Epoch: 28 [3232 / 7443] Clip Loss: 0.860893 2025-03-20,11:09:31 | INFO | Eval Epoch: 28 [6432 / 7443] Clip Loss: 0.640700 2025-03-20,11:09:33 | INFO | Eval Epoch: 28 image_to_text_mean_rank: 74.1526 image_to_text_median_rank: 4.0000 image_to_text_R@1: 0.2182 image_to_text_R@5: 0.5690 image_to_text_R@10: 0.7250 text_to_image_mean_rank: 51.4039 text_to_image_median_rank: 4.0000 text_to_image_R@1: 0.2152 text_to_image_R@5: 0.5640 text_to_image_R@10: 0.7184 clip_val_loss: 0.5956 epoch: 28.0000 num_samples: 7443.0000 2025-03-20,11:10:06 | INFO | Start epoch 28 2025-03-20,11:10:06 | INFO | Train Epoch: 28 [ 32/766009 (0%)] Data (t): 0.171 Batch (t): 0.369, 86.7028/s, 86.7028/s/gpu LR: 0.000002 Logit Scale: 30.880 Contrastive_loss: 0.0010101 (0.0010101) Loss: 0.0010101 (0.0010101) 2025-03-20,11:10:27 | INFO | Train Epoch: 28 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.215, 147.287/s, 147.287/s/gpu LR: 0.000002 Logit Scale: 30.882 Contrastive_loss: 0.0021428 (0.0015764) Loss: 0.0021428 (0.0015764) 2025-03-20,11:10:49 | INFO | Train Epoch: 28 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 149.488/s, 149.488/s/gpu LR: 0.000002 Logit Scale: 30.883 Contrastive_loss: 0.00046790 (0.0012069) Loss: 0.00046790 (0.0012069) 2025-03-20,11:11:11 | INFO | Train Epoch: 28 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 149.230/s, 149.230/s/gpu LR: 0.000002 Logit Scale: 30.884 Contrastive_loss: 0.086731 (0.022588) Loss: 0.086731 (0.022588) 2025-03-20,11:11:32 | INFO | Train Epoch: 28 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.215, 149.720/s, 149.720/s/gpu LR: 0.000002 Logit Scale: 30.885 Contrastive_loss: 0.0082896 (0.019728) Loss: 0.0082896 (0.019728) 2025-03-20,11:11:54 | INFO | Train Epoch: 28 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.214, 147.134/s, 147.134/s/gpu LR: 0.000002 Logit Scale: 30.886 Contrastive_loss: 0.017603 (0.019374) Loss: 0.017603 (0.019374) 2025-03-20,11:12:15 | INFO | Train Epoch: 28 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 146.858/s, 146.858/s/gpu LR: 0.000002 Logit Scale: 30.887 Contrastive_loss: 0.032417 (0.021237) Loss: 0.032417 (0.021237) 2025-03-20,11:12:37 | INFO | Train Epoch: 28 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.215, 148.752/s, 148.752/s/gpu LR: 0.000002 Logit Scale: 30.888 Contrastive_loss: 0.081134 (0.028724) Loss: 0.081134 (0.028724) 2025-03-20,11:12:58 | INFO | Train Epoch: 28 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 149.334/s, 149.334/s/gpu LR: 0.000002 Logit Scale: 30.889 Contrastive_loss: 0.056558 (0.031817) Loss: 0.056558 (0.031817) 2025-03-20,11:13:20 | INFO | Train Epoch: 28 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 147.493/s, 147.493/s/gpu LR: 0.000002 Logit Scale: 30.891 Contrastive_loss: 0.00042544 (0.028678) Loss: 0.00042544 (0.028678) 2025-03-20,11:13:41 | INFO | Train Epoch: 28 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.313/s, 149.313/s/gpu LR: 0.000002 Logit Scale: 30.892 Contrastive_loss: 0.0010920 (0.026170) Loss: 0.0010920 (0.026170) 2025-03-20,11:14:03 | INFO | Train Epoch: 28 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 149.131/s, 149.131/s/gpu LR: 0.000002 Logit Scale: 30.892 Contrastive_loss: 0.037903 (0.027148) Loss: 0.037903 (0.027148) 2025-03-20,11:14:24 | INFO | Train Epoch: 28 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 148.631/s, 148.631/s/gpu LR: 0.000002 Logit Scale: 30.893 Contrastive_loss: 0.10245 (0.032940) Loss: 0.10245 (0.032940) 2025-03-20,11:14:46 | INFO | Train Epoch: 28 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 148.581/s, 148.581/s/gpu LR: 0.000002 Logit Scale: 30.894 Contrastive_loss: 0.088435 (0.036904) Loss: 0.088435 (0.036904) 2025-03-20,11:15:07 | INFO | Train Epoch: 28 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.714/s, 148.714/s/gpu LR: 0.000002 Logit Scale: 30.895 Contrastive_loss: 0.087155 (0.040254) Loss: 0.087155 (0.040254) 2025-03-20,11:15:29 | INFO | Train Epoch: 28 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 149.075/s, 149.075/s/gpu LR: 0.000002 Logit Scale: 30.896 Contrastive_loss: 0.00017562 (0.037749) Loss: 0.00017562 (0.037749) 2025-03-20,11:15:51 | INFO | Train Epoch: 28 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.217, 148.326/s, 148.326/s/gpu LR: 0.000002 Logit Scale: 30.897 Contrastive_loss: 0.00010764 (0.035535) Loss: 0.00010764 (0.035535) 2025-03-20,11:16:12 | INFO | Train Epoch: 28 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 147.416/s, 147.416/s/gpu LR: 0.000002 Logit Scale: 30.898 Contrastive_loss: 0.0097491 (0.034102) Loss: 0.0097491 (0.034102) 2025-03-20,11:16:34 | INFO | Train Epoch: 28 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 153.512/s, 153.512/s/gpu LR: 0.000002 Logit Scale: 30.899 Contrastive_loss: 0.12747 (0.039017) Loss: 0.12747 (0.039017) 2025-03-20,11:16:55 | INFO | Train Epoch: 28 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.212, 151.450/s, 151.450/s/gpu LR: 0.000002 Logit Scale: 30.899 Contrastive_loss: 0.082053 (0.041168) Loss: 0.082053 (0.041168) 2025-03-20,11:17:17 | INFO | Train Epoch: 28 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.215, 149.756/s, 149.756/s/gpu LR: 0.000002 Logit Scale: 30.899 Contrastive_loss: 0.051800 (0.041675) Loss: 0.051800 (0.041675) 2025-03-20,11:17:39 | INFO | Train Epoch: 28 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.220, 144.496/s, 144.496/s/gpu LR: 0.000002 Logit Scale: 30.901 Contrastive_loss: 0.030690 (0.041175) Loss: 0.030690 (0.041175) 2025-03-20,11:18:01 | INFO | Train Epoch: 28 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.226, 143.318/s, 143.318/s/gpu LR: 0.000002 Logit Scale: 30.901 Contrastive_loss: 0.0042983 (0.039572) Loss: 0.0042983 (0.039572) 2025-03-20,11:18:23 | INFO | Train Epoch: 28 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.223, 146.345/s, 146.345/s/gpu LR: 0.000002 Logit Scale: 30.902 Contrastive_loss: 0.035231 (0.039391) Loss: 0.035231 (0.039391) 2025-03-20,11:18:46 | INFO | Train Epoch: 28 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.220, 143.338/s, 143.338/s/gpu LR: 0.000002 Logit Scale: 30.903 Contrastive_loss: 0.0010998 (0.037859) Loss: 0.0010998 (0.037859) 2025-03-20,11:19:08 | INFO | Train Epoch: 28 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.221, 146.330/s, 146.330/s/gpu LR: 0.000002 Logit Scale: 30.904 Contrastive_loss: 0.014224 (0.036950) Loss: 0.014224 (0.036950) 2025-03-20,11:19:29 | INFO | Train Epoch: 28 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 147.387/s, 147.387/s/gpu LR: 0.000002 Logit Scale: 30.905 Contrastive_loss: 0.050271 (0.037444) Loss: 0.050271 (0.037444) 2025-03-20,11:19:51 | INFO | Train Epoch: 28 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 149.005/s, 149.005/s/gpu LR: 0.000002 Logit Scale: 30.905 Contrastive_loss: 0.0092691 (0.036438) Loss: 0.0092691 (0.036438) 2025-03-20,11:20:13 | INFO | Train Epoch: 28 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 148.636/s, 148.636/s/gpu LR: 0.000002 Logit Scale: 30.905 Contrastive_loss: 0.00046099 (0.035197) Loss: 0.00046099 (0.035197) 2025-03-20,11:20:34 | INFO | Train Epoch: 28 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.215, 148.262/s, 148.262/s/gpu LR: 0.000002 Logit Scale: 30.906 Contrastive_loss: 0.065237 (0.036198) Loss: 0.065237 (0.036198) 2025-03-20,11:20:56 | INFO | Train Epoch: 28 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.169/s, 148.169/s/gpu LR: 0.000002 Logit Scale: 30.907 Contrastive_loss: 0.00015592 (0.035036) Loss: 0.00015592 (0.035036) 2025-03-20,11:21:17 | INFO | Train Epoch: 28 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 149.495/s, 149.495/s/gpu LR: 0.000002 Logit Scale: 30.906 Contrastive_loss: 0.00017694 (0.033946) Loss: 0.00017694 (0.033946) 2025-03-20,11:21:39 | INFO | Train Epoch: 28 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.215, 148.754/s, 148.754/s/gpu LR: 0.000002 Logit Scale: 30.907 Contrastive_loss: 0.019687 (0.033514) Loss: 0.019687 (0.033514) 2025-03-20,11:22:00 | INFO | Train Epoch: 28 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 145.967/s, 145.967/s/gpu LR: 0.000002 Logit Scale: 30.908 Contrastive_loss: 0.21548 (0.038866) Loss: 0.21548 (0.038866) 2025-03-20,11:22:22 | INFO | Train Epoch: 28 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.218, 146.846/s, 146.846/s/gpu LR: 0.000002 Logit Scale: 30.909 Contrastive_loss: 0.017817 (0.038265) Loss: 0.017817 (0.038265) 2025-03-20,11:22:44 | INFO | Train Epoch: 28 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 148.463/s, 148.463/s/gpu LR: 0.000002 Logit Scale: 30.911 Contrastive_loss: 5.5438e-05 (0.037203) Loss: 5.5438e-05 (0.037203) 2025-03-20,11:23:05 | INFO | Train Epoch: 28 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 149.004/s, 149.004/s/gpu LR: 0.000002 Logit Scale: 30.911 Contrastive_loss: 0.00034682 (0.036207) Loss: 0.00034682 (0.036207) 2025-03-20,11:23:26 | INFO | Train Epoch: 28 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.906/s, 148.906/s/gpu LR: 0.000002 Logit Scale: 30.912 Contrastive_loss: 0.0067416 (0.035432) Loss: 0.0067416 (0.035432) 2025-03-20,11:23:48 | INFO | Train Epoch: 28 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 149.392/s, 149.392/s/gpu LR: 0.000002 Logit Scale: 30.912 Contrastive_loss: 0.039152 (0.035527) Loss: 0.039152 (0.035527) 2025-03-20,11:24:09 | INFO | Train Epoch: 28 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.215, 147.588/s, 147.588/s/gpu LR: 0.000002 Logit Scale: 30.912 Contrastive_loss: 0.00033749 (0.034648) Loss: 0.00033749 (0.034648) 2025-03-20,11:24:31 | INFO | Train Epoch: 28 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.215, 144.809/s, 144.809/s/gpu LR: 0.000002 Logit Scale: 30.913 Contrastive_loss: 0.013050 (0.034121) Loss: 0.013050 (0.034121) 2025-03-20,11:24:53 | INFO | Train Epoch: 28 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.217, 148.044/s, 148.044/s/gpu LR: 0.000002 Logit Scale: 30.913 Contrastive_loss: 0.012319 (0.033602) Loss: 0.012319 (0.033602) 2025-03-20,11:25:14 | INFO | Train Epoch: 28 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 149.194/s, 149.194/s/gpu LR: 0.000002 Logit Scale: 30.914 Contrastive_loss: 0.00052049 (0.032832) Loss: 0.00052049 (0.032832) 2025-03-20,11:25:36 | INFO | Train Epoch: 28 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.216, 146.279/s, 146.279/s/gpu LR: 0.000002 Logit Scale: 30.916 Contrastive_loss: 0.0057906 (0.032218) Loss: 0.0057906 (0.032218) 2025-03-20,11:25:58 | INFO | Train Epoch: 28 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.217, 147.611/s, 147.611/s/gpu LR: 0.000002 Logit Scale: 30.916 Contrastive_loss: 0.013962 (0.031812) Loss: 0.013962 (0.031812) 2025-03-20,11:26:19 | INFO | Train Epoch: 28 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 146.158/s, 146.158/s/gpu LR: 0.000002 Logit Scale: 30.918 Contrastive_loss: 0.00019519 (0.031125) Loss: 0.00019519 (0.031125) 2025-03-20,11:26:41 | INFO | Train Epoch: 28 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.217, 147.732/s, 147.732/s/gpu LR: 0.000002 Logit Scale: 30.918 Contrastive_loss: 0.0023888 (0.030513) Loss: 0.0023888 (0.030513) 2025-03-20,11:27:03 | INFO | Train Epoch: 28 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.218, 148.012/s, 148.012/s/gpu LR: 0.000002 Logit Scale: 30.919 Contrastive_loss: 0.00035017 (0.029885) Loss: 0.00035017 (0.029885) 2025-03-20,11:27:24 | INFO | Train Epoch: 28 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 146.556/s, 146.556/s/gpu LR: 0.000002 Logit Scale: 30.920 Contrastive_loss: 0.0059093 (0.029396) Loss: 0.0059093 (0.029396) 2025-03-20,11:27:46 | INFO | Train Epoch: 28 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 149.882/s, 149.882/s/gpu LR: 0.000002 Logit Scale: 30.922 Contrastive_loss: 0.10871 (0.030982) Loss: 0.10871 (0.030982) 2025-03-20,11:28:08 | INFO | Train Epoch: 28 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 149.051/s, 149.051/s/gpu LR: 0.000002 Logit Scale: 30.923 Contrastive_loss: 0.0037024 (0.030447) Loss: 0.0037024 (0.030447) 2025-03-20,11:28:29 | INFO | Train Epoch: 28 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 151.131/s, 151.131/s/gpu LR: 0.000002 Logit Scale: 30.925 Contrastive_loss: 0.043283 (0.030694) Loss: 0.043283 (0.030694) 2025-03-20,11:28:51 | INFO | Train Epoch: 28 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 149.351/s, 149.351/s/gpu LR: 0.000002 Logit Scale: 30.925 Contrastive_loss: 0.065093 (0.031343) Loss: 0.065093 (0.031343) 2025-03-20,11:29:12 | INFO | Train Epoch: 28 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.214, 149.607/s, 149.607/s/gpu LR: 0.000002 Logit Scale: 30.926 Contrastive_loss: 0.050150 (0.031691) Loss: 0.050150 (0.031691) 2025-03-20,11:29:34 | INFO | Train Epoch: 28 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 143.210/s, 143.210/s/gpu LR: 0.000002 Logit Scale: 30.926 Contrastive_loss: 0.044830 (0.031930) Loss: 0.044830 (0.031930) 2025-03-20,11:29:56 | INFO | Train Epoch: 28 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.222, 145.477/s, 145.477/s/gpu LR: 0.000002 Logit Scale: 30.927 Contrastive_loss: 0.010411 (0.031546) Loss: 0.010411 (0.031546) 2025-03-20,11:30:18 | INFO | Train Epoch: 28 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.222, 146.512/s, 146.512/s/gpu LR: 0.000002 Logit Scale: 30.928 Contrastive_loss: 0.066946 (0.032167) Loss: 0.066946 (0.032167) 2025-03-20,11:30:41 | INFO | Train Epoch: 28 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.220, 144.908/s, 144.908/s/gpu LR: 0.000002 Logit Scale: 30.928 Contrastive_loss: 0.00039303 (0.031619) Loss: 0.00039303 (0.031619) 2025-03-20,11:31:03 | INFO | Train Epoch: 28 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.222, 