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# vit-base-kidney-stone-3-Jonathan_El-Beze_-w256_1k_v1-_SUR

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: 0.5478
- Accuracy: 0.8875
- Precision: 0.8942
- Recall: 0.8875
- F1: 0.8875

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3968        | 0.3333  | 100  | 0.7205          | 0.7083   | 0.7287    | 0.7083 | 0.6701 |
| 0.0922        | 0.6667  | 200  | 0.7700          | 0.7433   | 0.7885    | 0.7433 | 0.7336 |
| 0.216         | 1.0     | 300  | 0.7658          | 0.7875   | 0.8259    | 0.7875 | 0.7863 |
| 0.0292        | 1.3333  | 400  | 0.7448          | 0.7983   | 0.8228    | 0.7983 | 0.7899 |
| 0.0139        | 1.6667  | 500  | 0.7137          | 0.8433   | 0.8527    | 0.8433 | 0.8416 |
| 0.0841        | 2.0     | 600  | 0.6836          | 0.8608   | 0.8715    | 0.8608 | 0.8603 |
| 0.0769        | 2.3333  | 700  | 0.5478          | 0.8875   | 0.8942    | 0.8875 | 0.8875 |
| 0.0046        | 2.6667  | 800  | 0.8076          | 0.8308   | 0.8564    | 0.8308 | 0.8314 |
| 0.019         | 3.0     | 900  | 0.8791          | 0.8408   | 0.8617    | 0.8408 | 0.8297 |
| 0.0451        | 3.3333  | 1000 | 0.7948          | 0.8567   | 0.8578    | 0.8567 | 0.8549 |
| 0.0022        | 3.6667  | 1100 | 0.7782          | 0.8592   | 0.8610    | 0.8592 | 0.8592 |
| 0.1346        | 4.0     | 1200 | 2.1560          | 0.62     | 0.7251    | 0.62   | 0.5922 |
| 0.0825        | 4.3333  | 1300 | 0.8192          | 0.8317   | 0.8600    | 0.8317 | 0.8297 |
| 0.0035        | 4.6667  | 1400 | 0.9398          | 0.8325   | 0.8360    | 0.8325 | 0.8265 |
| 0.0015        | 5.0     | 1500 | 0.8447          | 0.8367   | 0.8504    | 0.8367 | 0.8321 |
| 0.0013        | 5.3333  | 1600 | 1.1910          | 0.765    | 0.7940    | 0.765  | 0.7562 |
| 0.0009        | 5.6667  | 1700 | 0.9889          | 0.8317   | 0.8360    | 0.8317 | 0.8288 |
| 0.009         | 6.0     | 1800 | 0.8982          | 0.8517   | 0.8577    | 0.8517 | 0.8497 |
| 0.0007        | 6.3333  | 1900 | 0.8245          | 0.8683   | 0.8690    | 0.8683 | 0.8659 |
| 0.0006        | 6.6667  | 2000 | 0.8204          | 0.8708   | 0.8718    | 0.8708 | 0.8686 |
| 0.001         | 7.0     | 2100 | 1.3166          | 0.8      | 0.7992    | 0.8    | 0.7964 |
| 0.0006        | 7.3333  | 2200 | 1.0597          | 0.8383   | 0.8440    | 0.8383 | 0.8306 |
| 0.001         | 7.6667  | 2300 | 0.8703          | 0.8617   | 0.8592    | 0.8617 | 0.8586 |
| 0.0005        | 8.0     | 2400 | 1.0801          | 0.835    | 0.8377    | 0.835  | 0.8334 |
| 0.0007        | 8.3333  | 2500 | 1.3133          | 0.7975   | 0.8092    | 0.7975 | 0.7974 |
| 0.0004        | 8.6667  | 2600 | 1.0982          | 0.845    | 0.8581    | 0.845  | 0.8420 |
| 0.0004        | 9.0     | 2700 | 0.9103          | 0.8575   | 0.8742    | 0.8575 | 0.8558 |
| 0.0003        | 9.3333  | 2800 | 0.9156          | 0.8517   | 0.8642    | 0.8517 | 0.8506 |
| 0.0003        | 9.6667  | 2900 | 0.9209          | 0.8517   | 0.8645    | 0.8517 | 0.8506 |
| 0.0003        | 10.0    | 3000 | 0.9283          | 0.8517   | 0.8645    | 0.8517 | 0.8506 |
| 0.0003        | 10.3333 | 3100 | 0.9326          | 0.8533   | 0.8658    | 0.8533 | 0.8524 |
| 0.0003        | 10.6667 | 3200 | 0.9352          | 0.8542   | 0.8664    | 0.8542 | 0.8531 |
| 0.0003        | 11.0    | 3300 | 0.9393          | 0.8533   | 0.8655    | 0.8533 | 0.8522 |
| 0.0003        | 11.3333 | 3400 | 0.9418          | 0.8558   | 0.8672    | 0.8558 | 0.8545 |
| 0.0002        | 11.6667 | 3500 | 0.9446          | 0.855    | 0.8662    | 0.855  | 0.8537 |
| 0.0002        | 12.0    | 3600 | 0.9476          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 12.3333 | 3700 | 0.9502          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 12.6667 | 3800 | 0.9523          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 13.0    | 3900 | 0.9538          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 13.3333 | 4000 | 0.9558          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 13.6667 | 4100 | 0.9572          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 14.0    | 4200 | 0.9584          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 14.3333 | 4300 | 0.9588          | 0.8567   | 0.8681    | 0.8567 | 0.8553 |
| 0.0002        | 14.6667 | 4400 | 0.9595          | 0.8558   | 0.8669    | 0.8558 | 0.8545 |
| 0.0002        | 15.0    | 4500 | 0.9597          | 0.8558   | 0.8669    | 0.8558 | 0.8545 |


### Framework versions

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

sur-subtype_iiia sur-subtype_iia sur-subtype_ivc sur-subtype_ivd sur-subtype_ia sur-subtype_va