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# Wound-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: 0.1836
- Accuracy: 0.9575

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1241        | 1.0   | 200  | 0.7452          | 0.765    |
| 0.5854        | 2.0   | 400  | 0.4880          | 0.835    |
| 0.4279        | 3.0   | 600  | 0.5049          | 0.8375   |
| 0.4041        | 4.0   | 800  | 0.3321          | 0.8975   |
| 0.2805        | 5.0   | 1000 | 0.4105          | 0.895    |
| 0.279         | 6.0   | 1200 | 0.4269          | 0.8825   |
| 0.1782        | 7.0   | 1400 | 0.3583          | 0.905    |
| 0.1834        | 8.0   | 1600 | 0.3009          | 0.925    |
| 0.1197        | 9.0   | 1800 | 0.3020          | 0.93     |
| 0.1231        | 10.0  | 2000 | 0.3352          | 0.9225   |
| 0.1273        | 11.0  | 2200 | 0.2908          | 0.91     |
| 0.1019        | 12.0  | 2400 | 0.2528          | 0.94     |
| 0.0951        | 13.0  | 2600 | 0.2989          | 0.9325   |
| 0.0957        | 14.0  | 2800 | 0.3189          | 0.9325   |
| 0.0618        | 15.0  | 3000 | 0.1973          | 0.9475   |
| 0.0583        | 16.0  | 3200 | 0.1836          | 0.9575   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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