145.619/s, 145.619/s/gpu LR: 0.000002 Logit Scale: 30.928 Contrastive_loss: 0.0022966 (0.031122) Loss: 0.0022966 (0.031122) 2025-03-20,11:31:25 | INFO | Train Epoch: 28 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.221, 143.627/s, 143.627/s/gpu LR: 0.000002 Logit Scale: 30.928 Contrastive_loss: 0.015554 (0.030863) Loss: 0.015554 (0.030863) 2025-03-20,11:31:47 | INFO | Train Epoch: 28 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.223, 143.254/s, 143.254/s/gpu LR: 0.000002 Logit Scale: 30.930 Contrastive_loss: 0.00011971 (0.030359) Loss: 0.00011971 (0.030359) 2025-03-20,11:32:09 | INFO | Train Epoch: 28 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.223, 143.901/s, 143.901/s/gpu LR: 0.000002 Logit Scale: 30.931 Contrastive_loss: 2.0474e-05 (0.029869) Loss: 2.0474e-05 (0.029869) 2025-03-20,11:32:32 | INFO | Train Epoch: 28 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.222, 144.009/s, 144.009/s/gpu LR: 0.000002 Logit Scale: 30.932 Contrastive_loss: 6.1197e-05 (0.029396) Loss: 6.1197e-05 (0.029396) 2025-03-20,11:32:54 | INFO | Train Epoch: 28 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.221, 146.104/s, 146.104/s/gpu LR: 0.000002 Logit Scale: 30.934 Contrastive_loss: 0.022508 (0.029289) Loss: 0.022508 (0.029289) 2025-03-20,11:33:16 | INFO | Train Epoch: 28 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.221, 139.574/s, 139.574/s/gpu LR: 0.000002 Logit Scale: 30.935 Contrastive_loss: 0.031268 (0.029319) Loss: 0.031268 (0.029319) 2025-03-20,11:33:38 | INFO | Train Epoch: 28 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.219, 149.206/s, 149.206/s/gpu LR: 0.000002 Logit Scale: 30.935 Contrastive_loss: 0.00024676 (0.028878) Loss: 0.00024676 (0.028878) 2025-03-20,11:33:59 | INFO | Train Epoch: 28 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 146.827/s, 146.827/s/gpu LR: 0.000002 Logit Scale: 30.936 Contrastive_loss: 0.044019 (0.029104) Loss: 0.044019 (0.029104) 2025-03-20,11:34:21 | INFO | Train Epoch: 28 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.218, 147.359/s, 147.359/s/gpu LR: 0.000002 Logit Scale: 30.936 Contrastive_loss: 0.044827 (0.029336) Loss: 0.044827 (0.029336) 2025-03-20,11:34:43 | INFO | Train Epoch: 28 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.217, 146.549/s, 146.549/s/gpu LR: 0.000002 Logit Scale: 30.937 Contrastive_loss: 0.0017271 (0.028936) Loss: 0.0017271 (0.028936) 2025-03-20,11:35:05 | INFO | Train Epoch: 28 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.217, 149.567/s, 149.567/s/gpu LR: 0.000002 Logit Scale: 30.937 Contrastive_loss: 0.044086 (0.029152) Loss: 0.044086 (0.029152) 2025-03-20,11:35:27 | INFO | Train Epoch: 28 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.217, 145.493/s, 145.493/s/gpu LR: 0.000002 Logit Scale: 30.938 Contrastive_loss: 0.042721 (0.029343) Loss: 0.042721 (0.029343) 2025-03-20,11:35:49 | INFO | Train Epoch: 28 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.222, 143.489/s, 143.489/s/gpu LR: 0.000002 Logit Scale: 30.939 Contrastive_loss: 0.0030052 (0.028977) Loss: 0.0030052 (0.028977) 2025-03-20,11:36:10 | INFO | Train Epoch: 28 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 147.384/s, 147.384/s/gpu LR: 0.000002 Logit Scale: 30.940 Contrastive_loss: 0.022870 (0.028894) Loss: 0.022870 (0.028894) 2025-03-20,11:36:32 | INFO | Train Epoch: 28 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.217, 146.784/s, 146.784/s/gpu LR: 0.000002 Logit Scale: 30.941 Contrastive_loss: 0.045150 (0.029113) Loss: 0.045150 (0.029113) 2025-03-20,11:36:53 | INFO | Train Epoch: 28 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.214, 151.006/s, 151.006/s/gpu LR: 0.000002 Logit Scale: 30.941 Contrastive_loss: 0.045835 (0.029336) Loss: 0.045835 (0.029336) 2025-03-20,11:37:15 | INFO | Train Epoch: 28 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.213, 151.597/s, 151.597/s/gpu LR: 0.000002 Logit Scale: 30.942 Contrastive_loss: 0.0033359 (0.028994) Loss: 0.0033359 (0.028994) 2025-03-20,11:37:36 | INFO | Train Epoch: 28 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.215, 146.923/s, 146.923/s/gpu LR: 0.000002 Logit Scale: 30.943 Contrastive_loss: 0.055743 (0.029342) Loss: 0.055743 (0.029342) 2025-03-20,11:37:58 | INFO | Train Epoch: 28 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.221, 145.890/s, 145.890/s/gpu LR: 0.000002 Logit Scale: 30.943 Contrastive_loss: 0.00035635 (0.028970) Loss: 0.00035635 (0.028970) 2025-03-20,11:38:20 | INFO | Train Epoch: 28 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.220, 146.800/s, 146.800/s/gpu LR: 0.000002 Logit Scale: 30.943 Contrastive_loss: 0.0058988 (0.028678) Loss: 0.0058988 (0.028678) 2025-03-20,11:38:42 | INFO | Train Epoch: 28 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 147.591/s, 147.591/s/gpu LR: 0.000002 Logit Scale: 30.944 Contrastive_loss: 0.092858 (0.029480) Loss: 0.092858 (0.029480) 2025-03-20,11:39:04 | INFO | Train Epoch: 28 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 146.098/s, 146.098/s/gpu LR: 0.000002 Logit Scale: 30.945 Contrastive_loss: 0.081529 (0.030123) Loss: 0.081529 (0.030123) 2025-03-20,11:39:26 | INFO | Train Epoch: 28 [259232/766009 (34%)] Data (t): 0.001 Batch (t): 0.220, 145.499/s, 145.499/s/gpu LR: 0.000002 Logit Scale: 30.945 Contrastive_loss: 0.0070696 (0.029842) Loss: 0.0070696 (0.029842) 2025-03-20,11:39:48 | INFO | Train Epoch: 28 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.219, 145.843/s, 145.843/s/gpu LR: 0.000002 Logit Scale: 30.945 Contrastive_loss: 0.076982 (0.030410) Loss: 0.076982 (0.030410) 2025-03-20,11:40:09 | INFO | Train Epoch: 28 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 150.086/s, 150.086/s/gpu LR: 0.000002 Logit Scale: 30.945 Contrastive_loss: 0.017102 (0.030251) Loss: 0.017102 (0.030251) 2025-03-20,11:40:31 | INFO | Train Epoch: 28 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 149.632/s, 149.632/s/gpu LR: 0.000002 Logit Scale: 30.946 Contrastive_loss: 0.00026935 (0.029898) Loss: 0.00026935 (0.029898) 2025-03-20,11:40:53 | INFO | Train Epoch: 28 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.217, 147.366/s, 147.366/s/gpu LR: 0.000002 Logit Scale: 30.946 Contrastive_loss: 0.079044 (0.030470) Loss: 0.079044 (0.030470) 2025-03-20,11:41:15 | INFO | Train Epoch: 28 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.224, 142.511/s, 142.511/s/gpu LR: 0.000002 Logit Scale: 30.946 Contrastive_loss: 0.019771 (0.030347) Loss: 0.019771 (0.030347) 2025-03-20,11:41:37 | INFO | Train Epoch: 28 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 150.756/s, 150.756/s/gpu LR: 0.000002 Logit Scale: 30.946 Contrastive_loss: 0.0061314 (0.030072) Loss: 0.0061314 (0.030072) 2025-03-20,11:41:59 | INFO | Train Epoch: 28 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.222, 142.883/s, 142.883/s/gpu LR: 0.000001 Logit Scale: 30.947 Contrastive_loss: 4.6557e-05 (0.029734) Loss: 4.6557e-05 (0.029734) 2025-03-20,11:42:21 | INFO | Train Epoch: 28 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.222, 149.106/s, 149.106/s/gpu LR: 0.000001 Logit Scale: 30.948 Contrastive_loss: 0.00011905 (0.029405) Loss: 0.00011905 (0.029405) 2025-03-20,11:42:42 | INFO | Train Epoch: 28 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.597/s, 149.597/s/gpu LR: 0.000001 Logit Scale: 30.948 Contrastive_loss: 0.044966 (0.029576) Loss: 0.044966 (0.029576) 2025-03-20,11:43:04 | INFO | Train Epoch: 28 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.580/s, 149.580/s/gpu LR: 0.000001 Logit Scale: 30.948 Contrastive_loss: 4.8352e-05 (0.029255) Loss: 4.8352e-05 (0.029255) 2025-03-20,11:43:26 | INFO | Train Epoch: 28 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.216, 147.330/s, 147.330/s/gpu LR: 0.000001 Logit Scale: 30.948 Contrastive_loss: 0.025039 (0.029210) Loss: 0.025039 (0.029210) 2025-03-20,11:43:47 | INFO | Train Epoch: 28 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.217, 147.886/s, 147.886/s/gpu LR: 0.000001 Logit Scale: 30.949 Contrastive_loss: 0.0013385 (0.028913) Loss: 0.0013385 (0.028913) 2025-03-20,11:44:09 | INFO | Train Epoch: 28 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.217, 147.722/s, 147.722/s/gpu LR: 0.000001 Logit Scale: 30.949 Contrastive_loss: 0.050242 (0.029138) Loss: 0.050242 (0.029138) 2025-03-20,11:44:31 | INFO | Train Epoch: 28 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.219, 144.873/s, 144.873/s/gpu LR: 0.000001 Logit Scale: 30.949 Contrastive_loss: 0.17069 (0.030613) Loss: 0.17069 (0.030613) 2025-03-20,11:44:53 | INFO | Train Epoch: 28 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.217, 147.356/s, 147.356/s/gpu LR: 0.000001 Logit Scale: 30.950 Contrastive_loss: 0.014617 (0.030448) Loss: 0.014617 (0.030448) 2025-03-20,11:45:14 | INFO | Train Epoch: 28 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 150.776/s, 150.776/s/gpu LR: 0.000001 Logit Scale: 30.950 Contrastive_loss: 0.014326 (0.030283) Loss: 0.014326 (0.030283) 2025-03-20,11:45:35 | INFO | Train Epoch: 28 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.214, 149.405/s, 149.405/s/gpu LR: 0.000001 Logit Scale: 30.950 Contrastive_loss: 6.6686e-05 (0.029978) Loss: 6.6686e-05 (0.029978) 2025-03-20,11:45:57 | INFO | Train Epoch: 28 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.215, 147.822/s, 147.822/s/gpu LR: 0.000001 Logit Scale: 30.951 Contrastive_loss: 0.00064589 (0.029685) Loss: 0.00064589 (0.029685) 2025-03-20,11:46:19 | INFO | Train Epoch: 28 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.221, 141.571/s, 141.571/s/gpu LR: 0.000001 Logit Scale: 30.951 Contrastive_loss: 0.00012470 (0.029392) Loss: 0.00012470 (0.029392) 2025-03-20,11:46:41 | INFO | Train Epoch: 28 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.224, 141.191/s, 141.191/s/gpu LR: 0.000001 Logit Scale: 30.951 Contrastive_loss: 0.0045248 (0.029148) Loss: 0.0045248 (0.029148) 2025-03-20,11:47:04 | INFO | Train Epoch: 28 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.221, 150.897/s, 150.897/s/gpu LR: 0.000001 Logit Scale: 30.951 Contrastive_loss: 0.0034034 (0.028898) Loss: 0.0034034 (0.028898) 2025-03-20,11:47:25 | INFO | Train Epoch: 28 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.217, 149.506/s, 149.506/s/gpu LR: 0.000001 Logit Scale: 30.952 Contrastive_loss: 0.00031231 (0.028623) Loss: 0.00031231 (0.028623) 2025-03-20,11:47:47 | INFO | Train Epoch: 28 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.221, 137.753/s, 137.753/s/gpu LR: 0.000001 Logit Scale: 30.952 Contrastive_loss: 0.097468 (0.029279) Loss: 0.097468 (0.029279) 2025-03-20,11:48:10 | INFO | Train Epoch: 28 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.227, 141.133/s, 141.133/s/gpu LR: 0.000001 Logit Scale: 30.953 Contrastive_loss: 0.0053356 (0.029053) Loss: 0.0053356 (0.029053) 2025-03-20,11:48:32 | INFO | Train Epoch: 28 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.223, 143.633/s, 143.633/s/gpu LR: 0.000001 Logit Scale: 30.953 Contrastive_loss: 0.030963 (0.029071) Loss: 0.030963 (0.029071) 2025-03-20,11:48:55 | INFO | Train Epoch: 28 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.222, 142.604/s, 142.604/s/gpu LR: 0.000001 Logit Scale: 30.954 Contrastive_loss: 0.0085653 (0.028881) Loss: 0.0085653 (0.028881) 2025-03-20,11:49:17 | INFO | Train Epoch: 28 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.223, 141.593/s, 141.593/s/gpu LR: 0.000001 Logit Scale: 30.955 Contrastive_loss: 0.044316 (0.029023) Loss: 0.044316 (0.029023) 2025-03-20,11:49:39 | INFO | Train Epoch: 28 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.224, 146.987/s, 146.987/s/gpu LR: 0.000001 Logit Scale: 30.956 Contrastive_loss: 6.9732e-05 (0.028759) Loss: 6.9732e-05 (0.028759) 2025-03-20,11:50:01 | INFO | Train Epoch: 28 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.221, 142.117/s, 142.117/s/gpu LR: 0.000001 Logit Scale: 30.956 Contrastive_loss: 0.017432 (0.028657) Loss: 0.017432 (0.028657) 2025-03-20,11:50:23 | INFO | Train Epoch: 28 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.219, 148.641/s, 148.641/s/gpu LR: 0.000001 Logit Scale: 30.956 Contrastive_loss: 0.00066266 (0.028407) Loss: 0.00066266 (0.028407) 2025-03-20,11:50:45 | INFO | Train Epoch: 28 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.219, 147.103/s, 147.103/s/gpu LR: 0.000001 Logit Scale: 30.956 Contrastive_loss: 2.6949e-05 (0.028156) Loss: 2.6949e-05 (0.028156) 2025-03-20,11:51:07 | INFO | Train Epoch: 28 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.218, 147.172/s, 147.172/s/gpu LR: 0.000001 Logit Scale: 30.957 Contrastive_loss: 0.00033078 (0.027912) Loss: 0.00033078 (0.027912) 2025-03-20,11:51:28 | INFO | Train Epoch: 28 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 151.908/s, 151.908/s/gpu LR: 0.000001 Logit Scale: 30.957 Contrastive_loss: 0.10168 (0.028554) Loss: 0.10168 (0.028554) 2025-03-20,11:51:50 | INFO | Train Epoch: 28 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 149.269/s, 149.269/s/gpu LR: 0.000001 Logit Scale: 30.958 Contrastive_loss: 0.061302 (0.028836) Loss: 0.061302 (0.028836) 2025-03-20,11:52:11 | INFO | Train Epoch: 28 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 148.170/s, 148.170/s/gpu LR: 0.000001 Logit Scale: 30.959 Contrastive_loss: 3.9289e-05 (0.028590) Loss: 3.9289e-05 (0.028590) 2025-03-20,11:52:33 | INFO | Train Epoch: 28 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.217, 151.336/s, 151.336/s/gpu LR: 0.000001 Logit Scale: 30.960 Contrastive_loss: 0.023384 (0.028546) Loss: 0.023384 (0.028546) 2025-03-20,11:52:54 | INFO | Train Epoch: 28 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 151.653/s, 151.653/s/gpu LR: 0.000001 Logit Scale: 30.961 Contrastive_loss: 0.10143 (0.029158) Loss: 0.10143 (0.029158) 2025-03-20,11:53:16 | INFO | Train Epoch: 28 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 150.605/s, 150.605/s/gpu LR: 0.000001 Logit Scale: 30.961 Contrastive_loss: 0.00031685 (0.028918) Loss: 0.00031685 (0.028918) 2025-03-20,11:53:37 | INFO | Train Epoch: 28 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.216, 151.190/s, 151.190/s/gpu LR: 0.000001 Logit Scale: 30.962 Contrastive_loss: 0.045937 (0.029059) Loss: 0.045937 (0.029059) 2025-03-20,11:53:59 | INFO | Train Epoch: 28 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 145.533/s, 145.533/s/gpu LR: 0.000001 Logit Scale: 30.962 Contrastive_loss: 0.041787 (0.029163) Loss: 0.041787 (0.029163) 2025-03-20,11:54:21 | INFO | Train Epoch: 28 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 150.773/s, 150.773/s/gpu LR: 0.000001 Logit Scale: 30.963 Contrastive_loss: 0.045792 (0.029298) Loss: 0.045792 (0.029298) 2025-03-20,11:54:42 | INFO | Train Epoch: 28 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 147.929/s, 147.929/s/gpu LR: 0.000001 Logit Scale: 30.963 Contrastive_loss: 0.062109 (0.029563) Loss: 0.062109 (0.029563) 2025-03-20,11:55:04 | INFO | Train Epoch: 28 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.215, 146.818/s, 146.818/s/gpu LR: 0.000001 Logit Scale: 30.963 Contrastive_loss: 0.00049125 (0.029330) Loss: 0.00049125 (0.029330) 2025-03-20,11:55:25 | INFO | Train Epoch: 28 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.216, 149.120/s, 149.120/s/gpu LR: 0.000001 Logit Scale: 30.964 Contrastive_loss: 0.0012150 (0.029107) Loss: 0.0012150 (0.029107) 2025-03-20,11:55:47 | INFO | Train Epoch: 28 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.218, 147.678/s, 147.678/s/gpu LR: 0.000001 Logit Scale: 30.964 Contrastive_loss: 0.013556 (0.028985) Loss: 0.013556 (0.028985) 2025-03-20,11:56:09 | INFO | Train Epoch: 28 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.393/s, 148.393/s/gpu LR: 0.000001 Logit Scale: 30.965 Contrastive_loss: 0.13041 (0.029777) Loss: 0.13041 (0.029777) 2025-03-20,11:56:30 | INFO | Train Epoch: 28 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.214, 148.582/s, 148.582/s/gpu LR: 0.000001 Logit Scale: 30.966 Contrastive_loss: 0.043440 (0.029883) Loss: 0.043440 (0.029883) 2025-03-20,11:56:51 | INFO | Train Epoch: 28 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.213, 150.188/s, 150.188/s/gpu LR: 0.000001 Logit Scale: 30.967 Contrastive_loss: 0.00026545 (0.029655) Loss: 0.00026545 (0.029655) 2025-03-20,11:57:13 | INFO | Train Epoch: 28 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.216, 149.282/s, 149.282/s/gpu LR: 0.000001 Logit Scale: 30.968 Contrastive_loss: 0.00027315 (0.029431) Loss: 0.00027315 (0.029431) 2025-03-20,11:57:35 | INFO | Train Epoch: 28 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.215, 144.960/s, 144.960/s/gpu LR: 0.000001 Logit Scale: 30.969 Contrastive_loss: 0.15970 (0.030418) Loss: 0.15970 (0.030418) 2025-03-20,11:57:57 | INFO | Train Epoch: 28 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.220, 145.435/s, 145.435/s/gpu LR: 0.000001 Logit Scale: 30.969 Contrastive_loss: 0.059388 (0.030635) Loss: 0.059388 (0.030635) 2025-03-20,11:58:19 | INFO | Train Epoch: 28 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.220, 145.280/s, 145.280/s/gpu LR: 0.000001 Logit Scale: 30.969 Contrastive_loss: 0.011877 (0.030495) Loss: 0.011877 (0.030495) 2025-03-20,11:58:41 | INFO | Train Epoch: 28 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.222, 145.015/s, 145.015/s/gpu LR: 0.000001 Logit Scale: 30.969 Contrastive_loss: 0.0034419 (0.030295) Loss: 0.0034419 (0.030295) 2025-03-20,11:59:03 | INFO | Train Epoch: 28 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.219, 147.525/s, 147.525/s/gpu LR: 0.000001 Logit Scale: 30.970 Contrastive_loss: 7.0702e-05 (0.030073) Loss: 7.0702e-05 (0.030073) 2025-03-20,11:59:25 | INFO | Train Epoch: 28 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.219, 140.536/s, 140.536/s/gpu LR: 0.000001 Logit Scale: 30.970 Contrastive_loss: 0.00037795 (0.029856) Loss: 0.00037795 (0.029856) 2025-03-20,11:59:47 | INFO | Train Epoch: 28 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.221, 144.293/s, 144.293/s/gpu LR: 0.000001 Logit Scale: 30.971 Contrastive_loss: 0.086919 (0.030270) Loss: 0.086919 (0.030270) 2025-03-20,12:00:08 | INFO | Train Epoch: 28 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.219, 149.110/s, 149.110/s/gpu LR: 0.000001 Logit Scale: 30.971 Contrastive_loss: 0.0019279 (0.030066) Loss: 0.0019279 (0.030066) 2025-03-20,12:00:30 | INFO | Train Epoch: 28 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.393/s, 148.393/s/gpu LR: 0.000001 Logit Scale: 30.971 Contrastive_loss: 0.00032038 (0.029853) Loss: 0.00032038 (0.029853) 2025-03-20,12:00:52 | INFO | Train Epoch: 28 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 149.160/s, 149.160/s/gpu LR: 0.000001 Logit Scale: 30.971 Contrastive_loss: 0.051526 (0.030007) Loss: 0.051526 (0.030007) 2025-03-20,12:01:13 | INFO | Train Epoch: 28 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 149.467/s, 149.467/s/gpu LR: 0.000001 Logit Scale: 30.972 Contrastive_loss: 4.3510e-05 (0.029796) Loss: 4.3510e-05 (0.029796) 2025-03-20,12:01:35 | INFO | Train Epoch: 28 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 148.658/s, 148.658/s/gpu LR: 0.000001 Logit Scale: 30.972 Contrastive_loss: 0.00061724 (0.029592) Loss: 0.00061724 (0.029592) 2025-03-20,12:01:56 | INFO | Train Epoch: 28 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.218, 147.908/s, 147.908/s/gpu LR: 0.000001 Logit Scale: 30.972 Contrastive_loss: 0.00011819 (0.029387) Loss: 0.00011819 (0.029387) 2025-03-20,12:02:18 | INFO | Train Epoch: 28 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 150.581/s, 150.581/s/gpu LR: 0.000001 Logit Scale: 30.973 Contrastive_loss: 0.096737 (0.029852) Loss: 0.096737 (0.029852) 2025-03-20,12:02:40 | INFO | Train Epoch: 28 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.220, 150.015/s, 150.015/s/gpu LR: 0.000001 Logit Scale: 30.973 Contrastive_loss: 0.099706 (0.030330) Loss: 0.099706 (0.030330) 2025-03-20,12:03:02 | INFO | Train Epoch: 28 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 147.922/s, 147.922/s/gpu LR: 0.000001 Logit Scale: 30.974 Contrastive_loss: 0.016449 (0.030236) Loss: 0.016449 (0.030236) 2025-03-20,12:03:23 | INFO | Train Epoch: 28 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.278/s, 148.278/s/gpu LR: 0.000001 Logit Scale: 30.974 Contrastive_loss: 0.020190 (0.030168) Loss: 0.020190 (0.030168) 2025-03-20,12:03:45 | INFO | Train Epoch: 28 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.217, 146.286/s, 146.286/s/gpu LR: 0.000001 Logit Scale: 30.975 Contrastive_loss: 0.061941 (0.030381) Loss: 0.061941 (0.030381) 2025-03-20,12:04:07 | INFO | Train Epoch: 28 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.218, 146.887/s, 146.887/s/gpu LR: 0.000001 Logit Scale: 30.975 Contrastive_loss: 5.5540e-05 (0.030179) Loss: 5.5540e-05 (0.030179) 2025-03-20,12:04:29 | INFO | Train Epoch: 28 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.216, 149.214/s, 149.214/s/gpu LR: 0.000001 Logit Scale: 30.975 Contrastive_loss: 0.0049404 (0.030012) Loss: 0.0049404 (0.030012) 2025-03-20,12:04:50 | INFO | Train Epoch: 28 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.442/s, 148.442/s/gpu LR: 0.000001 Logit Scale: 30.975 Contrastive_loss: 0.016905 (0.029926) Loss: 0.016905 (0.029926) 2025-03-20,12:05:12 | INFO | Train Epoch: 28 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 150.460/s, 150.460/s/gpu LR: 0.000001 Logit Scale: 30.975 Contrastive_loss: 0.00013079 (0.029731) Loss: 0.00013079 (0.029731) 2025-03-20,12:05:33 | INFO | Train Epoch: 28 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.214, 149.092/s, 149.092/s/gpu LR: 0.000001 Logit Scale: 30.976 Contrastive_loss: 0.00076546 (0.029543) Loss: 0.00076546 (0.029543) 2025-03-20,12:05:54 | INFO | Train Epoch: 28 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 148.350/s, 148.350/s/gpu LR: 0.000001 Logit Scale: 30.976 Contrastive_loss: 8.7176e-05 (0.029353) Loss: 8.7176e-05 (0.029353) 2025-03-20,12:06:16 | INFO | Train Epoch: 28 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 150.508/s, 150.508/s/gpu LR: 0.000001 Logit Scale: 30.976 Contrastive_loss: 0.00078105 (0.029169) Loss: 0.00078105 (0.029169) 2025-03-20,12:06:38 | INFO | Train Epoch: 28 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.216, 148.555/s, 148.555/s/gpu LR: 0.000001 Logit Scale: 30.976 Contrastive_loss: 0.00017277 (0.028985) Loss: 0.00017277 (0.028985) 2025-03-20,12:06:59 | INFO | Train Epoch: 28 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 150.572/s, 150.572/s/gpu LR: 0.000001 Logit Scale: 30.977 Contrastive_loss: 0.00033393 (0.028803) Loss: 0.00033393 (0.028803) 2025-03-20,12:07:20 | INFO | Train Epoch: 28 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.214, 149.688/s, 149.688/s/gpu LR: 0.000001 Logit Scale: 30.977 Contrastive_loss: 0.040328 (0.028876) Loss: 0.040328 (0.028876) 2025-03-20,12:07:42 | INFO | Train Epoch: 28 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 148.837/s, 148.837/s/gpu LR: 0.000001 Logit Scale: 30.976 Contrastive_loss: 0.030976 (0.028889) Loss: 0.030976 (0.028889) 2025-03-20,12:08:04 | INFO | Train Epoch: 28 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.218, 144.566/s, 144.566/s/gpu LR: 0.000001 Logit Scale: 30.977 Contrastive_loss: 0.073017 (0.029163) Loss: 0.073017 (0.029163) 2025-03-20,12:08:26 | INFO | Train Epoch: 28 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.222, 141.859/s, 141.859/s/gpu LR: 0.000001 Logit Scale: 30.977 Contrastive_loss: 0.013084 (0.029064) Loss: 0.013084 (0.029064) 2025-03-20,12:08:48 | INFO | Train Epoch: 28 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.219, 146.557/s, 146.557/s/gpu LR: 0.000001 Logit Scale: 30.978 Contrastive_loss: 0.065654 (0.029288) Loss: 0.065654 (0.029288) 2025-03-20,12:09:10 | INFO | Train Epoch: 28 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.221, 148.441/s, 148.441/s/gpu LR: 0.000001 Logit Scale: 30.978 Contrastive_loss: 0.0035791 (0.029132) Loss: 0.0035791 (0.029132) 2025-03-20,12:09:32 | INFO | Train Epoch: 28 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 145.391/s, 145.391/s/gpu LR: 0.000001 Logit Scale: 30.979 Contrastive_loss: 0.052723 (0.029275) Loss: 0.052723 (0.029275) 2025-03-20,12:09:54 | INFO | Train Epoch: 28 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.219, 147.139/s, 147.139/s/gpu LR: 0.000001 Logit Scale: 30.979 Contrastive_loss: 0.0047333 (0.029127) Loss: 0.0047333 (0.029127) 2025-03-20,12:10:15 | INFO | Train Epoch: 28 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.217, 151.531/s, 151.531/s/gpu LR: 0.000001 Logit Scale: 30.980 Contrastive_loss: 0.11436 (0.029637) Loss: 0.11436 (0.029637) 2025-03-20,12:10:37 | INFO | Train Epoch: 28 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 147.769/s, 147.769/s/gpu LR: 0.000001 Logit Scale: 30.980 Contrastive_loss: 0.047520 (0.029744) Loss: 0.047520 (0.029744) 2025-03-20,12:10:59 | INFO | Train Epoch: 28 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 146.249/s, 146.249/s/gpu LR: 0.000001 Logit Scale: 30.980 Contrastive_loss: 0.021172 (0.029693) Loss: 0.021172 (0.029693) 2025-03-20,12:11:20 | INFO | Train Epoch: 28 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 149.263/s, 149.263/s/gpu LR: 0.000001 Logit Scale: 30.980 Contrastive_loss: 5.9299e-05 (0.029519) Loss: 5.9299e-05 (0.029519) 2025-03-20,12:11:42 | INFO | Train Epoch: 28 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.215, 148.354/s, 148.354/s/gpu LR: 0.000001 Logit Scale: 30.981 Contrastive_loss: 0.0046335 (0.029373) Loss: 0.0046335 (0.029373) 2025-03-20,12:12:04 | INFO | Train Epoch: 28 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.218, 146.499/s, 146.499/s/gpu LR: 0.000001 Logit Scale: 30.981 Contrastive_loss: 0.046401 (0.029472) Loss: 0.046401 (0.029472) 2025-03-20,12:12:25 | INFO | Train Epoch: 28 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.219, 147.132/s, 147.132/s/gpu LR: 0.000001 Logit Scale: 30.982 Contrastive_loss: 0.041300 (0.029540) Loss: 0.041300 (0.029540) 2025-03-20,12:12:47 | INFO | Train Epoch: 28 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.218, 148.674/s, 148.674/s/gpu LR: 0.000001 Logit Scale: 30.982 Contrastive_loss: 0.00052831 (0.029374) Loss: 0.00052831 (0.029374) 2025-03-20,12:13:09 | INFO | Train Epoch: 28 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.217, 147.260/s, 147.260/s/gpu LR: 0.000001 Logit Scale: 30.982 Contrastive_loss: 0.00022135 (0.029207) Loss: 0.00022135 (0.029207) 2025-03-20,12:13:31 | INFO | Train Epoch: 28 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.218, 146.688/s, 146.688/s/gpu LR: 0.000001 Logit Scale: 30.982 Contrastive_loss: 0.084317 (0.029520) Loss: 0.084317 (0.029520) 2025-03-20,12:13:52 | INFO | Train Epoch: 28 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 148.669/s, 148.669/s/gpu LR: 0.000001 Logit Scale: 30.983 Contrastive_loss: 0.021007 (0.029472) Loss: 0.021007 (0.029472) 2025-03-20,12:14:14 | INFO | Train Epoch: 28 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.216, 145.618/s, 145.618/s/gpu LR: 0.000001 Logit Scale: 30.983 Contrastive_loss: 0.11301 (0.029941) Loss: 0.11301 (0.029941) 2025-03-20,12:14:35 | INFO | Train Epoch: 28 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 151.558/s, 151.558/s/gpu LR: 0.000001 Logit Scale: 30.984 Contrastive_loss: 0.00021453 (0.029775) Loss: 0.00021453 (0.029775) 2025-03-20,12:14:57 | INFO | Train Epoch: 28 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.213, 149.655/s, 