
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5530
- Accuracy: 0.8733
- Precision: 0.8738
- Recall: 0.8733
- F1: 0.8688

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1177        | 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 |


### Framework versions

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

sur-subtype_iiia sur-subtype_iia sur-subtype_ivc sur-subtype_ivd sur-subtype_ia sur-subtype_va