readme: ---
  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
  