readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - image_folder
  metrics:
  - accuracy
  model-index:
  - name: vit-base-patch16-224-in21k-finetuned-cassava3
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: image_folder
        type: image_folder
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8855140186915887
  ---
  
  <!-- 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-in21k-finetuned-cassava3
  
  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 image_folder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3419
  - Accuracy: 0.8855
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5624        | 0.99  | 133  | 0.5866          | 0.8166   |
  | 0.4717        | 1.99  | 266  | 0.4245          | 0.8692   |
  | 0.4105        | 2.99  | 399  | 0.3708          | 0.8811   |
  | 0.3753        | 3.99  | 532  | 0.3646          | 0.8787   |
  | 0.2997        | 4.99  | 665  | 0.3655          | 0.8780   |
  | 0.3176        | 5.99  | 798  | 0.3545          | 0.8822   |
  | 0.2849        | 6.99  | 931  | 0.3441          | 0.8850   |
  | 0.2931        | 7.99  | 1064 | 0.3419          | 0.8855   |
  | 0.27          | 8.99  | 1197 | 0.3419          | 0.8848   |
  | 0.2927        | 9.99  | 1330 | 0.3403          | 0.8853   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  