---
license: apache-2.0
base_model: google/vit-large-patch32-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-brain-xray
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: sartajbhuvaji/Brain-Tumor-Classification
      type: imagefolder
      config: default
      split: Testing
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7741116751269036
---

<!-- 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-large-brain-xray

This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the sartajbhuvaji/Brain-Tumor-Classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9050
- Accuracy: 0.7741

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.352         | 0.5556 | 100  | 1.2267          | 0.6294   |
| 0.1612        | 1.1111 | 200  | 1.0895          | 0.7538   |
| 0.0473        | 1.6667 | 300  | 0.9050          | 0.7741   |
| 0.0525        | 2.2222 | 400  | 1.0663          | 0.7690   |
| 0.0123        | 2.7778 | 500  | 1.2450          | 0.7462   |
| 0.0066        | 3.3333 | 600  | 1.1283          | 0.7817   |
| 0.0126        | 3.8889 | 700  | 1.1717          | 0.7843   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1