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# vit-base-kidney-stone-5-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: 0.2317
- Accuracy: 0.9583
- Precision: 0.9611
- Recall: 0.9583
- F1: 0.9575

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1048        | 0.3333  | 100  | 0.2766          | 0.9125   | 0.9266    | 0.9125 | 0.9148 |
| 0.1694        | 0.6667  | 200  | 0.5766          | 0.855    | 0.8878    | 0.855  | 0.8515 |
| 0.1116        | 1.0     | 300  | 0.8084          | 0.8233   | 0.8730    | 0.8233 | 0.8067 |
| 0.0071        | 1.3333  | 400  | 0.6568          | 0.8783   | 0.9098    | 0.8783 | 0.8717 |
| 0.0606        | 1.6667  | 500  | 0.6522          | 0.8767   | 0.9201    | 0.8767 | 0.8796 |
| 0.0069        | 2.0     | 600  | 1.3007          | 0.7383   | 0.7651    | 0.7383 | 0.7228 |
| 0.003         | 2.3333  | 700  | 0.3122          | 0.925    | 0.9287    | 0.925  | 0.9253 |
| 0.002         | 2.6667  | 800  | 0.5233          | 0.89     | 0.9141    | 0.89   | 0.8863 |
| 0.0023        | 3.0     | 900  | 0.7763          | 0.8567   | 0.8853    | 0.8567 | 0.8499 |
| 0.1048        | 3.3333  | 1000 | 0.5440          | 0.8983   | 0.9024    | 0.8983 | 0.8971 |
| 0.0023        | 3.6667  | 1100 | 0.3234          | 0.9367   | 0.9471    | 0.9367 | 0.9366 |
| 0.0943        | 4.0     | 1200 | 0.9164          | 0.84     | 0.9062    | 0.84   | 0.8402 |
| 0.0858        | 4.3333  | 1300 | 0.2317          | 0.9583   | 0.9611    | 0.9583 | 0.9575 |
| 0.0011        | 4.6667  | 1400 | 1.0192          | 0.82     | 0.8376    | 0.82   | 0.8045 |
| 0.0009        | 5.0     | 1500 | 0.5853          | 0.8725   | 0.9008    | 0.8725 | 0.8718 |
| 0.0007        | 5.3333  | 1600 | 0.5612          | 0.8842   | 0.9086    | 0.8842 | 0.8841 |
| 0.0006        | 5.6667  | 1700 | 0.5591          | 0.8842   | 0.9085    | 0.8842 | 0.8842 |
| 0.0006        | 6.0     | 1800 | 0.5744          | 0.8833   | 0.9085    | 0.8833 | 0.8832 |
| 0.0005        | 6.3333  | 1900 | 0.5831          | 0.8817   | 0.9065    | 0.8817 | 0.8816 |
| 0.0005        | 6.6667  | 2000 | 0.5819          | 0.8842   | 0.9075    | 0.8842 | 0.8842 |
| 0.0004        | 7.0     | 2100 | 0.5861          | 0.8842   | 0.9076    | 0.8842 | 0.8843 |
| 0.0004        | 7.3333  | 2200 | 0.5866          | 0.8867   | 0.9092    | 0.8867 | 0.8869 |
| 0.0004        | 7.6667  | 2300 | 0.5911          | 0.8867   | 0.9092    | 0.8867 | 0.8869 |
| 0.0004        | 8.0     | 2400 | 0.5931          | 0.8867   | 0.9092    | 0.8867 | 0.8869 |
| 0.0003        | 8.3333  | 2500 | 0.5992          | 0.8867   | 0.9092    | 0.8867 | 0.8869 |
| 0.0003        | 8.6667  | 2600 | 0.5975          | 0.8892   | 0.9108    | 0.8892 | 0.8895 |
| 0.0003        | 9.0     | 2700 | 0.5978          | 0.89     | 0.9112    | 0.89   | 0.8904 |
| 0.0003        | 9.3333  | 2800 | 0.6015          | 0.89     | 0.9115    | 0.89   | 0.8905 |
| 0.0003        | 9.6667  | 2900 | 0.6045          | 0.89     | 0.9115    | 0.89   | 0.8905 |
| 0.0002        | 10.0    | 3000 | 0.6030          | 0.89     | 0.9115    | 0.89   | 0.8905 |
| 0.0002        | 10.3333 | 3100 | 0.6025          | 0.8917   | 0.9124    | 0.8917 | 0.8922 |
| 0.0002        | 10.6667 | 3200 | 0.6038          | 0.8917   | 0.9124    | 0.8917 | 0.8922 |
| 0.0002        | 11.0    | 3300 | 0.6075          | 0.8908   | 0.9112    | 0.8908 | 0.8913 |
| 0.0002        | 11.3333 | 3400 | 0.6090          | 0.8917   | 0.9116    | 0.8917 | 0.8922 |
| 0.0002        | 11.6667 | 3500 | 0.6109          | 0.8917   | 0.9116    | 0.8917 | 0.8923 |
| 0.0002        | 12.0    | 3600 | 0.6111          | 0.8917   | 0.9116    | 0.8917 | 0.8923 |
| 0.0002        | 12.3333 | 3700 | 0.6121          | 0.8917   | 0.9116    | 0.8917 | 0.8923 |
| 0.0002        | 12.6667 | 3800 | 0.6126          | 0.8917   | 0.9116    | 0.8917 | 0.8923 |
| 0.0002        | 13.0    | 3900 | 0.6135          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |
| 0.0002        | 13.3333 | 4000 | 0.6142          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |
| 0.0002        | 13.6667 | 4100 | 0.6154          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |
| 0.0002        | 14.0    | 4200 | 0.6156          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |
| 0.0002        | 14.3333 | 4300 | 0.6159          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |
| 0.0002        | 14.6667 | 4400 | 0.6162          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |
| 0.0002        | 15.0    | 4500 | 0.6163          | 0.8917   | 0.9119    | 0.8917 | 0.8923 |


### 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