readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  datasets:
  - cats_vs_dogs
  metrics:
  - accuracy
  model-index:
  - name: vit-base-cats-vs-dogs
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cats_vs_dogs
        type: cats_vs_dogs
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9937357630979499
  ---
  
  <!-- 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-cats-vs-dogs
  
  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 cats_vs_dogs dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0182
  - Accuracy: 0.9937
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 1337
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.1177        | 1.0   | 622  | 0.0473          | 0.9832   |
  | 0.057         | 2.0   | 1244 | 0.0362          | 0.9883   |
  | 0.0449        | 3.0   | 1866 | 0.0261          | 0.9886   |
  | 0.066         | 4.0   | 2488 | 0.0248          | 0.9923   |
  | 0.0328        | 5.0   | 3110 | 0.0182          | 0.9937   |
  
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.8.1+cu111
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  