readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: vit-base-patch16-224-finetuned-imageclassification
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9501779359430605
  ---
  
  <!-- 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-patch16-224-finetuned-imageclassification
  
  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1790
  - Accuracy: 0.9502
  
  ## 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: 0.0001
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 9    | 0.5791          | 0.9004   |
  | 1.4122        | 2.0   | 18   | 0.2002          | 0.9359   |
  | 0.3147        | 3.0   | 27   | 0.1717          | 0.9502   |
  | 0.1907        | 4.0   | 36   | 0.1632          | 0.9466   |
  | 0.158         | 5.0   | 45   | 0.1822          | 0.9466   |
  | 0.1169        | 6.0   | 54   | 0.1778          | 0.9502   |
  | 0.0984        | 7.0   | 63   | 0.1552          | 0.9573   |
  | 0.0971        | 8.0   | 72   | 0.1835          | 0.9502   |
  | 0.0965        | 9.0   | 81   | 0.1878          | 0.9484   |
  | 0.0766        | 10.0  | 90   | 0.1790          | 0.9502   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  