test_model_90

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the corranm/first_vote_100_per_new2 dataset. It achieves the following results on the evaluation set:

• Loss: 1.8966
• F1 Macro: 0.1255
• F1 Micro: 0.2652
• F1 Weighted: 0.1671
• Precision Macro: 0.1232
• Precision Micro: 0.2652
• Precision Weighted: 0.1573
• Recall Macro: 0.1971
• Recall Micro: 0.2652
• Recall Weighted: 0.2652
• Accuracy: 0.2652

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
1.9497	0.8	3	1.8943	0.1087	0.2197	0.1434	0.1559	0.2197	0.1899	0.1632	0.2197	0.2197	0.2197
1.8932	1.8	6	1.8811	0.0832	0.2121	0.1143	0.0925	0.2121	0.1296	0.1579	0.2121	0.2121	0.2121

Framework versions

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