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# vit-base-kidney-stone-Michel_Daudon_-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: 0.3802
- Accuracy: 0.8975
- Precision: 0.9004
- Recall: 0.8975
- F1: 0.8961

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1982        | 0.6667  | 100  | 0.5328          | 0.8342   | 0.8678    | 0.8342 | 0.8304 |
| 0.103         | 1.3333  | 200  | 0.5614          | 0.8342   | 0.8518    | 0.8342 | 0.8235 |
| 0.0646        | 2.0     | 300  | 0.3802          | 0.8975   | 0.9004    | 0.8975 | 0.8961 |
| 0.0206        | 2.6667  | 400  | 0.5236          | 0.8908   | 0.8932    | 0.8908 | 0.8910 |
| 0.0073        | 3.3333  | 500  | 0.4848          | 0.885    | 0.9037    | 0.885  | 0.8879 |
| 0.0237        | 4.0     | 600  | 0.6534          | 0.8617   | 0.8872    | 0.8617 | 0.8633 |
| 0.0414        | 4.6667  | 700  | 0.5937          | 0.8808   | 0.8914    | 0.8808 | 0.8782 |
| 0.0027        | 5.3333  | 800  | 0.5129          | 0.8933   | 0.8992    | 0.8933 | 0.8953 |
| 0.0023        | 6.0     | 900  | 0.6645          | 0.8867   | 0.9012    | 0.8867 | 0.8876 |
| 0.0017        | 6.6667  | 1000 | 0.4428          | 0.9158   | 0.9162    | 0.9158 | 0.9158 |
| 0.0014        | 7.3333  | 1100 | 0.4490          | 0.9183   | 0.9188    | 0.9183 | 0.9183 |
| 0.0012        | 8.0     | 1200 | 0.4573          | 0.9183   | 0.9188    | 0.9183 | 0.9183 |
| 0.0011        | 8.6667  | 1300 | 0.4643          | 0.9183   | 0.9186    | 0.9183 | 0.9182 |
| 0.001         | 9.3333  | 1400 | 0.4724          | 0.9175   | 0.9178    | 0.9175 | 0.9174 |
| 0.0009        | 10.0    | 1500 | 0.4783          | 0.9192   | 0.9196    | 0.9192 | 0.9191 |
| 0.0008        | 10.6667 | 1600 | 0.4834          | 0.92     | 0.9205    | 0.92   | 0.9200 |
| 0.0008        | 11.3333 | 1700 | 0.4880          | 0.9183   | 0.9188    | 0.9183 | 0.9183 |
| 0.0007        | 12.0    | 1800 | 0.4913          | 0.9192   | 0.9196    | 0.9192 | 0.9191 |
| 0.0007        | 12.6667 | 1900 | 0.4946          | 0.9192   | 0.9196    | 0.9192 | 0.9191 |
| 0.0007        | 13.3333 | 2000 | 0.4967          | 0.9192   | 0.9196    | 0.9192 | 0.9191 |
| 0.0006        | 14.0    | 2100 | 0.4982          | 0.9192   | 0.9196    | 0.9192 | 0.9191 |
| 0.0006        | 14.6667 | 2200 | 0.4990          | 0.9192   | 0.9196    | 0.9192 | 0.9191 |


### Framework versions

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

sec-subtype_iva sec-subtype_iva2 sec-subtype_ivc sec-subtype_ivd sec-subtype_ia sec-subtype_va