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# vit-base-kidney-stone-2-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: 0.5352
- Accuracy: 0.8542
- Precision: 0.8593
- Recall: 0.8542
- F1: 0.8516

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3658        | 0.3333  | 100  | 0.7426          | 0.7017   | 0.6844    | 0.7017 | 0.6699 |
| 0.3256        | 0.6667  | 200  | 0.7536          | 0.7608   | 0.8199    | 0.7608 | 0.7638 |
| 0.0727        | 1.0     | 300  | 0.5352          | 0.8542   | 0.8593    | 0.8542 | 0.8516 |
| 0.0553        | 1.3333  | 400  | 0.5903          | 0.8575   | 0.8636    | 0.8575 | 0.8547 |
| 0.116         | 1.6667  | 500  | 0.8102          | 0.8075   | 0.8478    | 0.8075 | 0.8036 |
| 0.1034        | 2.0     | 600  | 0.9591          | 0.79     | 0.8360    | 0.79   | 0.7929 |
| 0.0921        | 2.3333  | 700  | 1.0530          | 0.7917   | 0.8153    | 0.7917 | 0.7890 |
| 0.0845        | 2.6667  | 800  | 0.8513          | 0.81     | 0.8188    | 0.81   | 0.8074 |
| 0.0027        | 3.0     | 900  | 1.1166          | 0.7883   | 0.8020    | 0.7883 | 0.7852 |
| 0.0046        | 3.3333  | 1000 | 1.0594          | 0.8075   | 0.8496    | 0.8075 | 0.7994 |
| 0.1194        | 3.6667  | 1100 | 1.1294          | 0.7992   | 0.8259    | 0.7992 | 0.7985 |
| 0.0865        | 4.0     | 1200 | 1.0208          | 0.7908   | 0.8241    | 0.7908 | 0.7874 |
| 0.0015        | 4.3333  | 1300 | 0.6127          | 0.8783   | 0.8875    | 0.8783 | 0.8778 |
| 0.0086        | 4.6667  | 1400 | 0.9398          | 0.8383   | 0.8601    | 0.8383 | 0.8352 |
| 0.0016        | 5.0     | 1500 | 0.9671          | 0.835    | 0.8414    | 0.835  | 0.8361 |
| 0.0031        | 5.3333  | 1600 | 0.7669          | 0.8425   | 0.8480    | 0.8425 | 0.8379 |
| 0.0015        | 5.6667  | 1700 | 1.6634          | 0.7092   | 0.7774    | 0.7092 | 0.6878 |
| 0.0011        | 6.0     | 1800 | 0.9625          | 0.8517   | 0.8701    | 0.8517 | 0.8464 |
| 0.0015        | 6.3333  | 1900 | 0.9576          | 0.8392   | 0.8558    | 0.8392 | 0.8367 |
| 0.0009        | 6.6667  | 2000 | 0.9355          | 0.84     | 0.8615    | 0.84   | 0.8390 |
| 0.0629        | 7.0     | 2100 | 0.8580          | 0.8508   | 0.8527    | 0.8508 | 0.8490 |
| 0.0446        | 7.3333  | 2200 | 0.7906          | 0.8783   | 0.8798    | 0.8783 | 0.8759 |
| 0.0007        | 7.6667  | 2300 | 0.9514          | 0.8283   | 0.8405    | 0.8283 | 0.8258 |
| 0.0006        | 8.0     | 2400 | 1.0413          | 0.8317   | 0.8407    | 0.8317 | 0.8298 |
| 0.0006        | 8.3333  | 2500 | 1.0492          | 0.8342   | 0.8427    | 0.8342 | 0.8324 |
| 0.0478        | 8.6667  | 2600 | 0.7952          | 0.8667   | 0.8701    | 0.8667 | 0.8664 |
| 0.0006        | 9.0     | 2700 | 0.8355          | 0.8708   | 0.8827    | 0.8708 | 0.8689 |
| 0.0004        | 9.3333  | 2800 | 1.0021          | 0.8508   | 0.8675    | 0.8508 | 0.8501 |
| 0.0004        | 9.6667  | 2900 | 1.0899          | 0.84     | 0.8573    | 0.84   | 0.8378 |
| 0.0004        | 10.0    | 3000 | 0.9897          | 0.8533   | 0.8614    | 0.8533 | 0.8505 |
| 0.0007        | 10.3333 | 3100 | 1.4134          | 0.8008   | 0.8407    | 0.8008 | 0.7956 |
| 0.0004        | 10.6667 | 3200 | 1.2195          | 0.8225   | 0.8459    | 0.8225 | 0.8212 |
| 0.0003        | 11.0    | 3300 | 1.2032          | 0.8242   | 0.8459    | 0.8242 | 0.8230 |
| 0.0003        | 11.3333 | 3400 | 1.1995          | 0.8267   | 0.8479    | 0.8267 | 0.8255 |
| 0.0003        | 11.6667 | 3500 | 1.1979          | 0.825    | 0.8453    | 0.825  | 0.8239 |
| 0.0003        | 12.0    | 3600 | 1.1959          | 0.8258   | 0.8461    | 0.8258 | 0.8248 |
| 0.0003        | 12.3333 | 3700 | 1.1960          | 0.8275   | 0.8473    | 0.8275 | 0.8264 |
| 0.0003        | 12.6667 | 3800 | 1.1960          | 0.8275   | 0.8473    | 0.8275 | 0.8264 |
| 0.0003        | 13.0    | 3900 | 1.1972          | 0.8275   | 0.8473    | 0.8275 | 0.8264 |
| 0.0003        | 13.3333 | 4000 | 1.1986          | 0.8283   | 0.8479    | 0.8283 | 0.8273 |
| 0.0003        | 13.6667 | 4100 | 1.1993          | 0.8292   | 0.8484    | 0.8292 | 0.8280 |
| 0.0003        | 14.0    | 4200 | 1.1999          | 0.8292   | 0.8484    | 0.8292 | 0.8280 |
| 0.0002        | 14.3333 | 4300 | 1.2012          | 0.8292   | 0.8484    | 0.8292 | 0.8280 |
| 0.0002        | 14.6667 | 4400 | 1.2014          | 0.8292   | 0.8484    | 0.8292 | 0.8280 |
| 0.0002        | 15.0    | 4500 | 1.2016          | 0.8292   | 0.8484    | 0.8292 | 0.8280 |


### 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