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# vit-base-kidney-stone-3-Michel_Daudon_-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: 0.4251
- Accuracy: 0.885
- Precision: 0.9079
- Recall: 0.885
- F1: 0.8879

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2433        | 0.3333  | 100  | 0.6496          | 0.7967   | 0.8609    | 0.7967 | 0.7672 |
| 0.2097        | 0.6667  | 200  | 0.7346          | 0.7875   | 0.8299    | 0.7875 | 0.7848 |
| 0.1057        | 1.0     | 300  | 0.4491          | 0.8725   | 0.8916    | 0.8725 | 0.8719 |
| 0.0154        | 1.3333  | 400  | 0.6859          | 0.8508   | 0.8583    | 0.8508 | 0.8379 |
| 0.1202        | 1.6667  | 500  | 0.6336          | 0.8525   | 0.8773    | 0.8525 | 0.8478 |
| 0.0187        | 2.0     | 600  | 0.4251          | 0.885    | 0.9079    | 0.885  | 0.8879 |
| 0.0527        | 2.3333  | 700  | 0.6578          | 0.8533   | 0.8676    | 0.8533 | 0.8524 |
| 0.0191        | 2.6667  | 800  | 0.8956          | 0.8308   | 0.8736    | 0.8308 | 0.8306 |
| 0.0616        | 3.0     | 900  | 1.0589          | 0.8042   | 0.8572    | 0.8042 | 0.8088 |
| 0.0187        | 3.3333  | 1000 | 0.8005          | 0.8425   | 0.8624    | 0.8425 | 0.8383 |
| 0.0355        | 3.6667  | 1100 | 0.7664          | 0.865    | 0.8956    | 0.865  | 0.8614 |
| 0.0777        | 4.0     | 1200 | 0.9895          | 0.8158   | 0.8409    | 0.8158 | 0.8131 |
| 0.0017        | 4.3333  | 1300 | 0.5217          | 0.8983   | 0.9122    | 0.8983 | 0.8960 |
| 0.0013        | 4.6667  | 1400 | 0.5152          | 0.9      | 0.9129    | 0.9    | 0.8981 |
| 0.0011        | 5.0     | 1500 | 0.5119          | 0.905    | 0.9168    | 0.905  | 0.9036 |
| 0.0009        | 5.3333  | 1600 | 0.5259          | 0.905    | 0.9170    | 0.905  | 0.9038 |
| 0.0008        | 5.6667  | 1700 | 0.5235          | 0.9033   | 0.9151    | 0.9033 | 0.9020 |
| 0.0007        | 6.0     | 1800 | 0.5293          | 0.9042   | 0.9157    | 0.9042 | 0.9030 |
| 0.0007        | 6.3333  | 1900 | 0.5337          | 0.905    | 0.9163    | 0.905  | 0.9039 |
| 0.0006        | 6.6667  | 2000 | 0.5352          | 0.905    | 0.9165    | 0.905  | 0.9040 |
| 0.0005        | 7.0     | 2100 | 0.5415          | 0.9058   | 0.9170    | 0.9058 | 0.9049 |
| 0.0005        | 7.3333  | 2200 | 0.5467          | 0.9042   | 0.9152    | 0.9042 | 0.9033 |
| 0.0005        | 7.6667  | 2300 | 0.5490          | 0.905    | 0.9159    | 0.905  | 0.9040 |
| 0.0004        | 8.0     | 2400 | 0.5517          | 0.9067   | 0.9172    | 0.9067 | 0.9059 |
| 0.0004        | 8.3333  | 2500 | 0.5559          | 0.9075   | 0.9179    | 0.9075 | 0.9068 |
| 0.0004        | 8.6667  | 2600 | 0.5575          | 0.9075   | 0.9179    | 0.9075 | 0.9068 |
| 0.0003        | 9.0     | 2700 | 0.5613          | 0.9075   | 0.9179    | 0.9075 | 0.9068 |
| 0.0003        | 9.3333  | 2800 | 0.5647          | 0.9075   | 0.9183    | 0.9075 | 0.9069 |
| 0.0003        | 9.6667  | 2900 | 0.5675          | 0.9075   | 0.9183    | 0.9075 | 0.9069 |
| 0.0003        | 10.0    | 3000 | 0.5700          | 0.9075   | 0.9177    | 0.9075 | 0.9069 |
| 0.0003        | 10.3333 | 3100 | 0.5712          | 0.9067   | 0.9165    | 0.9067 | 0.9060 |
| 0.0003        | 10.6667 | 3200 | 0.5738          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0003        | 11.0    | 3300 | 0.5768          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0003        | 11.3333 | 3400 | 0.5792          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 11.6667 | 3500 | 0.5806          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 12.0    | 3600 | 0.5830          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 12.3333 | 3700 | 0.5847          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 12.6667 | 3800 | 0.5860          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 13.0    | 3900 | 0.5875          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 13.3333 | 4000 | 0.5889          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 13.6667 | 4100 | 0.5898          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 14.0    | 4200 | 0.5906          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 14.3333 | 4300 | 0.5914          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 14.6667 | 4400 | 0.5918          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |
| 0.0002        | 15.0    | 4500 | 0.5919          | 0.9067   | 0.9159    | 0.9067 | 0.9061 |


### Framework versions

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

sec-subtype_iva sec-subtype_iva2 sec-subtype_ivc sec-subtype_ivd sec-subtype_ia sec-subtype_va