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# vit-base-oxford-brain-tumor_try_stuff

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Mahadih534/brain-tumor-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5406
- Accuracy: 0.8077
- Precision: 0.8514
- Recall: 0.8077
- F1: 0.7830

## 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: 0.0003
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6608        | 1.0   | 11   | 0.5499          | 0.8      | 0.8308    | 0.8    | 0.8039 |
| 0.6097        | 2.0   | 22   | 0.4836          | 0.88     | 0.8989    | 0.88   | 0.8731 |
| 0.5882        | 3.0   | 33   | 0.4191          | 0.88     | 0.8853    | 0.88   | 0.8812 |
| 0.5673        | 4.0   | 44   | 0.4871          | 0.84     | 0.8561    | 0.84   | 0.8427 |
| 0.5619        | 5.0   | 55   | 0.4079          | 0.92     | 0.92      | 0.92   | 0.92   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

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