---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-mri
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: mriDataSet
      type: imagefolder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9827025893699549
---

<!-- 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-mri

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 mriDataSet dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0453
- Accuracy: 0.9827

## 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: 3e-05
- train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.04          | 0.3   | 500  | 0.0828          | 0.9690   |
| 0.0765        | 0.59  | 1000 | 0.0623          | 0.9750   |
| 0.0479        | 0.89  | 1500 | 0.0453          | 0.9827   |
| 0.0199        | 1.18  | 2000 | 0.0524          | 0.9857   |
| 0.0114        | 1.48  | 2500 | 0.0484          | 0.9861   |
| 0.008         | 1.78  | 3000 | 0.0566          | 0.9852   |
| 0.0051        | 2.07  | 3500 | 0.0513          | 0.9874   |
| 0.0008        | 2.37  | 4000 | 0.0617          | 0.9874   |
| 0.0021        | 2.66  | 4500 | 0.0664          | 0.9870   |
| 0.0005        | 2.96  | 5000 | 0.0639          | 0.9872   |
| 0.001         | 3.25  | 5500 | 0.0644          | 0.9879   |
| 0.0004        | 3.55  | 6000 | 0.0672          | 0.9875   |
| 0.0003        | 3.85  | 6500 | 0.0690          | 0.9879   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1