
<!-- 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-_SEC-finetune

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3310
- Accuracy: 0.9083
- Precision: 0.9122
- Recall: 0.9083
- F1: 0.9062

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.058         | 0.6667  | 100  | 0.3310          | 0.9083   | 0.9122    | 0.9083 | 0.9062 |
| 0.0028        | 1.3333  | 200  | 1.0903          | 0.7817   | 0.8859    | 0.7817 | 0.7660 |
| 0.016         | 2.0     | 300  | 0.8386          | 0.8167   | 0.8599    | 0.8167 | 0.8163 |
| 0.0032        | 2.6667  | 400  | 0.7872          | 0.8592   | 0.8953    | 0.8592 | 0.8567 |
| 0.0029        | 3.3333  | 500  | 1.1179          | 0.8058   | 0.8379    | 0.8058 | 0.8004 |
| 0.001         | 4.0     | 600  | 0.7550          | 0.8617   | 0.8971    | 0.8617 | 0.8628 |
| 0.0006        | 4.6667  | 700  | 0.6433          | 0.8833   | 0.9051    | 0.8833 | 0.8850 |
| 0.0004        | 5.3333  | 800  | 0.6051          | 0.8883   | 0.9094    | 0.8883 | 0.8903 |
| 0.0004        | 6.0     | 900  | 0.6016          | 0.8925   | 0.9128    | 0.8925 | 0.8946 |
| 0.0003        | 6.6667  | 1000 | 0.6000          | 0.8933   | 0.9138    | 0.8933 | 0.8956 |
| 0.0003        | 7.3333  | 1100 | 0.6001          | 0.8925   | 0.9130    | 0.8925 | 0.8947 |
| 0.0003        | 8.0     | 1200 | 0.6025          | 0.8933   | 0.9134    | 0.8933 | 0.8955 |
| 0.0002        | 8.6667  | 1300 | 0.6047          | 0.8958   | 0.9151    | 0.8958 | 0.8980 |
| 0.0002        | 9.3333  | 1400 | 0.6045          | 0.8958   | 0.9151    | 0.8958 | 0.8980 |
| 0.0002        | 10.0    | 1500 | 0.6056          | 0.8958   | 0.9147    | 0.8958 | 0.8979 |
| 0.0002        | 10.6667 | 1600 | 0.6063          | 0.8958   | 0.9147    | 0.8958 | 0.8979 |
| 0.0002        | 11.3333 | 1700 | 0.6082          | 0.8967   | 0.9152    | 0.8967 | 0.8987 |
| 0.0002        | 12.0    | 1800 | 0.6092          | 0.8967   | 0.9152    | 0.8967 | 0.8987 |
| 0.0002        | 12.6667 | 1900 | 0.6091          | 0.8967   | 0.9152    | 0.8967 | 0.8987 |
| 0.0002        | 13.3333 | 2000 | 0.6104          | 0.8967   | 0.9152    | 0.8967 | 0.8987 |
| 0.0002        | 14.0    | 2100 | 0.6111          | 0.8967   | 0.9152    | 0.8967 | 0.8987 |
| 0.0002        | 14.6667 | 2200 | 0.6114          | 0.8967   | 0.9152    | 0.8967 | 0.8987 |


### Framework versions

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

sec-subtype_iiia sec-subtype_iia sec-subtype_ivc sec-subtype_ivd sec-subtype_ia sec-subtype_va