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# vit-finetune-kidney-stone-Jonathan_El-Beze_-w256_1k_v1-_MIX-finetune

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3739
- Accuracy: 0.9025
- Precision: 0.9065
- Recall: 0.9025
- F1: 0.9011

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1672        | 0.3333  | 100  | 0.3739          | 0.9025   | 0.9065    | 0.9025 | 0.9011 |
| 0.1364        | 0.6667  | 200  | 0.7118          | 0.7879   | 0.8371    | 0.7879 | 0.7837 |
| 0.0603        | 1.0     | 300  | 0.6678          | 0.8275   | 0.8502    | 0.8275 | 0.8257 |
| 0.0532        | 1.3333  | 400  | 0.6051          | 0.8596   | 0.8785    | 0.8596 | 0.8578 |
| 0.0195        | 1.6667  | 500  | 0.6989          | 0.8263   | 0.8493    | 0.8263 | 0.8278 |
| 0.0284        | 2.0     | 600  | 0.7349          | 0.8342   | 0.8608    | 0.8342 | 0.8366 |
| 0.0145        | 2.3333  | 700  | 0.7102          | 0.8662   | 0.8741    | 0.8662 | 0.8636 |
| 0.0142        | 2.6667  | 800  | 0.7562          | 0.8583   | 0.8652    | 0.8583 | 0.8554 |
| 0.0327        | 3.0     | 900  | 0.6251          | 0.87     | 0.8830    | 0.87   | 0.8697 |
| 0.0014        | 3.3333  | 1000 | 0.6991          | 0.8571   | 0.8772    | 0.8571 | 0.8535 |
| 0.0015        | 3.6667  | 1100 | 0.4318          | 0.9075   | 0.9117    | 0.9075 | 0.9077 |
| 0.0022        | 4.0     | 1200 | 0.7833          | 0.8592   | 0.8752    | 0.8592 | 0.8583 |
| 0.0049        | 4.3333  | 1300 | 0.4950          | 0.9054   | 0.9088    | 0.9054 | 0.9049 |
| 0.0125        | 4.6667  | 1400 | 0.5476          | 0.8879   | 0.8898    | 0.8879 | 0.8873 |
| 0.0163        | 5.0     | 1500 | 0.4917          | 0.9096   | 0.9099    | 0.9096 | 0.9087 |
| 0.003         | 5.3333  | 1600 | 0.8279          | 0.8612   | 0.8665    | 0.8612 | 0.8586 |
| 0.0027        | 5.6667  | 1700 | 0.9960          | 0.8242   | 0.8615    | 0.8242 | 0.8141 |
| 0.0015        | 6.0     | 1800 | 0.7634          | 0.8621   | 0.8865    | 0.8621 | 0.8611 |
| 0.0006        | 6.3333  | 1900 | 0.5313          | 0.9      | 0.9068    | 0.9    | 0.8991 |
| 0.0005        | 6.6667  | 2000 | 0.4222          | 0.9225   | 0.9243    | 0.9225 | 0.9222 |
| 0.0322        | 7.0     | 2100 | 0.5260          | 0.9067   | 0.9115    | 0.9067 | 0.9063 |
| 0.0106        | 7.3333  | 2200 | 0.5679          | 0.8817   | 0.8903    | 0.8817 | 0.8819 |
| 0.0006        | 7.6667  | 2300 | 0.7876          | 0.8517   | 0.8828    | 0.8517 | 0.8532 |
| 0.0004        | 8.0     | 2400 | 0.5605          | 0.8992   | 0.9061    | 0.8992 | 0.8987 |
| 0.0003        | 8.3333  | 2500 | 0.5620          | 0.9021   | 0.9084    | 0.9021 | 0.9016 |
| 0.0003        | 8.6667  | 2600 | 0.5725          | 0.9004   | 0.9071    | 0.9004 | 0.9001 |
| 0.0002        | 9.0     | 2700 | 0.5745          | 0.9008   | 0.9074    | 0.9008 | 0.9006 |
| 0.0002        | 9.3333  | 2800 | 0.5751          | 0.9012   | 0.9074    | 0.9012 | 0.9009 |
| 0.0002        | 9.6667  | 2900 | 0.5769          | 0.9017   | 0.9078    | 0.9017 | 0.9013 |
| 0.0002        | 10.0    | 3000 | 0.5792          | 0.9012   | 0.9075    | 0.9012 | 0.9009 |
| 0.0002        | 10.3333 | 3100 | 0.5812          | 0.9017   | 0.9078    | 0.9017 | 0.9014 |
| 0.0002        | 10.6667 | 3200 | 0.5832          | 0.9017   | 0.9078    | 0.9017 | 0.9014 |
| 0.0002        | 11.0    | 3300 | 0.5849          | 0.9017   | 0.9078    | 0.9017 | 0.9014 |
| 0.0002        | 11.3333 | 3400 | 0.5864          | 0.9021   | 0.9080    | 0.9021 | 0.9018 |
| 0.0002        | 11.6667 | 3500 | 0.5881          | 0.9021   | 0.9080    | 0.9021 | 0.9018 |
| 0.0001        | 12.0    | 3600 | 0.5898          | 0.9029   | 0.9086    | 0.9029 | 0.9026 |
| 0.0002        | 12.3333 | 3700 | 0.5913          | 0.9033   | 0.9089    | 0.9033 | 0.9030 |
| 0.0001        | 12.6667 | 3800 | 0.5925          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 13.0    | 3900 | 0.5936          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 13.3333 | 4000 | 0.5945          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 13.6667 | 4100 | 0.5953          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 14.0    | 4200 | 0.5961          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 14.3333 | 4300 | 0.5966          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 14.6667 | 4400 | 0.5970          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |
| 0.0001        | 15.0    | 4500 | 0.5971          | 0.9038   | 0.9093    | 0.9038 | 0.9034 |


### Framework versions

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

mix-subtype_iiia mix-subtype_iia mix-subtype_ivc mix-subtype_ivd mix-subtype_ia mix-subtype_va