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# vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SEC-pretrain

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: 0.3455
- Accuracy: 0.9108
- Precision: 0.9190
- Recall: 0.9108
- F1: 0.9103

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1494        | 0.6667  | 100  | 0.6088          | 0.8442   | 0.8766    | 0.8442 | 0.8390 |
| 0.0665        | 1.3333  | 200  | 0.5533          | 0.8492   | 0.8810    | 0.8492 | 0.8542 |
| 0.0215        | 2.0     | 300  | 0.3721          | 0.9017   | 0.9082    | 0.9017 | 0.8985 |
| 0.0101        | 2.6667  | 400  | 0.5347          | 0.8942   | 0.9061    | 0.8942 | 0.8920 |
| 0.043         | 3.3333  | 500  | 0.7850          | 0.8425   | 0.8592    | 0.8425 | 0.8427 |
| 0.0641        | 4.0     | 600  | 0.7735          | 0.8583   | 0.8770    | 0.8583 | 0.8574 |
| 0.0036        | 4.6667  | 700  | 0.7351          | 0.8367   | 0.8623    | 0.8367 | 0.8250 |
| 0.0039        | 5.3333  | 800  | 0.3455          | 0.9108   | 0.9190    | 0.9108 | 0.9103 |
| 0.0021        | 6.0     | 900  | 0.5940          | 0.8758   | 0.8985    | 0.8758 | 0.8730 |
| 0.054         | 6.6667  | 1000 | 0.7463          | 0.8733   | 0.9068    | 0.8733 | 0.8714 |
| 0.0015        | 7.3333  | 1100 | 0.8915          | 0.8392   | 0.8722    | 0.8392 | 0.8243 |
| 0.0013        | 8.0     | 1200 | 0.5725          | 0.8917   | 0.8943    | 0.8917 | 0.8909 |
| 0.0011        | 8.6667  | 1300 | 0.5772          | 0.8933   | 0.8960    | 0.8933 | 0.8926 |
| 0.001         | 9.3333  | 1400 | 0.5820          | 0.8933   | 0.8956    | 0.8933 | 0.8926 |
| 0.0009        | 10.0    | 1500 | 0.5859          | 0.8933   | 0.8954    | 0.8933 | 0.8925 |
| 0.0008        | 10.6667 | 1600 | 0.5901          | 0.8933   | 0.8955    | 0.8933 | 0.8926 |
| 0.0008        | 11.3333 | 1700 | 0.5938          | 0.8933   | 0.8955    | 0.8933 | 0.8926 |
| 0.0007        | 12.0    | 1800 | 0.5971          | 0.8933   | 0.8953    | 0.8933 | 0.8925 |
| 0.0007        | 12.6667 | 1900 | 0.5998          | 0.8933   | 0.8952    | 0.8933 | 0.8926 |
| 0.0007        | 13.3333 | 2000 | 0.6016          | 0.8933   | 0.8952    | 0.8933 | 0.8926 |
| 0.0006        | 14.0    | 2100 | 0.6032          | 0.8933   | 0.8952    | 0.8933 | 0.8926 |
| 0.0006        | 14.6667 | 2200 | 0.6039          | 0.8933   | 0.8952    | 0.8933 | 0.8926 |


### 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