learning_rate: 0.0002
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.1177	0.6667	100	0.5870	0.8358	0.8505	0.8358	0.8374
0.0716	1.3333	200	0.5973	0.84	0.8532	0.84	0.8370
0.0493	2.0	300	0.8088	0.815	0.8349	0.815	0.8146
0.0037	2.6667	400	0.9701	0.8183	0.8454	0.8183	0.8159
0.0835	3.3333	500	0.5530	0.8733	0.8738	0.8733	0.8688
0.0169	4.0	600	0.9139	0.8008	0.8338	0.8008	0.7979
0.0019	4.6667	700	0.8676	0.84	0.8546	0.84	0.8397
0.0013	5.3333	800	0.7638	0.8525	0.8594	0.8525	0.8506
0.0011	6.0	900	0.7257	0.8675	0.8711	0.8675	0.8658
0.0009	6.6667	1000	0.7446	0.8717	0.8746	0.8717	0.8695
0.0008	7.3333	1100	0.7601	0.8725	0.8759	0.8725	0.8702
0.0007	8.0	1200	0.7734	0.8725	0.8759	0.8725	0.8702
0.0006	8.6667	1300	0.7845	0.8725	0.8762	0.8725	0.8702
0.0006	9.3333	1400	0.7941	0.8717	0.8753	0.8717	0.8692
0.0005	10.0	1500	0.8019	0.8717	0.8753	0.8717	0.8692
0.0005	10.6667	1600	0.8085	0.8717	0.8753	0.8717	0.8692
0.0005	11.3333	1700	0.8148	0.8717	0.8753	0.8717	0.8692
0.0004	12.0	1800	0.8197	0.8717	0.8753	0.8717	0.8692
0.0004	12.6667	1900	0.8236	0.8717	0.8753	0.8717	0.8692
0.0004	13.3333	2000	0.8268	0.8717	0.8753	0.8717	0.8692
0.0004	14.0	2100	0.8289	0.8717	0.8753	0.8717	0.8692
0.0004	14.6667	2200	0.8300	0.8717	0.8753	0.8717	0.8692

Ivanrs/vit-finetune-kidney-stone-Jonathan_El-Beze_-w256_1k_v1-_SUR-finetune

vit-finetune-kidney-stone-Jonathan_El-Beze_-w256_1k_v1-_SUR-finetune