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# vit-base-kidney-stone-2-Jonathan_El-Beze_-w256_1k_v1-_SEC

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: 0.1129
- Accuracy: 0.9708
- Precision: 0.9708
- Recall: 0.9708
- F1: 0.9708

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2926        | 0.3333  | 100  | 0.6214          | 0.8408   | 0.8814    | 0.8408 | 0.8038 |
| 0.0637        | 0.6667  | 200  | 0.6714          | 0.8083   | 0.8903    | 0.8083 | 0.8003 |
| 0.058         | 1.0     | 300  | 1.0799          | 0.745    | 0.8358    | 0.745  | 0.7350 |
| 0.156         | 1.3333  | 400  | 1.1535          | 0.7142   | 0.8241    | 0.7142 | 0.6937 |
| 0.0075        | 1.6667  | 500  | 1.6682          | 0.6625   | 0.7947    | 0.6625 | 0.6207 |
| 0.0076        | 2.0     | 600  | 0.5363          | 0.8517   | 0.9048    | 0.8517 | 0.8568 |
| 0.0436        | 2.3333  | 700  | 0.1960          | 0.9558   | 0.9615    | 0.9558 | 0.9564 |
| 0.0019        | 2.6667  | 800  | 0.1241          | 0.975    | 0.9763    | 0.975  | 0.9746 |
| 0.0015        | 3.0     | 900  | 0.1129          | 0.9708   | 0.9708    | 0.9708 | 0.9708 |
| 0.0012        | 3.3333  | 1000 | 0.1154          | 0.9708   | 0.9708    | 0.9708 | 0.9708 |
| 0.001         | 3.6667  | 1100 | 0.1176          | 0.9717   | 0.9717    | 0.9717 | 0.9716 |
| 0.0009        | 4.0     | 1200 | 0.1204          | 0.9717   | 0.9717    | 0.9717 | 0.9717 |
| 0.0007        | 4.3333  | 1300 | 0.1223          | 0.9725   | 0.9725    | 0.9725 | 0.9725 |
| 0.0007        | 4.6667  | 1400 | 0.1246          | 0.9742   | 0.9742    | 0.9742 | 0.9742 |
| 0.0006        | 5.0     | 1500 | 0.1260          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0005        | 5.3333  | 1600 | 0.1281          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0005        | 5.6667  | 1700 | 0.1289          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0004        | 6.0     | 1800 | 0.1306          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0004        | 6.3333  | 1900 | 0.1321          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0004        | 6.6667  | 2000 | 0.1330          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0003        | 7.0     | 2100 | 0.1345          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0003        | 7.3333  | 2200 | 0.1357          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0003        | 7.6667  | 2300 | 0.1371          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0003        | 8.0     | 2400 | 0.1380          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0003        | 8.3333  | 2500 | 0.1392          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 8.6667  | 2600 | 0.1400          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 9.0     | 2700 | 0.1408          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 9.3333  | 2800 | 0.1417          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 9.6667  | 2900 | 0.1426          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 10.0    | 3000 | 0.1432          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 10.3333 | 3100 | 0.1441          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 10.6667 | 3200 | 0.1448          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 11.0    | 3300 | 0.1454          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 11.3333 | 3400 | 0.1460          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0002        | 11.6667 | 3500 | 0.1466          | 0.975    | 0.9751    | 0.975  | 0.9750 |
| 0.0001        | 12.0    | 3600 | 0.1471          | 0.9758   | 0.9760    | 0.9758 | 0.9759 |
| 0.0001        | 12.3333 | 3700 | 0.1476          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 12.6667 | 3800 | 0.1480          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 13.0    | 3900 | 0.1484          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 13.3333 | 4000 | 0.1487          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 13.6667 | 4100 | 0.1490          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 14.0    | 4200 | 0.1493          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 14.3333 | 4300 | 0.1494          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 14.6667 | 4400 | 0.1495          | 0.975    | 0.9752    | 0.975  | 0.9751 |
| 0.0001        | 15.0    | 4500 | 0.1496          | 0.975    | 0.9752    | 0.975  | 0.9751 |


### Framework versions

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

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