---
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_try_stuff
  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: 0.8076923076923077
    - name: Precision
      type: precision
      value: 0.8513986013986015
    - name: Recall
      type: recall
      value: 0.8076923076923077
    - name: F1
      type: f1
      value: 0.7830374753451677
---

<!-- 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_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