learning_rate: 8e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
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: 15
num_epochs: 10
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Auc Healing Status	Auc Erythema	Auc Edema	Auc Infection Risk	Auc Urgency	Auc Exudate	Roc Auc Macro
2.8488	1.0	30	1.2108	0.5414	0.4623	0.5294	0.5111	0.6648	0.7394	0.5747
4.0321	2.0	60	1.1783	0.6129	0.4752	0.5598	0.6247	0.7527	0.8253	0.6418
2.9959	3.0	90	1.1244	0.6922	0.5413	0.5549	0.8074	0.7816	0.8768	0.7090
3.7801	4.0	120	1.0727	0.7249	0.5837	0.6	0.8580	0.8036	0.9152	0.7475
3.1872	5.0	150	0.9895	0.7690	0.6333	0.6196	0.8451	0.7974	0.9434	0.7680
2.6057	6.0	180	0.9700	0.7937	0.6488	0.6373	0.8432	0.8125	0.9434	0.7798
1.7604	7.0	210	0.9973	0.7981	0.6648	0.6471	0.8494	0.8104	0.9535	0.7872
1.9733	8.0	240	1.0000	0.8069	0.6710	0.6529	0.8481	0.8098	0.9495	0.7897
2.6166	9.0	270	0.9944	0.8060	0.6622	0.6529	0.8481	0.8091	0.9495	0.7880
3.3173	10.0	300	0.9966	0.8060	0.6622	0.6529	0.8494	0.8084	0.9495	0.7881

Niro1x/medsiglip-448-surgwound-v2

medsiglip-448-surgwound-v2