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# vit-base-kidney-stone-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: 0.3066
- Accuracy: 0.9417
- Precision: 0.9483
- Recall: 0.9417
- F1: 0.9388

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.056         | 0.6667  | 100  | 0.4899          | 0.85     | 0.8963    | 0.85   | 0.8476 |
| 0.0229        | 1.3333  | 200  | 0.5003          | 0.8792   | 0.9087    | 0.8792 | 0.8645 |
| 0.0082        | 2.0     | 300  | 0.3076          | 0.8883   | 0.9190    | 0.8883 | 0.8891 |
| 0.0049        | 2.6667  | 400  | 0.4297          | 0.9067   | 0.9307    | 0.9067 | 0.9055 |
| 0.0355        | 3.3333  | 500  | 0.7084          | 0.8325   | 0.9102    | 0.8325 | 0.8265 |
| 0.0752        | 4.0     | 600  | 0.5323          | 0.875    | 0.8919    | 0.875  | 0.8602 |
| 0.0025        | 4.6667  | 700  | 0.4350          | 0.8983   | 0.9142    | 0.8983 | 0.8952 |
| 0.0018        | 5.3333  | 800  | 0.3244          | 0.935    | 0.9428    | 0.935  | 0.9310 |
| 0.0014        | 6.0     | 900  | 0.3183          | 0.9367   | 0.9443    | 0.9367 | 0.9328 |
| 0.0012        | 6.6667  | 1000 | 0.3114          | 0.9367   | 0.9441    | 0.9367 | 0.9330 |
| 0.0011        | 7.3333  | 1100 | 0.3090          | 0.9367   | 0.9442    | 0.9367 | 0.9330 |
| 0.0009        | 8.0     | 1200 | 0.3078          | 0.9392   | 0.9463    | 0.9392 | 0.9359 |
| 0.0008        | 8.6667  | 1300 | 0.3077          | 0.94     | 0.9470    | 0.94   | 0.9369 |
| 0.0008        | 9.3333  | 1400 | 0.3068          | 0.9408   | 0.9476    | 0.9408 | 0.9378 |
| 0.0007        | 10.0    | 1500 | 0.3068          | 0.9417   | 0.9483    | 0.9417 | 0.9388 |
| 0.0007        | 10.6667 | 1600 | 0.3066          | 0.9417   | 0.9483    | 0.9417 | 0.9388 |
| 0.0006        | 11.3333 | 1700 | 0.3078          | 0.9425   | 0.9490    | 0.9425 | 0.9398 |
| 0.0006        | 12.0    | 1800 | 0.3080          | 0.9425   | 0.9490    | 0.9425 | 0.9398 |
| 0.0006        | 12.6667 | 1900 | 0.3086          | 0.9433   | 0.9499    | 0.9433 | 0.9406 |
| 0.0005        | 13.3333 | 2000 | 0.3091          | 0.9433   | 0.9499    | 0.9433 | 0.9406 |
| 0.0005        | 14.0    | 2100 | 0.3093          | 0.9433   | 0.9499    | 0.9433 | 0.9406 |
| 0.0005        | 14.6667 | 2200 | 0.3095          | 0.9433   | 0.9499    | 0.9433 | 0.9406 |


### Framework versions

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

sec-subtype_iiia sec-subtype_iia sec-subtype_ivc sec-subtype_ivd sec-subtype_ia sec-subtype_va