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# vit-base-kidney-stone-3-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: 0.1421
- Accuracy: 0.97
- Precision: 0.9711
- Recall: 0.97
- F1: 0.9700

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1782        | 0.3333  | 100  | 1.5537          | 0.59     | 0.6419    | 0.59   | 0.5106 |
| 0.0982        | 0.6667  | 200  | 1.5012          | 0.6658   | 0.6563    | 0.6658 | 0.6262 |
| 0.1236        | 1.0     | 300  | 0.3710          | 0.895    | 0.9085    | 0.895  | 0.8958 |
| 0.0078        | 1.3333  | 400  | 1.4374          | 0.6992   | 0.7299    | 0.6992 | 0.6613 |
| 0.0049        | 1.6667  | 500  | 0.4037          | 0.9058   | 0.9181    | 0.9058 | 0.9064 |
| 0.0047        | 2.0     | 600  | 1.7908          | 0.675    | 0.7138    | 0.675  | 0.6297 |
| 0.0032        | 2.3333  | 700  | 1.1430          | 0.8233   | 0.8831    | 0.8233 | 0.7906 |
| 0.0027        | 2.6667  | 800  | 1.1627          | 0.735    | 0.8254    | 0.735  | 0.7005 |
| 0.0018        | 3.0     | 900  | 0.8254          | 0.8292   | 0.8864    | 0.8292 | 0.8050 |
| 0.0016        | 3.3333  | 1000 | 1.2364          | 0.7625   | 0.8527    | 0.7625 | 0.7462 |
| 0.0027        | 3.6667  | 1100 | 0.2785          | 0.9267   | 0.9359    | 0.9267 | 0.9271 |
| 0.001         | 4.0     | 1200 | 0.6703          | 0.8775   | 0.9013    | 0.8775 | 0.8784 |
| 0.001         | 4.3333  | 1300 | 0.8848          | 0.8458   | 0.8925    | 0.8458 | 0.8397 |
| 0.0009        | 4.6667  | 1400 | 0.3603          | 0.9183   | 0.9325    | 0.9183 | 0.9199 |
| 0.0007        | 5.0     | 1500 | 0.4274          | 0.9183   | 0.9325    | 0.9183 | 0.9144 |
| 0.0006        | 5.3333  | 1600 | 0.3995          | 0.9233   | 0.9368    | 0.9233 | 0.9200 |
| 0.0005        | 5.6667  | 1700 | 0.3866          | 0.9258   | 0.9384    | 0.9258 | 0.9229 |
| 0.0012        | 6.0     | 1800 | 0.5027          | 0.9083   | 0.9401    | 0.9083 | 0.9110 |
| 0.0004        | 6.3333  | 1900 | 0.1421          | 0.97     | 0.9711    | 0.97   | 0.9700 |
| 0.0004        | 6.6667  | 2000 | 0.1475          | 0.97     | 0.9713    | 0.97   | 0.9700 |
| 0.0004        | 7.0     | 2100 | 0.1484          | 0.9708   | 0.9720    | 0.9708 | 0.9709 |
| 0.0003        | 7.3333  | 2200 | 0.1502          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0003        | 7.6667  | 2300 | 0.1530          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0003        | 8.0     | 2400 | 0.1539          | 0.9708   | 0.9720    | 0.9708 | 0.9709 |
| 0.0003        | 8.3333  | 2500 | 0.1565          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0003        | 8.6667  | 2600 | 0.1574          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0002        | 9.0     | 2700 | 0.1592          | 0.9717   | 0.9727    | 0.9717 | 0.9717 |
| 0.0002        | 9.3333  | 2800 | 0.1610          | 0.9717   | 0.9727    | 0.9717 | 0.9717 |
| 0.0002        | 9.6667  | 2900 | 0.1626          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0002        | 10.0    | 3000 | 0.1636          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0002        | 10.3333 | 3100 | 0.1645          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0002        | 10.6667 | 3200 | 0.1657          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0002        | 11.0    | 3300 | 0.1669          | 0.9708   | 0.9719    | 0.9708 | 0.9708 |
| 0.0002        | 11.3333 | 3400 | 0.1682          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0002        | 11.6667 | 3500 | 0.1691          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0002        | 12.0    | 3600 | 0.1697          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0002        | 12.3333 | 3700 | 0.1704          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0002        | 12.6667 | 3800 | 0.1709          | 0.97     | 0.9712    | 0.97   | 0.9700 |
| 0.0001        | 13.0    | 3900 | 0.1715          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |
| 0.0001        | 13.3333 | 4000 | 0.1721          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |
| 0.0001        | 13.6667 | 4100 | 0.1727          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |
| 0.0001        | 14.0    | 4200 | 0.1730          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |
| 0.0001        | 14.3333 | 4300 | 0.1731          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |
| 0.0001        | 14.6667 | 4400 | 0.1733          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |
| 0.0001        | 15.0    | 4500 | 0.1734          | 0.9692   | 0.9704    | 0.9692 | 0.9692 |


### 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