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# Wound-Image-classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1209
- Accuracy: 0.965

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0919        | 1.0   | 200  | 0.7780          | 0.76     |
| 0.6157        | 2.0   | 400  | 0.5695          | 0.7925   |
| 0.4894        | 3.0   | 600  | 0.3667          | 0.8775   |
| 0.3786        | 4.0   | 800  | 0.4436          | 0.8625   |
| 0.3142        | 5.0   | 1000 | 0.4412          | 0.8625   |
| 0.2636        | 6.0   | 1200 | 0.4430          | 0.86     |
| 0.198         | 7.0   | 1400 | 0.2760          | 0.9175   |
| 0.1456        | 8.0   | 1600 | 0.2211          | 0.93     |
| 0.1586        | 9.0   | 1800 | 0.3520          | 0.905    |
| 0.1307        | 10.0  | 2000 | 0.3188          | 0.9175   |
| 0.106         | 11.0  | 2200 | 0.3167          | 0.925    |
| 0.0975        | 12.0  | 2400 | 0.2633          | 0.92     |
| 0.0734        | 13.0  | 2600 | 0.1813          | 0.9525   |
| 0.0994        | 14.0  | 2800 | 0.2150          | 0.945    |
| 0.0622        | 15.0  | 3000 | 0.1757          | 0.955    |
| 0.0609        | 16.0  | 3200 | 0.1209          | 0.965    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

abrasions bruises burns cut diabetic wounds laseration normal pressure wounds surgical wounds venous wounds