learning_rate: 0.0002
train_batch_size: 8
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: 10
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.3755	0.0840	100	2.1189	0.3766	0.2053
0.8418	0.1679	200	2.2810	0.3067	0.1753
0.7683	0.2519	300	1.8360	0.4570	0.3601
0.6862	0.3359	400	1.1806	0.6685	0.5573
0.4868	0.4198	500	1.0167	0.6855	0.6038
0.3877	0.5038	600	1.0808	0.6763	0.5733
0.4511	0.5877	700	1.2495	0.6708	0.5675
0.2773	0.6717	800	0.9806	0.7260	0.5884
0.3842	0.7557	900	0.9603	0.6961	0.5869
0.5853	0.8396	1000	1.1610	0.7113	0.6741
0.375	0.9236	1100	1.0845	0.6874	0.6517
0.4001	1.0076	1200	0.7816	0.7913	0.7468
0.2953	1.0915	1300	0.9925	0.7398	0.6851
0.1258	1.1755	1400	0.8799	0.7834	0.7269
0.2446	1.2594	1500	0.8710	0.7646	0.7369
0.2414	1.3434	1600	0.9198	0.7664	0.7199
0.1738	1.4274	1700	0.8230	0.7972	0.7353
0.2122	1.5113	1800	0.9513	0.7664	0.7091
0.153	1.5953	1900	0.8115	0.7766	0.7432
0.159	1.6793	2000	0.9063	0.7940	0.7453
0.1874	1.7632	2100	0.9330	0.8014	0.7365
0.1999	1.8472	2200	0.8745	0.7692	0.6915

redbioma/efficientvit-b0.r224-in1k-cemede-redbioma-ct

efficient-vit-CEMEDE-og