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# vit-base-kidney-stone-5-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: 0.3821
- Accuracy: 0.9283
- Precision: 0.9298
- Recall: 0.9283
- F1: 0.9282

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3259        | 0.3333  | 100  | 0.6052          | 0.8142   | 0.8678    | 0.8142 | 0.8113 |
| 0.1852        | 0.6667  | 200  | 0.4605          | 0.8525   | 0.8799    | 0.8525 | 0.8505 |
| 0.1342        | 1.0     | 300  | 0.5787          | 0.8583   | 0.8939    | 0.8583 | 0.8592 |
| 0.0984        | 1.3333  | 400  | 0.4582          | 0.8875   | 0.8938    | 0.8875 | 0.8863 |
| 0.0555        | 1.6667  | 500  | 0.3914          | 0.8825   | 0.8955    | 0.8825 | 0.8844 |
| 0.2228        | 2.0     | 600  | 0.5982          | 0.865    | 0.8807    | 0.865  | 0.8668 |
| 0.016         | 2.3333  | 700  | 0.5747          | 0.8708   | 0.8929    | 0.8708 | 0.8729 |
| 0.2215        | 2.6667  | 800  | 0.6513          | 0.8575   | 0.8777    | 0.8575 | 0.8564 |
| 0.0118        | 3.0     | 900  | 0.8234          | 0.8492   | 0.8687    | 0.8492 | 0.8498 |
| 0.0028        | 3.3333  | 1000 | 0.6503          | 0.88     | 0.8949    | 0.88   | 0.8804 |
| 0.0035        | 3.6667  | 1100 | 0.4011          | 0.9133   | 0.9207    | 0.9133 | 0.9145 |
| 0.0742        | 4.0     | 1200 | 0.5671          | 0.8833   | 0.9069    | 0.8833 | 0.8833 |
| 0.0074        | 4.3333  | 1300 | 0.6269          | 0.8742   | 0.8902    | 0.8742 | 0.8711 |
| 0.0043        | 4.6667  | 1400 | 0.6497          | 0.8792   | 0.8998    | 0.8792 | 0.8800 |
| 0.133         | 5.0     | 1500 | 0.7292          | 0.8733   | 0.9075    | 0.8733 | 0.8738 |
| 0.0012        | 5.3333  | 1600 | 0.7823          | 0.8633   | 0.8799    | 0.8633 | 0.8637 |
| 0.0009        | 5.6667  | 1700 | 0.4115          | 0.915    | 0.9186    | 0.915  | 0.9156 |
| 0.0011        | 6.0     | 1800 | 0.8521          | 0.85     | 0.8619    | 0.85   | 0.8493 |
| 0.001         | 6.3333  | 1900 | 0.4895          | 0.9108   | 0.9263    | 0.9108 | 0.9126 |
| 0.0219        | 6.6667  | 2000 | 0.3821          | 0.9283   | 0.9298    | 0.9283 | 0.9282 |
| 0.0008        | 7.0     | 2100 | 0.7710          | 0.8683   | 0.8868    | 0.8683 | 0.8666 |
| 0.0007        | 7.3333  | 2200 | 0.5704          | 0.9108   | 0.9179    | 0.9108 | 0.9073 |
| 0.0014        | 7.6667  | 2300 | 0.6604          | 0.8925   | 0.8981    | 0.8925 | 0.8902 |
| 0.0005        | 8.0     | 2400 | 0.5364          | 0.9075   | 0.9095    | 0.9075 | 0.9061 |
| 0.0005        | 8.3333  | 2500 | 0.5356          | 0.9075   | 0.9093    | 0.9075 | 0.9062 |
| 0.0004        | 8.6667  | 2600 | 0.5364          | 0.9067   | 0.9082    | 0.9067 | 0.9053 |
| 0.0004        | 9.0     | 2700 | 0.7982          | 0.8692   | 0.8722    | 0.8692 | 0.8636 |
| 0.0004        | 9.3333  | 2800 | 0.7586          | 0.875    | 0.8774    | 0.875  | 0.8706 |
| 0.0004        | 9.6667  | 2900 | 0.7252          | 0.8808   | 0.8837    | 0.8808 | 0.8774 |
| 0.0003        | 10.0    | 3000 | 0.6126          | 0.8992   | 0.9037    | 0.8992 | 0.8995 |
| 0.0003        | 10.3333 | 3100 | 0.6417          | 0.8917   | 0.8889    | 0.8917 | 0.8899 |
| 0.0003        | 10.6667 | 3200 | 0.6489          | 0.8925   | 0.8901    | 0.8925 | 0.8909 |
| 0.0003        | 11.0    | 3300 | 0.6508          | 0.8917   | 0.8892    | 0.8917 | 0.8900 |
| 0.0003        | 11.3333 | 3400 | 0.6529          | 0.8917   | 0.8892    | 0.8917 | 0.8900 |
| 0.0003        | 11.6667 | 3500 | 0.6544          | 0.8917   | 0.8892    | 0.8917 | 0.8900 |
| 0.0003        | 12.0    | 3600 | 0.6561          | 0.8917   | 0.8892    | 0.8917 | 0.8900 |
| 0.0003        | 12.3333 | 3700 | 0.6577          | 0.8925   | 0.8899    | 0.8925 | 0.8907 |
| 0.0002        | 12.6667 | 3800 | 0.6592          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 13.0    | 3900 | 0.6601          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 13.3333 | 4000 | 0.6613          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 13.6667 | 4100 | 0.6622          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 14.0    | 4200 | 0.6629          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 14.3333 | 4300 | 0.6635          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 14.6667 | 4400 | 0.6638          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |
| 0.0002        | 15.0    | 4500 | 0.6640          | 0.8933   | 0.8906    | 0.8933 | 0.8915 |


### Framework versions

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

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