test_model_7

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.8939
• F1 Macro: 0.0651
• F1 Micro: 0.2045
• F1 Weighted: 0.0913
• Precision Macro: 0.0760
• Precision Micro: 0.2045
• Precision Weighted: 0.1037
• Recall Macro: 0.1437
• Recall Micro: 0.2045
• Recall Weighted: 0.2045
• Accuracy: 0.2045

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	F1 Weighted	Precision Macro	Precision Micro	Precision Weighted	Recall Macro	Recall Micro	Recall Weighted	Accuracy
No log	0.8	3	1.9112	0.0464	0.1894	0.0664	0.0281	0.1894	0.0403	0.1323	0.1894	0.1894	0.1894
No log	1.8	6	1.8938	0.0654	0.2045	0.0917	0.0762	0.2045	0.1040	0.1437	0.2045	0.2045	0.2045

Framework versions

• Transformers 4.48.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0