vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_SEC

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

• Loss: 0.3802
• Accuracy: 0.8975
• Precision: 0.9004
• Recall: 0.8975
• F1: 0.8961

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: 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 results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.1982	0.6667	100	0.5328	0.8342	0.8678	0.8342	0.8304
0.103	1.3333	200	0.5614	0.8342	0.8518	0.8342	0.8235
0.0646	2.0	300	0.3802	0.8975	0.9004	0.8975	0.8961
0.0206	2.6667	400	0.5236	0.8908	0.8932	0.8908	0.8910
0.0073	3.3333	500	0.4848	0.885	0.9037	0.885	0.8879
0.0237	4.0	600	0.6534	0.8617	0.8872	0.8617	0.8633
0.0414	4.6667	700	0.5937	0.8808	0.8914	0.8808	0.8782
0.0027	5.3333	800	0.5129	0.8933	0.8992	0.8933	0.8953
0.0023	6.0	900	0.6645	0.8867	0.9012	0.8867	0.8876
0.0017	6.6667	1000	0.4428	0.9158	0.9162	0.9158	0.9158
0.0014	7.3333	1100	0.4490	0.9183	0.9188	0.9183	0.9183
0.0012	8.0	1200	0.4573	0.9183	0.9188	0.9183	0.9183
0.0011	8.6667	1300	0.4643	0.9183	0.9186	0.9183	0.9182
0.001	9.3333	1400	0.4724	0.9175	0.9178	0.9175	0.9174
0.0009	10.0	1500	0.4783	0.9192	0.9196	0.9192	0.9191
0.0008	10.6667	1600	0.4834	0.92	0.9205	0.92	0.9200
0.0008	11.3333	1700	0.4880	0.9183	0.9188	0.9183	0.9183
0.0007	12.0	1800	0.4913	0.9192	0.9196	0.9192	0.9191
0.0007	12.6667	1900	0.4946	0.9192	0.9196	0.9192	0.9191
0.0007	13.3333	2000	0.4967	0.9192	0.9196	0.9192	0.9191
0.0006	14.0	2100	0.4982	0.9192	0.9196	0.9192	0.9191
0.0006	14.6667	2200	0.4990	0.9192	0.9196	0.9192	0.9191

Framework versions

• Transformers 4.48.2
• Pytorch 2.6.0+cu126
• Datasets 3.2.0
• Tokenizers 0.21.0