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# vit-base-kidney-stone-2-Michel_Daudon_-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: 1.0040
- Accuracy: 0.6917
- Precision: 0.7078
- Recall: 0.6917
- F1: 0.6859

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3876        | 0.3333  | 100  | 1.0040          | 0.6917   | 0.7078    | 0.6917 | 0.6859 |
| 0.1233        | 0.6667  | 200  | 1.0383          | 0.7416   | 0.7515    | 0.7416 | 0.7427 |
| 0.0709        | 1.0     | 300  | 1.3706          | 0.7294   | 0.7222    | 0.7294 | 0.7186 |
| 0.0379        | 1.3333  | 400  | 1.3745          | 0.7105   | 0.7178    | 0.7105 | 0.7045 |
| 0.0256        | 1.6667  | 500  | 1.1379          | 0.7939   | 0.8114    | 0.7939 | 0.7879 |
| 0.0722        | 2.0     | 600  | 1.6149          | 0.6966   | 0.7899    | 0.6966 | 0.6896 |
| 0.006         | 2.3333  | 700  | 1.2398          | 0.7351   | 0.7541    | 0.7351 | 0.7410 |
| 0.0055        | 2.6667  | 800  | 1.6718          | 0.6893   | 0.7319    | 0.6893 | 0.6792 |
| 0.0597        | 3.0     | 900  | 1.3485          | 0.7637   | 0.7550    | 0.7637 | 0.7530 |
| 0.0621        | 3.3333  | 1000 | 1.2455          | 0.7907   | 0.7990    | 0.7907 | 0.7801 |
| 0.049         | 3.6667  | 1100 | 1.3096          | 0.7841   | 0.7851    | 0.7841 | 0.7808 |
| 0.0023        | 4.0     | 1200 | 1.3507          | 0.7800   | 0.7836    | 0.7800 | 0.7802 |
| 0.0807        | 4.3333  | 1300 | 1.5510          | 0.7318   | 0.7666    | 0.7318 | 0.7421 |
| 0.0486        | 4.6667  | 1400 | 1.7065          | 0.6860   | 0.7611    | 0.6860 | 0.6799 |
| 0.0861        | 5.0     | 1500 | 1.2896          | 0.7702   | 0.7706    | 0.7702 | 0.7677 |
| 0.0046        | 5.3333  | 1600 | 1.4991          | 0.7473   | 0.7584    | 0.7473 | 0.7467 |
| 0.0015        | 5.6667  | 1700 | 1.5548          | 0.7539   | 0.7529    | 0.7539 | 0.7502 |
| 0.0117        | 6.0     | 1800 | 1.6813          | 0.7261   | 0.7456    | 0.7261 | 0.7325 |
| 0.0481        | 6.3333  | 1900 | 1.8190          | 0.7490   | 0.7836    | 0.7490 | 0.7511 |
| 0.0011        | 6.6667  | 2000 | 1.8774          | 0.6877   | 0.6960    | 0.6877 | 0.6881 |
| 0.0636        | 7.0     | 2100 | 1.8792          | 0.7204   | 0.7292    | 0.7204 | 0.7164 |
| 0.0183        | 7.3333  | 2200 | 1.7606          | 0.7596   | 0.8027    | 0.7596 | 0.7589 |
| 0.0023        | 7.6667  | 2300 | 1.4724          | 0.7449   | 0.7879    | 0.7449 | 0.7466 |
| 0.0007        | 8.0     | 2400 | 1.4367          | 0.7751   | 0.7979    | 0.7751 | 0.7740 |
| 0.0007        | 8.3333  | 2500 | 1.4553          | 0.7760   | 0.7965    | 0.7760 | 0.7749 |
| 0.0006        | 8.6667  | 2600 | 1.4727          | 0.7776   | 0.7982    | 0.7776 | 0.7767 |
| 0.0006        | 9.0     | 2700 | 1.4842          | 0.7768   | 0.7960    | 0.7768 | 0.7758 |
| 0.0005        | 9.3333  | 2800 | 1.4965          | 0.7776   | 0.7963    | 0.7776 | 0.7766 |
| 0.0005        | 9.6667  | 2900 | 1.5049          | 0.7792   | 0.7966    | 0.7792 | 0.7789 |
| 0.0005        | 10.0    | 3000 | 1.5151          | 0.7792   | 0.7966    | 0.7792 | 0.7789 |
| 0.0004        | 10.3333 | 3100 | 1.5238          | 0.7792   | 0.7958    | 0.7792 | 0.7792 |
| 0.0004        | 10.6667 | 3200 | 1.5329          | 0.7776   | 0.7932    | 0.7776 | 0.7775 |
| 0.0004        | 11.0    | 3300 | 1.5415          | 0.7760   | 0.7907    | 0.7760 | 0.7758 |
| 0.0004        | 11.3333 | 3400 | 1.5492          | 0.7743   | 0.7882    | 0.7743 | 0.7742 |
| 0.0003        | 11.6667 | 3500 | 1.5563          | 0.7735   | 0.7870    | 0.7735 | 0.7734 |
| 0.0003        | 12.0    | 3600 | 1.5631          | 0.7735   | 0.7870    | 0.7735 | 0.7734 |
| 0.0003        | 12.3333 | 3700 | 1.5691          | 0.7735   | 0.7870    | 0.7735 | 0.7734 |
| 0.0003        | 12.6667 | 3800 | 1.5742          | 0.7735   | 0.7870    | 0.7735 | 0.7734 |
| 0.0003        | 13.0    | 3900 | 1.5795          | 0.7743   | 0.7878    | 0.7743 | 0.7743 |
| 0.0003        | 13.3333 | 4000 | 1.5838          | 0.7743   | 0.7875    | 0.7743 | 0.7745 |
| 0.0003        | 13.6667 | 4100 | 1.5876          | 0.7727   | 0.7851    | 0.7727 | 0.7728 |
| 0.0003        | 14.0    | 4200 | 1.5903          | 0.7735   | 0.7858    | 0.7735 | 0.7737 |
| 0.0003        | 14.3333 | 4300 | 1.5926          | 0.7735   | 0.7858    | 0.7735 | 0.7737 |
| 0.0003        | 14.6667 | 4400 | 1.5938          | 0.7735   | 0.7858    | 0.7735 | 0.7737 |
| 0.0003        | 15.0    | 4500 | 1.5943          | 0.7735   | 0.7858    | 0.7735 | 0.7737 |


### Framework versions

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

sur-subtype_iva sur-subtype_iva2 sur-subtype_ivc sur-subtype_ivd sur-subtype_ia sur-subtype_va