---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-base-oxford-brain-tumor_x-ray
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: Mahadih534/brain-tumor-dataset
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7692307692307693
    - name: Precision
      type: precision
      value: 0.7692307692307693
    - name: Recall
      type: recall
      value: 0.7692307692307693
    - name: F1
      type: f1
      value: 0.7692307692307693
---

<!-- 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-oxford-brain-tumor_x-ray

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: 0.5912
- Accuracy: 0.7692
- Precision: 0.7692
- Recall: 0.7692
- F1: 0.7692

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6752        | 1.0   | 11   | 0.4894          | 0.76     | 0.7148    | 0.76   | 0.7114 |
| 0.5673        | 2.0   | 22   | 0.4630          | 0.72     | 0.57      | 0.72   | 0.6363 |
| 0.6173        | 3.0   | 33   | 0.4269          | 0.92     | 0.92      | 0.92   | 0.92   |
| 0.5562        | 4.0   | 44   | 0.5047          | 0.84     | 0.8653    | 0.84   | 0.8470 |
| 0.5285        | 5.0   | 55   | 0.4036          | 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