vit_fold_1_v3

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

• Loss: 1.1599
• Accuracy: 0.9519
• F1 Score: 0.9528
• Recall: 0.9537

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: 1e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 100
• label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.8856	1.0	20	2.9355	0.2308	0.2215	0.2173
2.6469	2.0	40	2.7349	0.3462	0.3300	0.3278
2.3026	3.0	60	2.4305	0.5385	0.5365	0.5293
1.9124	4.0	80	2.0391	0.7179	0.7186	0.7155
1.5476	5.0	100	1.6601	0.8109	0.8161	0.8111
1.3336	6.0	120	1.4040	0.8910	0.8952	0.8987
1.2139	7.0	140	1.2838	0.9199	0.9223	0.9207
1.1520	8.0	160	1.2277	0.9295	0.9316	0.9306
1.0852	9.0	180	1.2013	0.9455	0.9467	0.9476
1.0903	10.0	200	1.1819	0.9423	0.9436	0.9439
1.0677	11.0	220	1.1724	0.9487	0.9499	0.9513
1.0281	12.0	240	1.1847	0.9487	0.9499	0.9513
1.0140	13.0	260	1.1599	0.9519	0.9528	0.9537
1.0105	14.0	280	1.1549	0.9519	0.9528	0.9537
0.9910	15.0	300	1.1596	0.9519	0.9528	0.9537
0.9909	16.0	320	1.1607	0.9519	0.9525	0.9525
0.9890	17.0	340	1.1589	0.9487	0.9496	0.9501
0.9742	18.0	360	1.1694	0.9487	0.9496	0.9501
0.9855	19.0	380	1.1700	0.9455	0.9467	0.9476
0.9839	20.0	400	1.1701	0.9487	0.9496	0.9501
0.9736	21.0	420	1.1686	0.9487	0.9496	0.9501
0.9678	22.0	440	1.1670	0.9487	0.9496	0.9501
0.9718	23.0	460	1.1681	0.9519	0.9525	0.9525
0.9782	24.0	480	1.1579	0.9519	0.9525	0.9525

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2