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# vit-base-kidney-stone-3-Michel_Daudon_-w256_1k_v1-_SUR

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0259
- Accuracy: 0.7850
- Precision: 0.7927
- Recall: 0.7850
- F1: 0.7850

## 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: 16
- 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3729        | 0.3333  | 100  | 1.0563          | 0.6631   | 0.7502    | 0.6631 | 0.6797 |
| 0.2029        | 0.6667  | 200  | 1.2777          | 0.7056   | 0.7455    | 0.7056 | 0.6872 |
| 0.1969        | 1.0     | 300  | 1.1211          | 0.7653   | 0.7679    | 0.7653 | 0.7600 |
| 0.1467        | 1.3333  | 400  | 1.2951          | 0.7048   | 0.7488    | 0.7048 | 0.7088 |
| 0.1034        | 1.6667  | 500  | 1.1112          | 0.8087   | 0.8384    | 0.8087 | 0.8075 |
| 0.0749        | 2.0     | 600  | 1.3484          | 0.7441   | 0.7662    | 0.7441 | 0.7478 |
| 0.0913        | 2.3333  | 700  | 1.0259          | 0.7850   | 0.7927    | 0.7850 | 0.7850 |
| 0.0138        | 2.6667  | 800  | 1.4442          | 0.7457   | 0.8109    | 0.7457 | 0.7557 |
| 0.0551        | 3.0     | 900  | 1.3089          | 0.7449   | 0.8007    | 0.7449 | 0.7480 |
| 0.0209        | 3.3333  | 1000 | 1.5728          | 0.7441   | 0.8047    | 0.7441 | 0.7321 |
| 0.0243        | 3.6667  | 1100 | 1.2074          | 0.7817   | 0.8299    | 0.7817 | 0.7875 |
| 0.0015        | 4.0     | 1200 | 1.2362          | 0.7817   | 0.8110    | 0.7817 | 0.7755 |
| 0.0491        | 4.3333  | 1300 | 1.6820          | 0.7089   | 0.7648    | 0.7089 | 0.7121 |
| 0.0041        | 4.6667  | 1400 | 1.2421          | 0.7629   | 0.7794    | 0.7629 | 0.7656 |
| 0.0014        | 5.0     | 1500 | 1.5195          | 0.7400   | 0.7439    | 0.7400 | 0.7395 |
| 0.001         | 5.3333  | 1600 | 1.3705          | 0.7596   | 0.7567    | 0.7596 | 0.7551 |
| 0.0008        | 5.6667  | 1700 | 1.3614          | 0.7637   | 0.7652    | 0.7637 | 0.7619 |
| 0.0007        | 6.0     | 1800 | 1.3627          | 0.7694   | 0.7676    | 0.7694 | 0.7662 |
| 0.0006        | 6.3333  | 1900 | 1.3871          | 0.7694   | 0.7682    | 0.7694 | 0.7667 |
| 0.0006        | 6.6667  | 2000 | 1.4079          | 0.7678   | 0.7664    | 0.7678 | 0.7649 |
| 0.0005        | 7.0     | 2100 | 1.4300          | 0.7653   | 0.7636    | 0.7653 | 0.7622 |
| 0.0005        | 7.3333  | 2200 | 1.4476          | 0.7661   | 0.7658    | 0.7661 | 0.7637 |
| 0.0004        | 7.6667  | 2300 | 1.4655          | 0.7678   | 0.7680    | 0.7678 | 0.7655 |
| 0.0004        | 8.0     | 2400 | 1.4802          | 0.7678   | 0.7675    | 0.7678 | 0.7652 |
| 0.0004        | 8.3333  | 2500 | 1.4962          | 0.7678   | 0.7682    | 0.7678 | 0.7655 |
| 0.0004        | 8.6667  | 2600 | 1.5100          | 0.7678   | 0.7690    | 0.7678 | 0.7658 |
| 0.0003        | 9.0     | 2700 | 1.5230          | 0.7678   | 0.7690    | 0.7678 | 0.7658 |
| 0.0003        | 9.3333  | 2800 | 1.5361          | 0.7678   | 0.7699    | 0.7678 | 0.7662 |
| 0.0003        | 9.6667  | 2900 | 1.5466          | 0.7686   | 0.7711    | 0.7686 | 0.7673 |
| 0.0003        | 10.0    | 3000 | 1.5581          | 0.7686   | 0.7711    | 0.7686 | 0.7673 |
| 0.0003        | 10.3333 | 3100 | 1.5686          | 0.7686   | 0.7711    | 0.7686 | 0.7673 |
| 0.0003        | 10.6667 | 3200 | 1.5787          | 0.7686   | 0.7710    | 0.7686 | 0.7672 |
| 0.0002        | 11.0    | 3300 | 1.5877          | 0.7686   | 0.7717    | 0.7686 | 0.7675 |
| 0.0002        | 11.3333 | 3400 | 1.5963          | 0.7686   | 0.7717    | 0.7686 | 0.7675 |
| 0.0002        | 11.6667 | 3500 | 1.6044          | 0.7686   | 0.7722    | 0.7686 | 0.7677 |
| 0.0002        | 12.0    | 3600 | 1.6116          | 0.7686   | 0.7726    | 0.7686 | 0.7679 |
| 0.0002        | 12.3333 | 3700 | 1.6187          | 0.7686   | 0.7726    | 0.7686 | 0.7679 |
| 0.0002        | 12.6667 | 3800 | 1.6238          | 0.7686   | 0.7726    | 0.7686 | 0.7679 |
| 0.0002        | 13.0    | 3900 | 1.6295          | 0.7686   | 0.7722    | 0.7686 | 0.7679 |
| 0.0002        | 13.3333 | 4000 | 1.6344          | 0.7686   | 0.7726    | 0.7686 | 0.7679 |
| 0.0002        | 13.6667 | 4100 | 1.6379          | 0.7686   | 0.7726    | 0.7686 | 0.7679 |
| 0.0002        | 14.0    | 4200 | 1.6415          | 0.7686   | 0.7726    | 0.7686 | 0.7679 |
| 0.0002        | 14.3333 | 4300 | 1.6436          | 0.7678   | 0.7719    | 0.7678 | 0.7671 |
| 0.0002        | 14.6667 | 4400 | 1.6450          | 0.7678   | 0.7719    | 0.7678 | 0.7671 |
| 0.0002        | 15.0    | 4500 | 1.6454          | 0.7678   | 0.7719    | 0.7678 | 0.7671 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu126
- Datasets 3.1.0
- Tokenizers 0.21.0

sur-subtype_iva sur-subtype_iva2 sur-subtype_ivc sur-subtype_ivd sur-subtype_ia sur-subtype_va