
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

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: 0.4058
- Accuracy: 0.8942
- Precision: 0.9042
- Recall: 0.8942
- F1: 0.8940

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2458        | 0.3333  | 100  | 0.6117          | 0.8183   | 0.8403    | 0.8183 | 0.8152 |
| 0.1311        | 0.6667  | 200  | 0.4116          | 0.8696   | 0.8705    | 0.8696 | 0.8694 |
| 0.037         | 1.0     | 300  | 0.4058          | 0.8942   | 0.9042    | 0.8942 | 0.8940 |
| 0.149         | 1.3333  | 400  | 0.4525          | 0.8846   | 0.8926    | 0.8846 | 0.8818 |
| 0.1007        | 1.6667  | 500  | 0.8220          | 0.7908   | 0.8404    | 0.7908 | 0.7917 |
| 0.0189        | 2.0     | 600  | 0.5199          | 0.8762   | 0.8808    | 0.8762 | 0.8756 |
| 0.0531        | 2.3333  | 700  | 0.5875          | 0.8804   | 0.8944    | 0.8804 | 0.8784 |
| 0.0169        | 2.6667  | 800  | 0.7323          | 0.8488   | 0.8554    | 0.8488 | 0.8479 |
| 0.0076        | 3.0     | 900  | 0.4755          | 0.8954   | 0.9015    | 0.8954 | 0.8931 |
| 0.0015        | 3.3333  | 1000 | 0.4957          | 0.9025   | 0.9070    | 0.9025 | 0.9006 |
| 0.012         | 3.6667  | 1100 | 0.8585          | 0.8367   | 0.8589    | 0.8367 | 0.8292 |
| 0.1429        | 4.0     | 1200 | 0.5490          | 0.8804   | 0.8904    | 0.8804 | 0.8785 |
| 0.0242        | 4.3333  | 1300 | 0.4934          | 0.9021   | 0.9144    | 0.9021 | 0.8970 |
| 0.001         | 4.6667  | 1400 | 0.5054          | 0.9062   | 0.9195    | 0.9062 | 0.9039 |
| 0.0012        | 5.0     | 1500 | 0.7132          | 0.8675   | 0.8886    | 0.8675 | 0.8680 |
| 0.0043        | 5.3333  | 1600 | 0.7203          | 0.8871   | 0.9069    | 0.8871 | 0.8844 |
| 0.0007        | 5.6667  | 1700 | 0.5250          | 0.9079   | 0.9097    | 0.9079 | 0.9072 |
| 0.043         | 6.0     | 1800 | 0.6485          | 0.8788   | 0.8943    | 0.8788 | 0.8740 |
| 0.0006        | 6.3333  | 1900 | 0.5322          | 0.8996   | 0.9015    | 0.8996 | 0.8996 |
| 0.0005        | 6.6667  | 2000 | 0.6328          | 0.8904   | 0.9044    | 0.8904 | 0.8872 |
| 0.0004        | 7.0     | 2100 | 0.6130          | 0.8942   | 0.9061    | 0.8942 | 0.8916 |
| 0.0004        | 7.3333  | 2200 | 0.6070          | 0.8967   | 0.9076    | 0.8967 | 0.8942 |
| 0.0003        | 7.6667  | 2300 | 0.6067          | 0.8983   | 0.9095    | 0.8983 | 0.8960 |
| 0.0003        | 8.0     | 2400 | 0.6028          | 0.9004   | 0.9107    | 0.9004 | 0.8981 |
| 0.0003        | 8.3333  | 2500 | 0.6009          | 0.9021   | 0.9118    | 0.9021 | 0.8999 |
| 0.0003        | 8.6667  | 2600 | 0.6020          | 0.9042   | 0.9132    | 0.9042 | 0.9021 |
| 0.0003        | 9.0     | 2700 | 0.6018          | 0.9042   | 0.9130    | 0.9042 | 0.9022 |
| 0.0002        | 9.3333  | 2800 | 0.6026          | 0.9042   | 0.9125    | 0.9042 | 0.9022 |
| 0.0002        | 9.6667  | 2900 | 0.6037          | 0.9042   | 0.9125    | 0.9042 | 0.9022 |
| 0.0002        | 10.0    | 3000 | 0.6053          | 0.905    | 0.9128    | 0.905  | 0.9031 |
| 0.0002        | 10.3333 | 3100 | 0.6060          | 0.9058   | 0.9133    | 0.9058 | 0.9040 |
| 0.0002        | 10.6667 | 3200 | 0.6082          | 0.9058   | 0.9133    | 0.9058 | 0.9040 |
| 0.0002        | 11.0    | 3300 | 0.6095          | 0.9058   | 0.9133    | 0.9058 | 0.9040 |
| 0.0002        | 11.3333 | 3400 | 0.6109          | 0.9062   | 0.9136    | 0.9062 | 0.9045 |
| 0.0002        | 11.6667 | 3500 | 0.6122          | 0.9062   | 0.9136    | 0.9062 | 0.9045 |
| 0.0002        | 12.0    | 3600 | 0.6135          | 0.9062   | 0.9136    | 0.9062 | 0.9045 |
| 0.0002        | 12.3333 | 3700 | 0.6150          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0002        | 12.6667 | 3800 | 0.6159          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0002        | 13.0    | 3900 | 0.6169          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0002        | 13.3333 | 4000 | 0.6179          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0001        | 13.6667 | 4100 | 0.6187          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0001        | 14.0    | 4200 | 0.6193          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0001        | 14.3333 | 4300 | 0.6198          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0001        | 14.6667 | 4400 | 0.6201          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |
| 0.0001        | 15.0    | 4500 | 0.6202          | 0.9067   | 0.9139    | 0.9067 | 0.9050 |


### 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