149.655/s/gpu LR: 0.000001 Logit Scale: 30.984 Contrastive_loss: 0.053191 (0.029905) Loss: 0.053191 (0.029905) 2025-03-20,12:15:18 | INFO | Train Epoch: 28 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.216, 148.087/s, 148.087/s/gpu LR: 0.000001 Logit Scale: 30.984 Contrastive_loss: 0.0012159 (0.029747) Loss: 0.0012159 (0.029747) 2025-03-20,12:15:40 | INFO | Train Epoch: 28 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.217, 149.525/s, 149.525/s/gpu LR: 0.000001 Logit Scale: 30.985 Contrastive_loss: 0.00037767 (0.029586) Loss: 0.00037767 (0.029586) 2025-03-20,12:16:02 | INFO | Train Epoch: 28 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.216, 149.935/s, 149.935/s/gpu LR: 0.000001 Logit Scale: 30.985 Contrastive_loss: 0.014010 (0.029500) Loss: 0.014010 (0.029500) 2025-03-20,12:16:23 | INFO | Train Epoch: 28 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 151.361/s, 151.361/s/gpu LR: 0.000001 Logit Scale: 30.986 Contrastive_loss: 0.0045290 (0.029365) Loss: 0.0045290 (0.029365) 2025-03-20,12:16:44 | INFO | Train Epoch: 28 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.214, 147.663/s, 147.663/s/gpu LR: 0.000001 Logit Scale: 30.986 Contrastive_loss: 0.052246 (0.029488) Loss: 0.052246 (0.029488) 2025-03-20,12:17:06 | INFO | Train Epoch: 28 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 151.900/s, 151.900/s/gpu LR: 0.000001 Logit Scale: 30.987 Contrastive_loss: 0.060239 (0.029654) Loss: 0.060239 (0.029654) 2025-03-20,12:17:27 | INFO | Train Epoch: 28 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.214, 148.771/s, 148.771/s/gpu LR: 0.000001 Logit Scale: 30.987 Contrastive_loss: 0.00091754 (0.029500) Loss: 0.00091754 (0.029500) 2025-03-20,12:17:49 | INFO | Train Epoch: 28 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.215, 150.996/s, 150.996/s/gpu LR: 0.000001 Logit Scale: 30.987 Contrastive_loss: 0.0012228 (0.029350) Loss: 0.0012228 (0.029350) 2025-03-20,12:18:10 | INFO | Train Epoch: 28 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.214, 148.938/s, 148.938/s/gpu LR: 0.000001 Logit Scale: 30.988 Contrastive_loss: 0.039583 (0.029404) Loss: 0.039583 (0.029404) 2025-03-20,12:18:32 | INFO | Train Epoch: 28 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.213, 152.853/s, 152.853/s/gpu LR: 0.000001 Logit Scale: 30.988 Contrastive_loss: 0.038677 (0.029453) Loss: 0.038677 (0.029453) 2025-03-20,12:18:53 | INFO | Train Epoch: 28 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.311/s, 148.311/s/gpu LR: 0.000001 Logit Scale: 30.988 Contrastive_loss: 0.068699 (0.029658) Loss: 0.068699 (0.029658) 2025-03-20,12:19:15 | INFO | Train Epoch: 28 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.218, 147.390/s, 147.390/s/gpu LR: 0.000001 Logit Scale: 30.988 Contrastive_loss: 0.044454 (0.029735) Loss: 0.044454 (0.029735) 2025-03-20,12:19:37 | INFO | Train Epoch: 28 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 148.839/s, 148.839/s/gpu LR: 0.000001 Logit Scale: 30.989 Contrastive_loss: 0.00010737 (0.029582) Loss: 0.00010737 (0.029582) 2025-03-20,12:19:58 | INFO | Train Epoch: 28 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 146.788/s, 146.788/s/gpu LR: 0.000001 Logit Scale: 30.989 Contrastive_loss: 0.00016825 (0.029430) Loss: 0.00016825 (0.029430) 2025-03-20,12:20:20 | INFO | Train Epoch: 28 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 148.484/s, 148.484/s/gpu LR: 0.000001 Logit Scale: 30.989 Contrastive_loss: 0.11706 (0.029879) Loss: 0.11706 (0.029879) 2025-03-20,12:20:41 | INFO | Train Epoch: 28 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.214, 149.134/s, 149.134/s/gpu LR: 0.000001 Logit Scale: 30.990 Contrastive_loss: 6.4416e-05 (0.029727) Loss: 6.4416e-05 (0.029727) 2025-03-20,12:21:03 | INFO | Train Epoch: 28 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.215, 149.395/s, 149.395/s/gpu LR: 0.000001 Logit Scale: 30.990 Contrastive_loss: 0.080358 (0.029984) Loss: 0.080358 (0.029984) 2025-03-20,12:21:24 | INFO | Train Epoch: 28 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 149.678/s, 149.678/s/gpu LR: 0.000001 Logit Scale: 30.990 Contrastive_loss: 0.0053811 (0.029860) Loss: 0.0053811 (0.029860) 2025-03-20,12:21:46 | INFO | Train Epoch: 28 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 148.726/s, 148.726/s/gpu LR: 0.000001 Logit Scale: 30.990 Contrastive_loss: 0.0093365 (0.029757) Loss: 0.0093365 (0.029757) 2025-03-20,12:22:07 | INFO | Train Epoch: 28 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 144.292/s, 144.292/s/gpu LR: 0.000001 Logit Scale: 30.991 Contrastive_loss: 0.049825 (0.029857) Loss: 0.049825 (0.029857) 2025-03-20,12:22:29 | INFO | Train Epoch: 28 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 151.712/s, 151.712/s/gpu LR: 0.000001 Logit Scale: 30.991 Contrastive_loss: 0.00079625 (0.029713) Loss: 0.00079625 (0.029713) 2025-03-20,12:22:50 | INFO | Train Epoch: 28 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.214, 148.242/s, 148.242/s/gpu LR: 0.000001 Logit Scale: 30.992 Contrastive_loss: 0.00054801 (0.029568) Loss: 0.00054801 (0.029568) 2025-03-20,12:23:12 | INFO | Train Epoch: 28 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 147.179/s, 147.179/s/gpu LR: 0.000001 Logit Scale: 30.992 Contrastive_loss: 0.00047314 (0.029425) Loss: 0.00047314 (0.029425) 2025-03-20,12:23:33 | INFO | Train Epoch: 28 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.216, 148.713/s, 148.713/s/gpu LR: 0.000001 Logit Scale: 30.991 Contrastive_loss: 0.00027223 (0.029282) Loss: 0.00027223 (0.029282) 2025-03-20,12:23:55 | INFO | Train Epoch: 28 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 152.220/s, 152.220/s/gpu LR: 0.000001 Logit Scale: 30.992 Contrastive_loss: 0.044241 (0.029355) Loss: 0.044241 (0.029355) 2025-03-20,12:24:16 | INFO | Train Epoch: 28 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 148.999/s, 148.999/s/gpu LR: 0.000001 Logit Scale: 30.992 Contrastive_loss: 0.00034933 (0.029214) Loss: 0.00034933 (0.029214) 2025-03-20,12:24:38 | INFO | Train Epoch: 28 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 147.333/s, 147.333/s/gpu LR: 0.000001 Logit Scale: 30.992 Contrastive_loss: 0.00061767 (0.029076) Loss: 0.00061767 (0.029076) 2025-03-20,12:24:59 | INFO | Train Epoch: 28 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.214, 148.077/s, 148.077/s/gpu LR: 0.000001 Logit Scale: 30.992 Contrastive_loss: 0.058696 (0.029218) Loss: 0.058696 (0.029218) 2025-03-20,12:25:21 | INFO | Train Epoch: 28 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.217, 142.256/s, 142.256/s/gpu LR: 0.000001 Logit Scale: 30.993 Contrastive_loss: 0.044335 (0.029291) Loss: 0.044335 (0.029291) 2025-03-20,12:25:43 | INFO | Train Epoch: 28 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 147.944/s, 147.944/s/gpu LR: 0.000001 Logit Scale: 30.993 Contrastive_loss: 0.057621 (0.029426) Loss: 0.057621 (0.029426) 2025-03-20,12:26:04 | INFO | Train Epoch: 28 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 147.798/s, 147.798/s/gpu LR: 0.000001 Logit Scale: 30.993 Contrastive_loss: 0.046051 (0.029505) Loss: 0.046051 (0.029505) 2025-03-20,12:26:26 | INFO | Train Epoch: 28 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 147.471/s, 147.471/s/gpu LR: 0.000001 Logit Scale: 30.993 Contrastive_loss: 0.025071 (0.029484) Loss: 0.025071 (0.029484) 2025-03-20,12:26:47 | INFO | Train Epoch: 28 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 146.392/s, 146.392/s/gpu LR: 0.000001 Logit Scale: 30.994 Contrastive_loss: 0.067638 (0.029663) Loss: 0.067638 (0.029663) 2025-03-20,12:27:09 | INFO | Train Epoch: 28 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 148.303/s, 148.303/s/gpu LR: 0.000001 Logit Scale: 30.994 Contrastive_loss: 0.010769 (0.029574) Loss: 0.010769 (0.029574) 2025-03-20,12:27:31 | INFO | Train Epoch: 28 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.217, 148.788/s, 148.788/s/gpu LR: 0.000001 Logit Scale: 30.995 Contrastive_loss: 0.077380 (0.029797) Loss: 0.077380 (0.029797) 2025-03-20,12:27:52 | INFO | Train Epoch: 28 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.218, 147.932/s, 147.932/s/gpu LR: 0.000001 Logit Scale: 30.995 Contrastive_loss: 3.3220e-05 (0.029659) Loss: 3.3220e-05 (0.029659) 2025-03-20,12:28:14 | INFO | Train Epoch: 28 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.216, 147.924/s, 147.924/s/gpu LR: 0.000001 Logit Scale: 30.995 Contrastive_loss: 0.037649 (0.029696) Loss: 0.037649 (0.029696) 2025-03-20,12:28:36 | INFO | Train Epoch: 28 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.218, 147.765/s, 147.765/s/gpu LR: 0.000001 Logit Scale: 30.996 Contrastive_loss: 0.012719 (0.029618) Loss: 0.012719 (0.029618) 2025-03-20,12:28:58 | INFO | Train Epoch: 28 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.217, 146.717/s, 146.717/s/gpu LR: 0.000001 Logit Scale: 30.996 Contrastive_loss: 0.047683 (0.029700) Loss: 0.047683 (0.029700) 2025-03-20,12:29:19 | INFO | Train Epoch: 28 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.218, 147.249/s, 147.249/s/gpu LR: 0.000001 Logit Scale: 30.996 Contrastive_loss: 0.0032855 (0.029580) Loss: 0.0032855 (0.029580) 2025-03-20,12:29:41 | INFO | Train Epoch: 28 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.217, 150.919/s, 150.919/s/gpu LR: 0.000001 Logit Scale: 30.997 Contrastive_loss: 0.044643 (0.029649) Loss: 0.044643 (0.029649) 2025-03-20,12:30:03 | INFO | Train Epoch: 28 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 147.009/s, 147.009/s/gpu LR: 0.000001 Logit Scale: 30.997 Contrastive_loss: 0.018473 (0.029598) Loss: 0.018473 (0.029598) 2025-03-20,12:30:24 | INFO | Train Epoch: 28 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.215, 141.530/s, 141.530/s/gpu LR: 0.000001 Logit Scale: 30.997 Contrastive_loss: 0.0013795 (0.029472) Loss: 0.0013795 (0.029472) 2025-03-20,12:30:46 | INFO | Train Epoch: 28 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 148.219/s, 148.219/s/gpu LR: 0.000001 Logit Scale: 30.997 Contrastive_loss: 0.030951 (0.029478) Loss: 0.030951 (0.029478) 2025-03-20,12:31:07 | INFO | Train Epoch: 28 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 148.018/s, 148.018/s/gpu LR: 0.000001 Logit Scale: 30.997 Contrastive_loss: 0.013814 (0.029409) Loss: 0.013814 (0.029409) 2025-03-20,12:31:29 | INFO | Train Epoch: 28 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 147.772/s, 147.772/s/gpu LR: 0.000001 Logit Scale: 30.997 Contrastive_loss: 0.15922 (0.029983) Loss: 0.15922 (0.029983) 2025-03-20,12:31:51 | INFO | Train Epoch: 28 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.218, 147.617/s, 147.617/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.018491 (0.029932) Loss: 0.018491 (0.029932) 2025-03-20,12:32:12 | INFO | Train Epoch: 28 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 147.932/s, 147.932/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.058909 (0.030059) Loss: 0.058909 (0.030059) 2025-03-20,12:32:34 | INFO | Train Epoch: 28 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.216, 147.876/s, 147.876/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.031690 (0.030067) Loss: 0.031690 (0.030067) 2025-03-20,12:32:55 | INFO | Train Epoch: 28 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.215, 151.197/s, 151.197/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 4.1507e-05 (0.029936) Loss: 4.1507e-05 (0.029936) 2025-03-20,12:33:17 | INFO | Train Epoch: 28 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.213, 151.802/s, 151.802/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.017669 (0.029883) Loss: 0.017669 (0.029883) 2025-03-20,12:33:38 | INFO | Train Epoch: 28 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 148.281/s, 148.281/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.064080 (0.030030) Loss: 0.064080 (0.030030) 2025-03-20,12:34:00 | INFO | Train Epoch: 28 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.215, 147.711/s, 147.711/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.0014492 (0.029908) Loss: 0.0014492 (0.029908) 2025-03-20,12:34:21 | INFO | Train Epoch: 28 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.217, 150.793/s, 150.793/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.015217 (0.029845) Loss: 0.015217 (0.029845) 2025-03-20,12:34:43 | INFO | Train Epoch: 28 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.217, 147.626/s, 147.626/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 0.0015251 (0.029724) Loss: 0.0015251 (0.029724) 2025-03-20,12:35:05 | INFO | Train Epoch: 28 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.216, 147.377/s, 147.377/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.048058 (0.029802) Loss: 0.048058 (0.029802) 2025-03-20,12:35:26 | INFO | Train Epoch: 28 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 147.601/s, 147.601/s/gpu LR: 0.000001 Logit Scale: 30.998 Contrastive_loss: 7.2928e-05 (0.029677) Loss: 7.2928e-05 (0.029677) 2025-03-20,12:35:48 | INFO | Train Epoch: 28 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.216, 148.994/s, 148.994/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.00029997 (0.029553) Loss: 0.00029997 (0.029553) 2025-03-20,12:36:10 | INFO | Train Epoch: 28 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 147.171/s, 147.171/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.00080442 (0.029433) Loss: 0.00080442 (0.029433) 2025-03-20,12:36:31 | INFO | Train Epoch: 28 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.217, 146.620/s, 146.620/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.087941 (0.029677) Loss: 0.087941 (0.029677) 2025-03-20,12:36:39 | INFO | Train Epoch: 28 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.217, 149.269/s, 