
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SUR-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: 0.8241
- Accuracy: 0.7318
- Precision: 0.7397
- Recall: 0.7318
- F1: 0.7202

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.189         | 0.6667  | 100  | 0.8241          | 0.7318   | 0.7397    | 0.7318 | 0.7202 |
| 0.0343        | 1.3333  | 200  | 1.1125          | 0.7269   | 0.8038    | 0.7269 | 0.7294 |
| 0.0214        | 2.0     | 300  | 0.9077          | 0.7645   | 0.7745    | 0.7645 | 0.7681 |
| 0.0684        | 2.6667  | 400  | 1.3120          | 0.7498   | 0.7677    | 0.7498 | 0.7542 |
| 0.0543        | 3.3333  | 500  | 1.4106          | 0.7212   | 0.7429    | 0.7212 | 0.7291 |
| 0.0367        | 4.0     | 600  | 0.9240          | 0.7850   | 0.8052    | 0.7850 | 0.7868 |
| 0.0028        | 4.6667  | 700  | 0.9933          | 0.8013   | 0.8130    | 0.8013 | 0.8037 |
| 0.0023        | 5.3333  | 800  | 1.1196          | 0.7964   | 0.8140    | 0.7964 | 0.8023 |
| 0.0279        | 6.0     | 900  | 1.1338          | 0.7825   | 0.8063    | 0.7825 | 0.7742 |
| 0.0351        | 6.6667  | 1000 | 1.2453          | 0.8046   | 0.8289    | 0.8046 | 0.7990 |
| 0.0015        | 7.3333  | 1100 | 1.4902          | 0.7833   | 0.8110    | 0.7833 | 0.7821 |
| 0.0012        | 8.0     | 1200 | 1.5158          | 0.7817   | 0.8050    | 0.7817 | 0.7801 |
| 0.001         | 8.6667  | 1300 | 1.5461          | 0.7776   | 0.7989    | 0.7776 | 0.7765 |
| 0.0009        | 9.3333  | 1400 | 1.5691          | 0.7735   | 0.7930    | 0.7735 | 0.7728 |
| 0.0009        | 10.0    | 1500 | 1.5899          | 0.7743   | 0.7935    | 0.7743 | 0.7735 |
| 0.0008        | 10.6667 | 1600 | 1.6074          | 0.7735   | 0.7927    | 0.7735 | 0.7731 |
| 0.0007        | 11.3333 | 1700 | 1.6235          | 0.7735   | 0.7927    | 0.7735 | 0.7731 |
| 0.0007        | 12.0    | 1800 | 1.6367          | 0.7727   | 0.7914    | 0.7727 | 0.7723 |
| 0.0007        | 12.6667 | 1900 | 1.6468          | 0.7735   | 0.7919    | 0.7735 | 0.7730 |
| 0.0006        | 13.3333 | 2000 | 1.6551          | 0.7735   | 0.7909    | 0.7735 | 0.7729 |
| 0.0006        | 14.0    | 2100 | 1.6609          | 0.7727   | 0.7896    | 0.7727 | 0.7721 |
| 0.0006        | 14.6667 | 2200 | 1.6637          | 0.7727   | 0.7896    | 0.7727 | 0.7721 |


### Framework versions

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

sur-subtype_iva sur-subtype_iva2 sur-subtype_ivc sur-subtype_ivd sur-subtype_ia sur-subtype_va