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# image_classification_for_fracture

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: 0.4783
- Accuracy: 0.85

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 2    | 0.6696          | 0.75     |
| No log        | 2.0   | 5    | 0.6296          | 0.7      |
| No log        | 2.8   | 7    | 0.5853          | 0.775    |
| 0.639         | 4.0   | 10   | 0.5731          | 0.8      |
| 0.639         | 4.8   | 12   | 0.5430          | 0.825    |
| 0.639         | 6.0   | 15   | 0.5223          | 0.85     |
| 0.639         | 6.8   | 17   | 0.5036          | 0.8      |
| 0.5453        | 8.0   | 20   | 0.4783          | 0.85     |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2

fractured nonfractured