149.269/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.00055752 (0.029556) Loss: 0.00055752 (0.029556) 2025-03-20,12:36:39 | INFO | Eval Epoch: 29 [32 / 7443] Clip Loss: 4.111406 2025-03-20,12:36:45 | INFO | Eval Epoch: 29 [3232 / 7443] Clip Loss: 0.859795 2025-03-20,12:36:51 | INFO | Eval Epoch: 29 [6432 / 7443] Clip Loss: 0.641481 2025-03-20,12:36:53 | INFO | Eval Epoch: 29 image_to_text_mean_rank: 77.1198 image_to_text_median_rank: 4.0000 image_to_text_R@1: 0.2142 image_to_text_R@5: 0.5682 image_to_text_R@10: 0.7298 text_to_image_mean_rank: 52.9757 text_to_image_median_rank: 4.0000 text_to_image_R@1: 0.2190 text_to_image_R@5: 0.5646 text_to_image_R@10: 0.7211 clip_val_loss: 0.5956 epoch: 29.0000 num_samples: 7443.0000 2025-03-20,12:37:25 | INFO | Start epoch 29 2025-03-20,12:37:25 | INFO | Train Epoch: 29 [ 32/766009 (0%)] Data (t): 0.181 Batch (t): 0.389, 82.3567/s, 82.3567/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.0083757 (0.0083757) Loss: 0.0083757 (0.0083757) 2025-03-20,12:37:47 | INFO | Train Epoch: 29 [ 3232/766009 (0%)] Data (t): 0.001 Batch (t): 0.215, 148.745/s, 148.745/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.022080 (0.015228) Loss: 0.022080 (0.015228) 2025-03-20,12:38:09 | INFO | Train Epoch: 29 [ 6432/766009 (1%)] Data (t): 0.001 Batch (t): 0.219, 148.096/s, 148.096/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.075046 (0.035167) Loss: 0.075046 (0.035167) 2025-03-20,12:38:30 | INFO | Train Epoch: 29 [ 9632/766009 (1%)] Data (t): 0.001 Batch (t): 0.216, 145.610/s, 145.610/s/gpu LR: 0.000001 Logit Scale: 30.999 Contrastive_loss: 0.043606 (0.037277) Loss: 0.043606 (0.037277) 2025-03-20,12:38:52 | INFO | Train Epoch: 29 [ 12832/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 148.694/s, 148.694/s/gpu LR: 0.000001 Logit Scale: 31.000 Contrastive_loss: 0.024378 (0.034697) Loss: 0.024378 (0.034697) 2025-03-20,12:39:14 | INFO | Train Epoch: 29 [ 16032/766009 (2%)] Data (t): 0.001 Batch (t): 0.216, 148.098/s, 148.098/s/gpu LR: 0.000001 Logit Scale: 31.000 Contrastive_loss: 0.045586 (0.036512) Loss: 0.045586 (0.036512) 2025-03-20,12:39:35 | INFO | Train Epoch: 29 [ 19232/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 148.153/s, 148.153/s/gpu LR: 0.000001 Logit Scale: 31.001 Contrastive_loss: 0.00023875 (0.031330) Loss: 0.00023875 (0.031330) 2025-03-20,12:39:57 | INFO | Train Epoch: 29 [ 22432/766009 (3%)] Data (t): 0.001 Batch (t): 0.217, 146.974/s, 146.974/s/gpu LR: 0.000001 Logit Scale: 31.001 Contrastive_loss: 0.013054 (0.029046) Loss: 0.013054 (0.029046) 2025-03-20,12:40:18 | INFO | Train Epoch: 29 [ 25632/766009 (3%)] Data (t): 0.001 Batch (t): 0.214, 147.949/s, 147.949/s/gpu LR: 0.000001 Logit Scale: 31.001 Contrastive_loss: 0.00018310 (0.025839) Loss: 0.00018310 (0.025839) 2025-03-20,12:40:40 | INFO | Train Epoch: 29 [ 28832/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 149.155/s, 149.155/s/gpu LR: 0.000001 Logit Scale: 31.002 Contrastive_loss: 2.3339e-05 (0.023257) Loss: 2.3339e-05 (0.023257) 2025-03-20,12:41:01 | INFO | Train Epoch: 29 [ 32032/766009 (4%)] Data (t): 0.001 Batch (t): 0.215, 147.016/s, 147.016/s/gpu LR: 0.000001 Logit Scale: 31.002 Contrastive_loss: 0.019433 (0.022910) Loss: 0.019433 (0.022910) 2025-03-20,12:41:23 | INFO | Train Epoch: 29 [ 35232/766009 (5%)] Data (t): 0.001 Batch (t): 0.217, 148.766/s, 148.766/s/gpu LR: 0.000001 Logit Scale: 31.002 Contrastive_loss: 0.043537 (0.024629) Loss: 0.043537 (0.024629) 2025-03-20,12:41:44 | INFO | Train Epoch: 29 [ 38432/766009 (5%)] Data (t): 0.001 Batch (t): 0.215, 148.640/s, 148.640/s/gpu LR: 0.000001 Logit Scale: 31.003 Contrastive_loss: 0.020755 (0.024331) Loss: 0.020755 (0.024331) 2025-03-20,12:42:06 | INFO | Train Epoch: 29 [ 41632/766009 (5%)] Data (t): 0.001 Batch (t): 0.216, 147.298/s, 147.298/s/gpu LR: 0.000001 Logit Scale: 31.003 Contrastive_loss: 0.0024435 (0.022767) Loss: 0.0024435 (0.022767) 2025-03-20,12:42:27 | INFO | Train Epoch: 29 [ 44832/766009 (6%)] Data (t): 0.001 Batch (t): 0.214, 149.438/s, 149.438/s/gpu LR: 0.000000 Logit Scale: 31.003 Contrastive_loss: 0.045236 (0.024265) Loss: 0.045236 (0.024265) 2025-03-20,12:42:49 | INFO | Train Epoch: 29 [ 48032/766009 (6%)] Data (t): 0.001 Batch (t): 0.216, 148.867/s, 148.867/s/gpu LR: 0.000000 Logit Scale: 31.003 Contrastive_loss: 0.051782 (0.025985) Loss: 0.051782 (0.025985) 2025-03-20,12:43:10 | INFO | Train Epoch: 29 [ 51232/766009 (7%)] Data (t): 0.001 Batch (t): 0.215, 148.215/s, 148.215/s/gpu LR: 0.000000 Logit Scale: 31.003 Contrastive_loss: 0.055180 (0.027702) Loss: 0.055180 (0.027702) 2025-03-20,12:43:32 | INFO | Train Epoch: 29 [ 54432/766009 (7%)] Data (t): 0.001 Batch (t): 0.216, 148.153/s, 148.153/s/gpu LR: 0.000000 Logit Scale: 31.004 Contrastive_loss: 0.0088896 (0.026657) Loss: 0.0088896 (0.026657) 2025-03-20,12:43:54 | INFO | Train Epoch: 29 [ 57632/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 149.275/s, 149.275/s/gpu LR: 0.000000 Logit Scale: 31.004 Contrastive_loss: 0.045361 (0.027642) Loss: 0.045361 (0.027642) 2025-03-20,12:44:15 | INFO | Train Epoch: 29 [ 60832/766009 (8%)] Data (t): 0.001 Batch (t): 0.216, 150.813/s, 150.813/s/gpu LR: 0.000000 Logit Scale: 31.004 Contrastive_loss: 0.016379 (0.027078) Loss: 0.016379 (0.027078) 2025-03-20,12:44:37 | INFO | Train Epoch: 29 [ 64032/766009 (8%)] Data (t): 0.001 Batch (t): 0.218, 148.729/s, 148.729/s/gpu LR: 0.000000 Logit Scale: 31.005 Contrastive_loss: 0.066227 (0.028943) Loss: 0.066227 (0.028943) 2025-03-20,12:44:59 | INFO | Train Epoch: 29 [ 67232/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 149.176/s, 149.176/s/gpu LR: 0.000000 Logit Scale: 31.005 Contrastive_loss: 0.00011323 (0.027632) Loss: 0.00011323 (0.027632) 2025-03-20,12:45:20 | INFO | Train Epoch: 29 [ 70432/766009 (9%)] Data (t): 0.001 Batch (t): 0.216, 142.277/s, 142.277/s/gpu LR: 0.000000 Logit Scale: 31.006 Contrastive_loss: 0.012763 (0.026986) Loss: 0.012763 (0.026986) 2025-03-20,12:45:42 | INFO | Train Epoch: 29 [ 73632/766009 (10%)] Data (t): 0.001 Batch (t): 0.213, 151.427/s, 151.427/s/gpu LR: 0.000000 Logit Scale: 31.006 Contrastive_loss: 0.022972 (0.026818) Loss: 0.022972 (0.026818) 2025-03-20,12:46:03 | INFO | Train Epoch: 29 [ 76832/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 148.372/s, 148.372/s/gpu LR: 0.000000 Logit Scale: 31.006 Contrastive_loss: 0.00047901 (0.025765) Loss: 0.00047901 (0.025765) 2025-03-20,12:46:25 | INFO | Train Epoch: 29 [ 80032/766009 (10%)] Data (t): 0.001 Batch (t): 0.217, 149.662/s, 149.662/s/gpu LR: 0.000000 Logit Scale: 31.006 Contrastive_loss: 0.040253 (0.026322) Loss: 0.040253 (0.026322) 2025-03-20,12:46:47 | INFO | Train Epoch: 29 [ 83232/766009 (11%)] Data (t): 0.001 Batch (t): 0.217, 148.718/s, 148.718/s/gpu LR: 0.000000 Logit Scale: 31.007 Contrastive_loss: 0.030601 (0.026481) Loss: 0.030601 (0.026481) 2025-03-20,12:47:08 | INFO | Train Epoch: 29 [ 86432/766009 (11%)] Data (t): 0.001 Batch (t): 0.216, 147.521/s, 147.521/s/gpu LR: 0.000000 Logit Scale: 31.007 Contrastive_loss: 0.060253 (0.027687) Loss: 0.060253 (0.027687) 2025-03-20,12:47:30 | INFO | Train Epoch: 29 [ 89632/766009 (12%)] Data (t): 0.001 Batch (t): 0.217, 148.246/s, 148.246/s/gpu LR: 0.000000 Logit Scale: 31.007 Contrastive_loss: 0.012579 (0.027166) Loss: 0.012579 (0.027166) 2025-03-20,12:47:51 | INFO | Train Epoch: 29 [ 92832/766009 (12%)] Data (t): 0.001 Batch (t): 0.214, 151.527/s, 151.527/s/gpu LR: 0.000000 Logit Scale: 31.007 Contrastive_loss: 0.11823 (0.030201) Loss: 0.11823 (0.030201) 2025-03-20,12:48:13 | INFO | Train Epoch: 29 [ 96032/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.246/s, 149.246/s/gpu LR: 0.000000 Logit Scale: 31.008 Contrastive_loss: 0.033661 (0.030313) Loss: 0.033661 (0.030313) 2025-03-20,12:48:35 | INFO | Train Epoch: 29 [ 99232/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.010/s, 149.010/s/gpu LR: 0.000000 Logit Scale: 31.008 Contrastive_loss: 0.00025044 (0.029374) Loss: 0.00025044 (0.029374) 2025-03-20,12:48:56 | INFO | Train Epoch: 29 [102432/766009 (13%)] Data (t): 0.001 Batch (t): 0.216, 149.065/s, 149.065/s/gpu LR: 0.000000 Logit Scale: 31.008 Contrastive_loss: 0.043497 (0.029802) Loss: 0.043497 (0.029802) 2025-03-20,12:49:18 | INFO | Train Epoch: 29 [105632/766009 (14%)] Data (t): 0.001 Batch (t): 0.216, 147.897/s, 147.897/s/gpu LR: 0.000000 Logit Scale: 31.008 Contrastive_loss: 0.15394 (0.033453) Loss: 0.15394 (0.033453) 2025-03-20,12:49:39 | INFO | Train Epoch: 29 [108832/766009 (14%)] Data (t): 0.001 Batch (t): 0.214, 152.003/s, 152.003/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.00025203 (0.032504) Loss: 0.00025203 (0.032504) 2025-03-20,12:50:01 | INFO | Train Epoch: 29 [112032/766009 (15%)] Data (t): 0.001 Batch (t): 0.215, 148.118/s, 148.118/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.022837 (0.032236) Loss: 0.022837 (0.032236) 2025-03-20,12:50:22 | INFO | Train Epoch: 29 [115232/766009 (15%)] Data (t): 0.001 Batch (t): 0.216, 149.044/s, 149.044/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.018715 (0.031870) Loss: 0.018715 (0.031870) 2025-03-20,12:50:44 | INFO | Train Epoch: 29 [118432/766009 (15%)] Data (t): 0.001 Batch (t): 0.218, 148.731/s, 148.731/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.00013220 (0.031035) Loss: 0.00013220 (0.031035) 2025-03-20,12:51:06 | INFO | Train Epoch: 29 [121632/766009 (16%)] Data (t): 0.001 Batch (t): 0.217, 148.109/s, 148.109/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.00010495 (0.030242) Loss: 0.00010495 (0.030242) 2025-03-20,12:51:28 | INFO | Train Epoch: 29 [124832/766009 (16%)] Data (t): 0.001 Batch (t): 0.217, 147.740/s, 147.740/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.0010312 (0.029512) Loss: 0.0010312 (0.029512) 2025-03-20,12:51:49 | INFO | Train Epoch: 29 [128032/766009 (17%)] Data (t): 0.001 Batch (t): 0.216, 149.350/s, 149.350/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.00054451 (0.028805) Loss: 0.00054451 (0.028805) 2025-03-20,12:52:10 | INFO | Train Epoch: 29 [131232/766009 (17%)] Data (t): 0.001 Batch (t): 0.213, 147.928/s, 147.928/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 2.2242e-05 (0.028120) Loss: 2.2242e-05 (0.028120) 2025-03-20,12:52:32 | INFO | Train Epoch: 29 [134432/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 150.861/s, 150.861/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 5.3750e-05 (0.027467) Loss: 5.3750e-05 (0.027467) 2025-03-20,12:52:53 | INFO | Train Epoch: 29 [137632/766009 (18%)] Data (t): 0.001 Batch (t): 0.214, 152.532/s, 152.532/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.012887 (0.027136) Loss: 0.012887 (0.027136) 2025-03-20,12:53:15 | INFO | Train Epoch: 29 [140832/766009 (18%)] Data (t): 0.001 Batch (t): 0.215, 147.799/s, 147.799/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.024224 (0.027071) Loss: 0.024224 (0.027071) 2025-03-20,12:53:37 | INFO | Train Epoch: 29 [144032/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 148.041/s, 148.041/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.0020354 (0.026527) Loss: 0.0020354 (0.026527) 2025-03-20,12:53:58 | INFO | Train Epoch: 29 [147232/766009 (19%)] Data (t): 0.001 Batch (t): 0.218, 147.638/s, 147.638/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 0.00026199 (0.025968) Loss: 0.00026199 (0.025968) 2025-03-20,12:54:20 | INFO | Train Epoch: 29 [150432/766009 (20%)] Data (t): 0.001 Batch (t): 0.216, 146.999/s, 146.999/s/gpu LR: 0.000000 Logit Scale: 31.009 Contrastive_loss: 4.9241e-05 (0.025428) Loss: 4.9241e-05 (0.025428) 2025-03-20,12:54:42 | INFO | Train Epoch: 29 [153632/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 135.080/s, 135.080/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.043321 (0.025793) Loss: 0.043321 (0.025793) 2025-03-20,12:55:03 | INFO | Train Epoch: 29 [156832/766009 (20%)] Data (t): 0.001 Batch (t): 0.217, 147.728/s, 147.728/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.067282 (0.026623) Loss: 0.067282 (0.026623) 2025-03-20,12:55:25 | INFO | Train Epoch: 29 [160032/766009 (21%)] Data (t): 0.001 Batch (t): 0.217, 148.166/s, 148.166/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.049691 (0.027075) Loss: 0.049691 (0.027075) 2025-03-20,12:55:47 | INFO | Train Epoch: 29 [163232/766009 (21%)] Data (t): 0.001 Batch (t): 0.216, 142.006/s, 142.006/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.016239 (0.026867) Loss: 0.016239 (0.026867) 2025-03-20,12:56:08 | INFO | Train Epoch: 29 [166432/766009 (22%)] Data (t): 0.001 Batch (t): 0.216, 148.187/s, 148.187/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.0025123 (0.026407) Loss: 0.0025123 (0.026407) 2025-03-20,12:56:30 | INFO | Train Epoch: 29 [169632/766009 (22%)] Data (t): 0.001 Batch (t): 0.215, 147.892/s, 147.892/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.016579 (0.026225) Loss: 0.016579 (0.026225) 2025-03-20,12:56:52 | INFO | Train Epoch: 29 [172832/766009 (23%)] Data (t): 0.001 Batch (t): 0.218, 148.533/s, 148.533/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 5.2340e-05 (0.025749) Loss: 5.2340e-05 (0.025749) 2025-03-20,12:57:13 | INFO | Train Epoch: 29 [176032/766009 (23%)] Data (t): 0.001 Batch (t): 0.215, 149.023/s, 149.023/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.010847 (0.025483) Loss: 0.010847 (0.025483) 2025-03-20,12:57:35 | INFO | Train Epoch: 29 [179232/766009 (23%)] Data (t): 0.001 Batch (t): 0.214, 149.250/s, 149.250/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.061824 (0.026121) Loss: 0.061824 (0.026121) 2025-03-20,12:57:56 | INFO | Train Epoch: 29 [182432/766009 (24%)] Data (t): 0.001 Batch (t): 0.214, 149.147/s, 149.147/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.016186 (0.025950) Loss: 0.016186 (0.025950) 2025-03-20,12:58:18 | INFO | Train Epoch: 29 [185632/766009 (24%)] Data (t): 0.001 Batch (t): 0.216, 148.049/s, 148.049/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.037582 (0.026147) Loss: 0.037582 (0.026147) 2025-03-20,12:58:39 | INFO | Train Epoch: 29 [188832/766009 (25%)] Data (t): 0.001 Batch (t): 0.217, 148.483/s, 148.483/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.0031955 (0.025764) Loss: 0.0031955 (0.025764) 2025-03-20,12:59:01 | INFO | Train Epoch: 29 [192032/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 147.728/s, 147.728/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 7.7875e-05 (0.025343) Loss: 7.7875e-05 (0.025343) 2025-03-20,12:59:22 | INFO | Train Epoch: 29 [195232/766009 (25%)] Data (t): 0.001 Batch (t): 0.216, 151.491/s, 151.491/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.00075744 (0.024947) Loss: 0.00075744 (0.024947) 2025-03-20,12:59:44 | INFO | Train Epoch: 29 [198432/766009 (26%)] Data (t): 0.001 Batch (t): 0.215, 148.483/s, 148.483/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 0.0042083 (0.024617) Loss: 0.0042083 (0.024617) 2025-03-20,13:00:06 | INFO | Train Epoch: 29 [201632/766009 (26%)] Data (t): 0.001 Batch (t): 0.217, 149.078/s, 149.078/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.080436 (0.025490) Loss: 0.080436 (0.025490) 2025-03-20,13:00:27 | INFO | Train Epoch: 29 [204832/766009 (27%)] Data (t): 0.001 Batch (t): 0.216, 147.981/s, 147.981/s/gpu LR: 0.000000 Logit Scale: 31.010 Contrastive_loss: 5.6222e-05 (0.025098) Loss: 5.6222e-05 (0.025098) 2025-03-20,13:00:49 | INFO | Train Epoch: 29 [208032/766009 (27%)] Data (t): 0.001 Batch (t): 0.215, 147.907/s, 147.907/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0011228 (0.024735) Loss: 0.0011228 (0.024735) 2025-03-20,13:01:10 | INFO | Train Epoch: 29 [211232/766009 (28%)] Data (t): 0.001 Batch (t): 0.212, 150.454/s, 150.454/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.032572 (0.024852) Loss: 0.032572 (0.024852) 2025-03-20,13:01:31 | INFO | Train Epoch: 29 [214432/766009 (28%)] Data (t): 0.001 Batch (t): 0.214, 148.434/s, 148.434/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 9.8564e-05 (0.024488) Loss: 9.8564e-05 (0.024488) 2025-03-20,13:01:53 | INFO | Train Epoch: 29 [217632/766009 (28%)] Data (t): 0.001 Batch (t): 0.215, 149.154/s, 149.154/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044129 (0.024773) Loss: 0.044129 (0.024773) 2025-03-20,13:02:14 | INFO | Train Epoch: 29 [220832/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.788/s, 148.788/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.050763 (0.025144) Loss: 0.050763 (0.025144) 2025-03-20,13:02:36 | INFO | Train Epoch: 29 [224032/766009 (29%)] Data (t): 0.001 Batch (t): 0.216, 148.494/s, 148.494/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.081879 (0.025943) Loss: 0.081879 (0.025943) 2025-03-20,13:02:58 | INFO | Train Epoch: 29 [227232/766009 (30%)] Data (t): 0.001 Batch (t): 0.216, 147.869/s, 147.869/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.011172 (0.025738) Loss: 0.011172 (0.025738) 2025-03-20,13:03:19 | INFO | Train Epoch: 29 [230432/766009 (30%)] Data (t): 0.001 Batch (t): 0.217, 147.936/s, 147.936/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 6.4744e-05 (0.025386) Loss: 6.4744e-05 (0.025386) 2025-03-20,13:03:41 | INFO | Train Epoch: 29 [233632/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 148.641/s, 148.641/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012149 (0.025207) Loss: 0.012149 (0.025207) 2025-03-20,13:04:02 | INFO | Train Epoch: 29 [236832/766009 (31%)] Data (t): 0.001 Batch (t): 0.215, 148.153/s, 148.153/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.082431 (0.025970) Loss: 0.082431 (0.025970) 2025-03-20,13:04:24 | INFO | Train Epoch: 29 [240032/766009 (31%)] Data (t): 0.001 Batch (t): 0.216, 147.784/s, 147.784/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.071410 (0.026568) Loss: 0.071410 (0.026568) 2025-03-20,13:04:46 | INFO | Train Epoch: 29 [243232/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 147.837/s, 147.837/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.011403 (0.026371) Loss: 0.011403 (0.026371) 2025-03-20,13:05:07 | INFO | Train Epoch: 29 [246432/766009 (32%)] Data (t): 0.001 Batch (t): 0.216, 149.430/s, 149.430/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.019459 (0.026283) Loss: 0.019459 (0.026283) 2025-03-20,13:05:29 | INFO | Train Epoch: 29 [249632/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 148.794/s, 148.794/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 6.3036e-05 (0.025951) Loss: 6.3036e-05 (0.025951) 2025-03-20,13:05:50 | INFO | Train Epoch: 29 [252832/766009 (33%)] Data (t): 0.001 Batch (t): 0.216, 148.290/s, 148.290/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044916 (0.026188) Loss: 0.044916 (0.026188) 2025-03-20,13:06:12 | INFO | Train Epoch: 29 [256032/766009 (33%)] Data (t): 0.001 Batch (t): 0.219, 75.6617/s, 75.6617/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043550 (0.026402) Loss: 0.043550 (0.026402) 2025-03-20,13:06:34 | INFO | Train Epoch: 29 [259232/766009 (34%)] Data (t): 0.000 Batch (t): 0.217, 148.165/s, 148.165/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012389 (0.026231) Loss: 0.012389 (0.026231) 2025-03-20,13:06:56 | INFO | Train Epoch: 29 [262432/766009 (34%)] Data (t): 0.001 Batch (t): 0.216, 149.040/s, 149.040/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0099889 (0.026036) Loss: 0.0099889 (0.026036) 2025-03-20,13:07:17 | INFO | Train Epoch: 29 [265632/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.708/s, 148.708/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 3.7070e-05 (0.025726) Loss: 3.7070e-05 (0.025726) 2025-03-20,13:07:39 | INFO | Train Epoch: 29 [268832/766009 (35%)] Data (t): 0.001 Batch (t): 0.215, 148.756/s, 148.756/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 5.3486e-05 (0.025424) Loss: 5.3486e-05 (0.025424) 2025-03-20,13:08:00 | INFO | Train Epoch: 29 [272032/766009 (36%)] Data (t): 0.001 Batch (t): 0.216, 137.899/s, 137.899/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0088665 (0.025231) Loss: 0.0088665 (0.025231) 2025-03-20,13:08:22 | INFO | Train Epoch: 29 [275232/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 149.198/s, 149.198/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.080159 (0.025863) Loss: 0.080159 (0.025863) 2025-03-20,13:08:43 | INFO | Train Epoch: 29 [278432/766009 (36%)] Data (t): 0.001 Batch (t): 0.215, 148.782/s, 148.782/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00071866 (0.025577) Loss: 0.00071866 (0.025577) 2025-03-20,13:09:05 | INFO | Train Epoch: 29 [281632/766009 (37%)] Data (t): 0.001 Batch (t): 0.215, 148.386/s, 148.386/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00010413 (0.025291) Loss: 0.00010413 (0.025291) 2025-03-20,13:09:26 | INFO | Train Epoch: 29 [284832/766009 (37%)] Data (t): 0.001 Batch (t): 0.217, 149.475/s, 149.475/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.063003 (0.025710) Loss: 0.063003 (0.025710) 2025-03-20,13:09:48 | INFO | Train Epoch: 29 [288032/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.015/s, 148.015/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.042837 (0.025898) Loss: 0.042837 (0.025898) 2025-03-20,13:10:10 | INFO | Train Epoch: 29 [291232/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 149.314/s, 149.314/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.098004 (0.026682) Loss: 0.098004 (0.026682) 2025-03-20,13:10:31 | INFO | Train Epoch: 29 [294432/766009 (38%)] Data (t): 0.001 Batch (t): 0.215, 148.810/s, 148.810/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.074849 (0.027200) Loss: 0.074849 (0.027200) 2025-03-20,13:10:53 | INFO | Train Epoch: 29 [297632/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 148.977/s, 148.977/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00031322 (0.026914) Loss: 0.00031322 (0.026914) 2025-03-20,13:11:14 | INFO | Train Epoch: 29 [300832/766009 (39%)] Data (t): 0.001 Batch (t): 0.215, 149.031/s, 149.031/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 8.2446e-05 (0.026631) Loss: 8.2446e-05 (0.026631) 2025-03-20,13:11:36 | INFO | Train Epoch: 29 [304032/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 148.778/s, 148.778/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 3.6836e-05 (0.026354) Loss: 3.6836e-05 (0.026354) 2025-03-20,13:11:57 | INFO | Train Epoch: 29 [307232/766009 (40%)] Data (t): 0.001 Batch (t): 0.216, 148.576/s, 148.576/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.015237 (0.026240) Loss: 0.015237 (0.026240) 2025-03-20,13:12:19 | INFO | Train Epoch: 29 [310432/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 148.601/s, 148.601/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.017880 (0.026154) Loss: 0.017880 (0.026154) 2025-03-20,13:12:41 | INFO | Train Epoch: 29 [313632/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 146.587/s, 146.587/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.079479 (0.026693) Loss: 0.079479 (0.026693) 2025-03-20,13:13:02 | INFO | Train Epoch: 29 [316832/766009 (41%)] Data (t): 0.001 Batch (t): 0.216, 147.177/s, 147.177/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00013596 (0.026427) Loss: 0.00013596 (0.026427) 2025-03-20,13:13:24 | INFO | Train Epoch: 29 [320032/766009 (42%)] Data (t): 0.001 Batch (t): 0.217, 147.322/s, 147.322/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.011698 (0.026282) Loss: 0.011698 (0.026282) 2025-03-20,13:13:45 | INFO | Train Epoch: 29 [323232/766009 (42%)] Data (t): 0.001 Batch (t): 0.216, 148.901/s, 148.901/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.050801 (0.026522) Loss: 0.050801 (0.026522) 2025-03-20,13:14:07 | INFO | Train Epoch: 29 [326432/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 148.980/s, 148.980/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.028614 (0.026542) Loss: 0.028614 (0.026542) 2025-03-20,13:14:28 | INFO | Train Epoch: 29 [329632/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 150.372/s, 150.372/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012281 (0.026405) Loss: 0.012281 (0.026405) 2025-03-20,13:14:50 | INFO | Train Epoch: 29 [332832/766009 (43%)] Data (t): 0.001 Batch (t): 0.215, 148.577/s, 148.577/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.059120 (0.026717) Loss: 0.059120 (0.026717) 2025-03-20,13:15:11 | INFO | Train Epoch: 29 [336032/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.802/s, 148.802/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0023872 (0.026487) Loss: 0.0023872 (0.026487) 2025-03-20,13:15:33 | INFO | Train Epoch: 29 [339232/766009 (44%)] Data (t): 0.001 Batch (t): 0.216, 148.614/s, 148.614/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.015394 (0.026384) Loss: 0.015394 (0.026384) 2025-03-20,13:15:55 | INFO | Train Epoch: 29 [342432/766009 (45%)] Data (t): 0.001 Batch (t): 0.217, 148.956/s, 148.956/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.077973 (0.026861) Loss: 0.077973 (0.026861) 2025-03-20,13:16:16 | INFO | Train Epoch: 29 [345632/766009 (45%)] Data (t): 0.001 Batch (t): 0.215, 148.637/s, 148.637/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044016 (0.027019) Loss: 0.044016 (0.027019) 2025-03-20,13:16:38 | INFO | Train Epoch: 29 [348832/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 148.664/s, 148.664/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00067013 (0.026779) Loss: 0.00067013 (0.026779) 2025-03-20,13:16:59 | INFO | Train Epoch: 29 [352032/766009 (46%)] Data (t): 0.001 Batch (t): 0.214, 151.316/s, 151.316/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00010632 (0.026539) Loss: 0.00010632 (0.026539) 2025-03-20,13:17:20 | INFO | Train Epoch: 29 [355232/766009 (46%)] Data (t): 0.001 Batch (t): 0.215, 149.050/s, 149.050/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0011863 (0.026312) Loss: 0.0011863 (0.026312) 2025-03-20,13:17:42 | INFO | Train Epoch: 29 [358432/766009 (47%)] Data (t): 0.001 Batch (t): 0.215, 151.416/s, 151.416/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.015898 (0.026220) Loss: 0.015898 (0.026220) 2025-03-20,13:18:03 | INFO | Train Epoch: 29 [361632/766009 (47%)] Data (t): 0.001 Batch (t): 0.214, 148.673/s, 148.673/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.052533 (0.026451) Loss: 0.052533 (0.026451) 2025-03-20,13:18:25 | INFO | Train Epoch: 29 [364832/766009 (48%)] Data (t): 0.001 Batch (t): 0.214, 142.535/s, 142.535/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.053296 (0.026685) Loss: 0.053296 (0.026685) 2025-03-20,13:18:46 | INFO | Train Epoch: 29 [368032/766009 (48%)] Data (t): 0.001 Batch (t): 0.216, 151.571/s, 151.571/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00021041 (0.026456) Loss: 0.00021041 (0.026456) 2025-03-20,13:19:08 | INFO | Train Epoch: 29 [371232/766009 (48%)] Data (t): 0.001 Batch (t): 0.213, 151.443/s, 151.443/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.048638 (0.026646) Loss: 0.048638 (0.026646) 2025-03-20,13:19:29 | INFO | Train Epoch: 29 [374432/766009 (49%)] Data (t): 0.001 Batch (t): 0.216, 148.042/s, 148.042/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044104 (0.026794) Loss: 0.044104 (0.026794) 2025-03-20,13:19:51 | INFO | Train Epoch: 29 [377632/766009 (49%)] Data (t): 0.001 Batch (t): 0.214, 141.928/s, 141.928/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.10619 (0.027461) Loss: 0.10619 (0.027461) 2025-03-20,13:20:12 | INFO | Train Epoch: 29 [380832/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 148.393/s, 148.393/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.016037 (0.027366) Loss: 0.016037 (0.027366) 2025-03-20,13:20:34 | INFO | Train Epoch: 29 [384032/766009 (50%)] Data (t): 0.001 Batch (t): 0.215, 150.691/s, 150.691/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.036038 (0.027438) Loss: 0.036038 (0.027438) 2025-03-20,13:20:55 | INFO | Train Epoch: 29 [387232/766009 (51%)] Data (t): 0.001 Batch (t): 0.214, 149.268/s, 149.268/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012623 (0.027316) Loss: 0.012623 (0.027316) 2025-03-20,13:21:17 | INFO | Train Epoch: 29 [390432/766009 (51%)] Data (t): 0.001 Batch (t): 0.216, 147.371/s, 147.371/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043528 (0.027448) Loss: 0.043528 (0.027448) 2025-03-20,13:21:38 | INFO | Train Epoch: 29 [393632/766009 (51%)] Data (t): 0.001 Batch (t): 0.217, 147.913/s, 147.913/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043280 (0.027576) Loss: 0.043280 (0.027576) 2025-03-20,13:22:00 | INFO | Train Epoch: 29 [396832/766009 (52%)] Data (t): 0.001 Batch (t): 0.218, 148.032/s, 148.032/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.074824 (0.027954) Loss: 0.074824 (0.027954) 2025-03-20,13:22:22 | INFO | Train Epoch: 29 [400032/766009 (52%)] Data (t): 0.001 Batch (t): 0.217, 145.699/s, 145.699/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.010604 (0.027816) Loss: 0.010604 (0.027816) 2025-03-20,13:22:44 | INFO | Train Epoch: 29 [403232/766009 (53%)] Data (t): 0.001 Batch (t): 0.217, 146.089/s, 146.089/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.017341 (0.027733) Loss: 0.017341 (0.027733) 2025-03-20,13:23:05 | INFO | Train Epoch: 29 [406432/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.642/s, 148.642/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.022514 (0.027693) Loss: 0.022514 (0.027693) 2025-03-20,13:23:27 | INFO | Train Epoch: 29 [409632/766009 (53%)] Data (t): 0.001 Batch (t): 0.216, 148.907/s, 148.907/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.14192 (0.028578) Loss: 0.14192 (0.028578) 2025-03-20,13:23:48 | INFO | Train Epoch: 29 [412832/766009 (54%)] Data (t): 0.001 Batch (t): 0.215, 150.881/s, 150.881/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.039474 (0.028662) Loss: 0.039474 (0.028662) 2025-03-20,13:24:10 | INFO | Train Epoch: 29 [416032/766009 (54%)] Data (t): 0.001 Batch (t): 0.214, 148.540/s, 148.540/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.027582 (0.028654) Loss: 0.027582 (0.028654) 2025-03-20,13:24:31 | INFO | Train Epoch: 29 [419232/766009 (55%)] Data (t): 0.001 Batch (t): 0.216, 148.318/s, 148.318/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043549 (0.028767) Loss: 0.043549 (0.028767) 2025-03-20,13:24:53 | INFO | Train Epoch: 29 [422432/766009 (55%)] Data (t): 0.001 Batch (t): 0.214, 144.387/s, 144.387/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.11786 (0.029436) Loss: 0.11786 (0.029436) 2025-03-20,13:25:14 | INFO | Train Epoch: 29 [425632/766009 (56%)] Data (t): 0.001 Batch (t): 0.215, 151.236/s, 151.236/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 7.3489e-05 (0.029217) Loss: 7.3489e-05 (0.029217) 2025-03-20,13:25:36 | INFO | Train Epoch: 29 [428832/766009 (56%)] Data (t): 0.001 Batch (t): 0.213, 149.308/s, 149.308/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044419 (0.029330) Loss: 0.044419 (0.029330) 2025-03-20,13:25:57 | INFO | Train Epoch: 29 [432032/766009 (56%)] Data (t): 0.001 Batch (t): 0.214, 151.175/s, 151.175/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043703 (0.029436) Loss: 0.043703 (0.029436) 2025-03-20,13:26:19 | INFO | Train Epoch: 29 [435232/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 149.150/s, 149.150/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0015042 (0.029232) Loss: 0.0015042 (0.029232) 2025-03-20,13:26:40 | INFO | Train Epoch: 29 [438432/766009 (57%)] Data (t): 0.001 Batch (t): 0.215, 145.300/s, 145.300/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.042432 (0.029327) Loss: 0.042432 (0.029327) 2025-03-20,13:27:02 | INFO | Train Epoch: 29 [441632/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.810/s, 148.810/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.052881 (0.029497) Loss: 0.052881 (0.029497) 2025-03-20,13:27:23 | INFO | Train Epoch: 29 [444832/766009 (58%)] Data (t): 0.001 Batch (t): 0.215, 149.106/s, 149.106/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.091473 (0.029940) Loss: 0.091473 (0.029940) 2025-03-20,13:27:45 | INFO | Train Epoch: 29 [448032/766009 (58%)] Data (t): 0.001 Batch (t): 0.216, 148.155/s, 148.155/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 7.1211e-05 (0.029728) Loss: 7.1211e-05 (0.029728) 2025-03-20,13:28:06 | INFO | Train Epoch: 29 [451232/766009 (59%)] Data (t): 0.001 Batch (t): 0.215, 148.910/s, 148.910/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.010169 (0.029590) Loss: 0.010169 (0.029590) 2025-03-20,13:28:28 | INFO | Train Epoch: 29 [454432/766009 (59%)] Data (t): 0.001 Batch (t): 0.217, 147.972/s, 147.972/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00010005 (0.029384) Loss: 0.00010005 (0.029384) 2025-03-20,13:28:50 | INFO | Train Epoch: 29 [457632/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 148.820/s, 148.820/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.020427 (0.029322) Loss: 0.020427 (0.029322) 2025-03-20,13:29:11 | INFO | Train Epoch: 29 [460832/766009 (60%)] Data (t): 0.001 Batch (t): 0.216, 146.396/s, 146.396/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.063806 (0.029559) Loss: 0.063806 (0.029559) 2025-03-20,13:29:33 | INFO | Train Epoch: 29 [464032/766009 (61%)] Data (t): 0.001 Batch (t): 0.215, 149.416/s, 149.416/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.018248 (0.029482) Loss: 0.018248 (0.029482) 2025-03-20,13:29:54 | INFO | Train Epoch: 29 [467232/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 148.421/s, 148.421/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0017597 (0.029293) Loss: 0.0017597 (0.029293) 2025-03-20,13:30:16 | INFO | Train Epoch: 29 [470432/766009 (61%)] Data (t): 0.001 Batch (t): 0.216, 146.932/s, 146.932/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012206 (0.029178) Loss: 0.012206 (0.029178) 2025-03-20,13:30:37 | INFO | Train Epoch: 29 [473632/766009 (62%)] Data (t): 0.001 Batch (t): 0.216, 149.143/s, 149.143/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00020965 (0.028983) Loss: 0.00020965 (0.028983) 2025-03-20,13:30:59 | INFO | Train Epoch: 29 [476832/766009 (62%)] Data (t): 0.001 Batch (t): 0.215, 148.904/s, 148.904/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.067955 (0.029243) Loss: 0.067955 (0.029243) 2025-03-20,13:31:21 | INFO | Train Epoch: 29 [480032/766009 (63%)] Data (t): 0.001 Batch (t): 0.217, 149.198/s, 149.198/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.063469 (0.029470) Loss: 0.063469 (0.029470) 2025-03-20,13:31:42 | INFO | Train Epoch: 29 [483232/766009 (63%)] Data (t): 0.001 Batch (t): 0.215, 148.977/s, 148.977/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00041300 (0.029279) Loss: 0.00041300 (0.029279) 2025-03-20,13:32:04 | INFO | Train Epoch: 29 [486432/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.471/s, 148.471/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.049872 (0.029413) Loss: 0.049872 (0.029413) 2025-03-20,13:32:25 | INFO | Train Epoch: 29 [489632/766009 (64%)] Data (t): 0.001 Batch (t): 0.216, 148.466/s, 148.466/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.022875 (0.029371) Loss: 0.022875 (0.029371) 2025-03-20,13:32:47 | INFO | Train Epoch: 29 [492832/766009 (64%)] Data (t): 0.001 Batch (t): 0.215, 151.781/s, 151.781/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.067523 (0.029617) Loss: 0.067523 (0.029617) 2025-03-20,13:33:08 | INFO | Train Epoch: 29 [496032/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 148.580/s, 148.580/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00016102 (0.029428) Loss: 0.00016102 (0.029428) 2025-03-20,13:33:30 | INFO | Train Epoch: 29 [499232/766009 (65%)] Data (t): 0.001 Batch (t): 0.215, 148.830/s, 148.830/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 8.7636e-05 (0.029241) Loss: 8.7636e-05 (0.029241) 2025-03-20,13:33:51 | INFO | Train Epoch: 29 [502432/766009 (66%)] Data (t): 0.001 Batch (t): 0.216, 148.891/s, 148.891/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.037543 (0.029294) Loss: 0.037543 (0.029294) 2025-03-20,13:34:13 | INFO | Train Epoch: 29 [505632/766009 (66%)] Data (t): 0.001 Batch (t): 0.215, 148.259/s, 148.259/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.032668 (0.029315) Loss: 0.032668 (0.029315) 2025-03-20,13:34:34 | INFO | Train Epoch: 29 [508832/766009 (66%)] Data (t): 0.001 Batch (t): 0.217, 148.707/s, 148.707/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 4.6676e-05 (0.029132) Loss: 4.6676e-05 (0.029132) 2025-03-20,13:34:56 | INFO | Train Epoch: 29 [512032/766009 (67%)] Data (t): 0.001 Batch (t): 0.216, 147.996/s, 147.996/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00018228 (0.028952) Loss: 0.00018228 (0.028952) 2025-03-20,13:35:17 | INFO | Train Epoch: 29 [515232/766009 (67%)] Data (t): 0.001 Batch (t): 0.214, 147.730/s, 147.730/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0048386 (0.028803) Loss: 0.0048386 (0.028803) 2025-03-20,13:35:39 | INFO | Train Epoch: 29 [518432/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 148.272/s, 148.272/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00056369 (0.028630) Loss: 0.00056369 (0.028630) 2025-03-20,13:36:01 | INFO | Train Epoch: 29 [521632/766009 (68%)] Data (t): 0.001 Batch (t): 0.216, 148.359/s, 148.359/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.048216 (0.028750) Loss: 0.048216 (0.028750) 2025-03-20,13:36:22 | INFO | Train Epoch: 29 [524832/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 148.114/s, 148.114/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.017597 (0.028682) Loss: 0.017597 (0.028682) 2025-03-20,13:36:44 | INFO | Train Epoch: 29 [528032/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 148.134/s, 148.134/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 3.1121e-05 (0.028509) Loss: 3.1121e-05 (0.028509) 2025-03-20,13:37:05 | INFO | Train Epoch: 29 [531232/766009 (69%)] Data (t): 0.001 Batch (t): 0.216, 147.975/s, 147.975/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.089812 (0.028877) Loss: 0.089812 (0.028877) 2025-03-20,13:37:27 | INFO | Train Epoch: 29 [534432/766009 (70%)] Data (t): 0.001 Batch (t): 0.216, 148.872/s, 148.872/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.058551 (0.029053) Loss: 0.058551 (0.029053) 2025-03-20,13:37:49 | INFO | Train Epoch: 29 [537632/766009 (70%)] Data (t): 0.001 Batch (t): 0.217, 147.691/s, 147.691/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00087037 (0.028886) Loss: 0.00087037 (0.028886) 2025-03-20,13:38:10 | INFO | Train Epoch: 29 [540832/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 147.542/s, 147.542/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.061730 (0.029080) Loss: 0.061730 (0.029080) 2025-03-20,13:38:32 | INFO | Train Epoch: 29 [544032/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 148.794/s, 148.794/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.13894 (0.029722) Loss: 0.13894 (0.029722) 2025-03-20,13:38:53 | INFO | Train Epoch: 29 [547232/766009 (71%)] Data (t): 0.001 Batch (t): 0.216, 148.200/s, 148.200/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.019835 (0.029665) Loss: 0.019835 (0.029665) 2025-03-20,13:39:15 | INFO | Train Epoch: 29 [550432/766009 (72%)] Data (t): 0.001 Batch (t): 0.216, 148.898/s, 148.898/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043375 (0.029744) Loss: 0.043375 (0.029744) 2025-03-20,13:39:36 | INFO | Train Epoch: 29 [553632/766009 (72%)] Data (t): 0.001 Batch (t): 0.215, 148.118/s, 148.118/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0060437 (0.029608) Loss: 0.0060437 (0.029608) 2025-03-20,13:39:58 | INFO | Train Epoch: 29 [556832/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 148.779/s, 148.779/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.029784 (0.029609) Loss: 0.029784 (0.029609) 2025-03-20,13:40:20 | INFO | Train Epoch: 29 [560032/766009 (73%)] Data (t): 0.001 Batch (t): 0.215, 148.325/s, 148.325/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.011934 (0.029508) Loss: 0.011934 (0.029508) 2025-03-20,13:40:41 | INFO | Train Epoch: 29 [563232/766009 (74%)] Data (t): 0.001 Batch (t): 0.217, 84.4460/s, 84.4460/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.035934 (0.029545) Loss: 0.035934 (0.029545) 2025-03-20,13:41:03 | INFO | Train Epoch: 29 [566432/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.341/s, 149.341/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.076012 (0.029806) Loss: 0.076012 (0.029806) 2025-03-20,13:41:24 | INFO | Train Epoch: 29 [569632/766009 (74%)] Data (t): 0.001 Batch (t): 0.215, 149.016/s, 149.016/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0027702 (0.029655) Loss: 0.0027702 (0.029655) 2025-03-20,13:41:46 | INFO | Train Epoch: 29 [572832/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 148.945/s, 148.945/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.094368 (0.030014) Loss: 0.094368 (0.030014) 2025-03-20,13:42:07 | INFO | Train Epoch: 29 [576032/766009 (75%)] Data (t): 0.001 Batch (t): 0.215, 148.866/s, 148.866/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.050124 (0.030125) Loss: 0.050124 (0.030125) 2025-03-20,13:42:29 | INFO | Train Epoch: 29 [579232/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 148.419/s, 148.419/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.045890 (0.030212) Loss: 0.045890 (0.030212) 2025-03-20,13:42:50 | INFO | Train Epoch: 29 [582432/766009 (76%)] Data (t): 0.001 Batch (t): 0.214, 148.800/s, 148.800/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012953 (0.030117) Loss: 0.012953 (0.030117) 2025-03-20,13:43:12 | INFO | Train Epoch: 29 [585632/766009 (76%)] Data (t): 0.001 Batch (t): 0.215, 148.613/s, 148.613/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044333 (0.030195) Loss: 0.044333 (0.030195) 2025-03-20,13:43:33 | INFO | Train Epoch: 29 [588832/766009 (77%)] Data (t): 0.001 Batch (t): 0.216, 147.319/s, 147.319/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.16376 (0.030917) Loss: 0.16376 (0.030917) 2025-03-20,13:43:55 | INFO | Train Epoch: 29 [592032/766009 (77%)] Data (t): 0.001 Batch (t): 0.217, 148.640/s, 148.640/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 2.2938e-05 (0.030751) Loss: 2.2938e-05 (0.030751) 2025-03-20,13:44:17 | INFO | Train Epoch: 29 [595232/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 148.373/s, 148.373/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 7.4856e-05 (0.030587) Loss: 7.4856e-05 (0.030587) 2025-03-20,13:44:38 | INFO | Train Epoch: 29 [598432/766009 (78%)] Data (t): 0.001 Batch (t): 0.216, 148.316/s, 148.316/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0074327 (0.030463) Loss: 0.0074327 (0.030463) 2025-03-20,13:45:00 | INFO | Train Epoch: 29 [601632/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.530/s, 148.530/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.023706 (0.030428) Loss: 0.023706 (0.030428) 2025-03-20,13:45:21 | INFO | Train Epoch: 29 [604832/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.311/s, 148.311/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.048330 (0.030522) Loss: 0.048330 (0.030522) 2025-03-20,13:45:43 | INFO | Train Epoch: 29 [608032/766009 (79%)] Data (t): 0.001 Batch (t): 0.216, 148.717/s, 148.717/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.042739 (0.030586) Loss: 0.042739 (0.030586) 2025-03-20,13:46:05 | INFO | Train Epoch: 29 [611232/766009 (80%)] Data (t): 0.001 Batch (t): 0.216, 148.219/s, 148.219/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.044426 (0.030658) Loss: 0.044426 (0.030658) 2025-03-20,13:46:26 | INFO | Train Epoch: 29 [614432/766009 (80%)] Data (t): 0.001 Batch (t): 0.217, 147.805/s, 147.805/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.024102 (0.030624) Loss: 0.024102 (0.030624) 2025-03-20,13:46:48 | INFO | Train Epoch: 29 [617632/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 153.871/s, 153.871/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.069529 (0.030824) Loss: 0.069529 (0.030824) 2025-03-20,13:47:09 | INFO | Train Epoch: 29 [620832/766009 (81%)] Data (t): 0.001 Batch (t): 0.216, 151.626/s, 151.626/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043537 (0.030890) Loss: 0.043537 (0.030890) 2025-03-20,13:47:31 | INFO | Train Epoch: 29 [624032/766009 (81%)] Data (t): 0.001 Batch (t): 0.215, 148.818/s, 148.818/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 3.0706e-05 (0.030732) Loss: 3.0706e-05 (0.030732) 2025-03-20,13:47:52 | INFO | Train Epoch: 29 [627232/766009 (82%)] Data (t): 0.001 Batch (t): 0.214, 150.172/s, 150.172/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0046340 (0.030600) Loss: 0.0046340 (0.030600) 2025-03-20,13:48:13 | INFO | Train Epoch: 29 [630432/766009 (82%)] Data (t): 0.001 Batch (t): 0.212, 150.908/s, 150.908/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 2.7411e-05 (0.030445) Loss: 2.7411e-05 (0.030445) 2025-03-20,13:48:35 | INFO | Train Epoch: 29 [633632/766009 (83%)] Data (t): 0.001 Batch (t): 0.215, 148.685/s, 148.685/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.098892 (0.030789) Loss: 0.098892 (0.030789) 2025-03-20,13:48:56 | INFO | Train Epoch: 29 [636832/766009 (83%)] Data (t): 0.001 Batch (t): 0.216, 148.917/s, 148.917/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.16173 (0.031444) Loss: 0.16173 (0.031444) 2025-03-20,13:49:18 | INFO | Train Epoch: 29 [640032/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.056/s, 148.056/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.042557 (0.031499) Loss: 0.042557 (0.031499) 2025-03-20,13:49:40 | INFO | Train Epoch: 29 [643232/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.605/s, 148.605/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0023586 (0.031355) Loss: 0.0023586 (0.031355) 2025-03-20,13:50:01 | INFO | Train Epoch: 29 [646432/766009 (84%)] Data (t): 0.001 Batch (t): 0.216, 148.456/s, 148.456/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 4.2206e-05 (0.031201) Loss: 4.2206e-05 (0.031201) 2025-03-20,13:50:23 | INFO | Train Epoch: 29 [649632/766009 (85%)] Data (t): 0.001 Batch (t): 0.217, 147.278/s, 147.278/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043362 (0.031260) Loss: 0.043362 (0.031260) 2025-03-20,13:50:44 | INFO | Train Epoch: 29 [652832/766009 (85%)] Data (t): 0.001 Batch (t): 0.215, 149.329/s, 149.329/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0013175 (0.031114) Loss: 0.0013175 (0.031114) 2025-03-20,13:51:06 | INFO | Train Epoch: 29 [656032/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 149.342/s, 149.342/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 1.5860e-05 (0.030963) Loss: 1.5860e-05 (0.030963) 2025-03-20,13:51:28 | INFO | Train Epoch: 29 [659232/766009 (86%)] Data (t): 0.001 Batch (t): 0.215, 149.481/s, 149.481/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0027873 (0.030827) Loss: 0.0027873 (0.030827) 2025-03-20,13:51:49 | INFO | Train Epoch: 29 [662432/766009 (86%)] Data (t): 0.001 Batch (t): 0.216, 147.289/s, 147.289/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0015158 (0.030686) Loss: 0.0015158 (0.030686) 2025-03-20,13:52:11 | INFO | Train Epoch: 29 [665632/766009 (87%)] Data (t): 0.001 Batch (t): 0.216, 148.953/s, 148.953/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.016121 (0.030617) Loss: 0.016121 (0.030617) 2025-03-20,13:52:32 | INFO | Train Epoch: 29 [668832/766009 (87%)] Data (t): 0.001 Batch (t): 0.215, 148.892/s, 148.892/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.15509 (0.031209) Loss: 0.15509 (0.031209) 2025-03-20,13:52:54 | INFO | Train Epoch: 29 [672032/766009 (88%)] Data (t): 0.001 Batch (t): 0.216, 148.796/s, 148.796/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043469 (0.031267) Loss: 0.043469 (0.031267) 2025-03-20,13:53:16 | INFO | Train Epoch: 29 [675232/766009 (88%)] Data (t): 0.001 Batch (t): 0.217, 150.669/s, 150.669/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.068738 (0.031444) Loss: 0.068738 (0.031444) 2025-03-20,13:53:37 | INFO | Train Epoch: 29 [678432/766009 (89%)] Data (t): 0.001 Batch (t): 0.215, 148.902/s, 148.902/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0087914 (0.031338) Loss: 0.0087914 (0.031338) 2025-03-20,13:53:59 | INFO | Train Epoch: 29 [681632/766009 (89%)] Data (t): 0.001 Batch (t): 0.216, 148.870/s, 148.870/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00017948 (0.031192) Loss: 0.00017948 (0.031192) 2025-03-20,13:54:20 | INFO | Train Epoch: 29 [684832/766009 (89%)] Data (t): 0.001 Batch (t): 0.214, 150.913/s, 150.913/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 1.8824e-05 (0.031047) Loss: 1.8824e-05 (0.031047) 2025-03-20,13:54:42 | INFO | Train Epoch: 29 [688032/766009 (90%)] Data (t): 0.001 Batch (t): 0.214, 149.332/s, 149.332/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043285 (0.031104) Loss: 0.043285 (0.031104) 2025-03-20,13:55:03 | INFO | Train Epoch: 29 [691232/766009 (90%)] Data (t): 0.001 Batch (t): 0.215, 149.039/s, 149.039/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0020894 (0.030970) Loss: 0.0020894 (0.030970) 2025-03-20,13:55:25 | INFO | Train Epoch: 29 [694432/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 147.354/s, 147.354/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.024066 (0.030939) Loss: 0.024066 (0.030939) 2025-03-20,13:55:46 | INFO | Train Epoch: 29 [697632/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 147.915/s, 147.915/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 3.9628e-05 (0.030797) Loss: 3.9628e-05 (0.030797) 2025-03-20,13:56:07 | INFO | Train Epoch: 29 [700832/766009 (91%)] Data (t): 0.001 Batch (t): 0.214, 149.887/s, 149.887/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00045166 (0.030660) Loss: 0.00045166 (0.030660) 2025-03-20,13:56:29 | INFO | Train Epoch: 29 [704032/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 150.950/s, 150.950/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00019144 (0.030522) Loss: 0.00019144 (0.030522) 2025-03-20,13:56:50 | INFO | Train Epoch: 29 [707232/766009 (92%)] Data (t): 0.001 Batch (t): 0.215, 148.759/s, 148.759/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.012673 (0.030441) Loss: 0.012673 (0.030441) 2025-03-20,13:57:12 | INFO | Train Epoch: 29 [710432/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 143.016/s, 143.016/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.042659 (0.030496) Loss: 0.042659 (0.030496) 2025-03-20,13:57:34 | INFO | Train Epoch: 29 [713632/766009 (93%)] Data (t): 0.001 Batch (t): 0.216, 148.244/s, 148.244/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00020399 (0.030361) Loss: 0.00020399 (0.030361) 2025-03-20,13:57:55 | INFO | Train Epoch: 29 [716832/766009 (94%)] Data (t): 0.001 Batch (t): 0.216, 148.834/s, 148.834/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.087771 (0.030616) Loss: 0.087771 (0.030616) 2025-03-20,13:58:16 | INFO | Train Epoch: 29 [720032/766009 (94%)] Data (t): 0.001 Batch (t): 0.214, 147.345/s, 147.345/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0072079 (0.030512) Loss: 0.0072079 (0.030512) 2025-03-20,13:58:38 | INFO | Train Epoch: 29 [723232/766009 (94%)] Data (t): 0.001 Batch (t): 0.215, 149.402/s, 149.402/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00014380 (0.030379) Loss: 0.00014380 (0.030379) 2025-03-20,13:58:59 | INFO | Train Epoch: 29 [726432/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 149.090/s, 149.090/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0020099 (0.030254) Loss: 0.0020099 (0.030254) 2025-03-20,13:59:21 | INFO | Train Epoch: 29 [729632/766009 (95%)] Data (t): 0.001 Batch (t): 0.215, 148.901/s, 148.901/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.020915 (0.030213) Loss: 0.020915 (0.030213) 2025-03-20,13:59:42 | INFO | Train Epoch: 29 [732832/766009 (96%)] Data (t): 0.001 Batch (t): 0.216, 149.132/s, 149.132/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0032958 (0.030096) Loss: 0.0032958 (0.030096) 2025-03-20,14:00:04 | INFO | Train Epoch: 29 [736032/766009 (96%)] Data (t): 0.001 Batch (t): 0.214, 150.558/s, 150.558/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.071126 (0.030274) Loss: 0.071126 (0.030274) 2025-03-20,14:00:25 | INFO | Train Epoch: 29 [739232/766009 (97%)] Data (t): 0.001 Batch (t): 0.212, 150.791/s, 150.791/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.067263 (0.030433) Loss: 0.067263 (0.030433) 2025-03-20,14:00:46 | INFO | Train Epoch: 29 [742432/766009 (97%)] Data (t): 0.001 Batch (t): 0.213, 150.601/s, 150.601/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 8.5499e-05 (0.030303) Loss: 8.5499e-05 (0.030303) 2025-03-20,14:01:08 | INFO | Train Epoch: 29 [745632/766009 (97%)] Data (t): 0.001 Batch (t): 0.214, 149.347/s, 149.347/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0059945 (0.030199) Loss: 0.0059945 (0.030199) 2025-03-20,14:01:29 | INFO | Train Epoch: 29 [748832/766009 (98%)] Data (t): 0.001 Batch (t): 0.215, 149.405/s, 149.405/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043772 (0.030257) Loss: 0.043772 (0.030257) 2025-03-20,14:01:51 | INFO | Train Epoch: 29 [752032/766009 (98%)] Data (t): 0.001 Batch (t): 0.214, 147.437/s, 147.437/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0034252 (0.030143) Loss: 0.0034252 (0.030143) 2025-03-20,14:02:12 | INFO | Train Epoch: 29 [755232/766009 (99%)] Data (t): 0.001 Batch (t): 0.213, 149.967/s, 149.967/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.043515 (0.030200) Loss: 0.043515 (0.030200) 2025-03-20,14:02:34 | INFO | Train Epoch: 29 [758432/766009 (99%)] Data (t): 0.001 Batch (t): 0.217, 168.838/s, 168.838/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.046434 (0.030268) Loss: 0.046434 (0.030268) 2025-03-20,14:02:55 | INFO | Train Epoch: 29 [761632/766009 (99%)] Data (t): 0.001 Batch (t): 0.214, 149.096/s, 149.096/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 8.4238e-05 (0.030142) Loss: 8.4238e-05 (0.030142) 2025-03-20,14:03:17 | INFO | Train Epoch: 29 [764832/766009 (100%)] Data (t): 0.001 Batch (t): 0.218, 147.813/s, 147.813/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.0016508 (0.030023) Loss: 0.0016508 (0.030023) 2025-03-20,14:03:25 | INFO | Train Epoch: 29 [765984/766009 (100%)] Data (t): 0.004 Batch (t): 0.219, 147.777/s, 147.777/s/gpu LR: 0.000000 Logit Scale: 31.011 Contrastive_loss: 0.00054518 (0.029901) Loss: 0.00054518 (0.029901) 2025-03-20,14:03:25 | INFO | Eval Epoch: 30 [32 / 7443] Clip Loss: 4.082745 2025-03-20,14:03:31 | INFO | Eval Epoch: 30 [3232 / 7443] Clip Loss: 0.856130 2025-03-20,14:03:37 | INFO | Eval Epoch: 30 [6432 / 7443] Clip Loss: 0.639552 2025-03-20,14:03:40 | INFO | Eval Epoch: 30 image_to_text_mean_rank: 76.5275 image_to_text_median_rank: 4.0000 image_to_text_R@1: 0.2174 image_to_text_R@5: 0.5710 image_to_text_R@10: 0.7285 text_to_image_mean_rank: 53.0009 text_to_image_median_rank: 4.0000 text_to_image_R@1: 0.2190 text_to_image_R@5: 0.5654 text_to_image_R@10: 0.7189 clip_val_loss: 0.5940 epoch: 30.0000 num_samples: 7443.0000