---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-oxford-brain-tumor
  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: 0.6153846153846154
---

<!-- 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

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: 0.6187
- Accuracy: 0.6154

## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 13   | 0.5587          | 0.68     |
| No log        | 2.0   | 26   | 0.5209          | 0.8      |
| No log        | 3.0   | 39   | 0.4983          | 0.84     |
| No log        | 4.0   | 52   | 0.4822          | 0.8      |
| No log        | 5.0   | 65   | 0.4770          | 0.8      |


### Framework versions

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