readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: dataset_model2
    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: 0.8797595190380761
  ---
  
  <!-- 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. -->
  
  # dataset_model2
  
  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 imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5350
  - Accuracy: 0.8798
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.1141        | 0.99  | 62   | 0.4707          | 0.8647   |
  | 0.1098        | 1.99  | 124  | 0.4876          | 0.8597   |
  | 0.1444        | 2.99  | 186  | 0.4651          | 0.8647   |
  | 0.1088        | 3.99  | 248  | 0.5397          | 0.8527   |
  | 0.1404        | 4.99  | 310  | 0.4794          | 0.8727   |
  | 0.0656        | 5.99  | 372  | 0.5637          | 0.8507   |
  | 0.1126        | 6.99  | 434  | 0.5318          | 0.8597   |
  | 0.099         | 7.99  | 496  | 0.5522          | 0.8597   |
  | 0.0501        | 8.99  | 558  | 0.5654          | 0.8667   |
  | 0.0878        | 9.99  | 620  | 0.5915          | 0.8517   |
  | 0.0594        | 10.99 | 682  | 0.5846          | 0.8717   |
  | 0.0562        | 11.99 | 744  | 0.5191          | 0.8778   |
  | 0.0554        | 12.99 | 806  | 0.5425          | 0.8717   |
  | 0.0368        | 13.99 | 868  | 0.5725          | 0.8778   |
  | 0.0415        | 14.99 | 930  | 0.5790          | 0.8637   |
  | 0.0208        | 15.99 | 992  | 0.5319          | 0.8788   |
  | 0.026         | 16.99 | 1054 | 0.5622          | 0.8677   |
  | 0.0307        | 17.99 | 1116 | 0.5129          | 0.8878   |
  | 0.015         | 18.99 | 1178 | 0.5508          | 0.8768   |
  | 0.0263        | 19.99 | 1240 | 0.5350          | 0.8798   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  