---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-brain-tumour
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: Simezu/brain-tumour-MRI-scan
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9925442684063374
---

<!-- 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-brain-tumour

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 Simezu/brain-tumour-MRI-scan dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0309
- Accuracy: 0.9925

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.227         | 0.4255 | 100  | 0.3067          | 0.8910   |
| 0.0659        | 0.8511 | 200  | 0.1109          | 0.9627   |
| 0.0404        | 1.2766 | 300  | 0.0900          | 0.9776   |
| 0.05          | 1.7021 | 400  | 0.1082          | 0.9748   |
| 0.006         | 2.1277 | 500  | 0.0374          | 0.9888   |
| 0.0147        | 2.5532 | 600  | 0.0541          | 0.9888   |
| 0.0105        | 2.9787 | 700  | 0.0359          | 0.9907   |
| 0.0032        | 3.4043 | 800  | 0.0392          | 0.9907   |
| 0.0055        | 3.8298 | 900  | 0.0309          | 0.9925   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1