readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - image_folder
  metrics:
  - accuracy
  model-index:
  - name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: image_folder
        type: image_folder
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7220674282529953
  ---
  
  <!-- 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. -->
  
  # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05
  
  This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7881
  - Accuracy: 0.7221
  
  ## 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: 7e-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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.2307        | 1.0   | 224  | 1.0863          | 0.5874   |
  | 1.0893        | 2.0   | 448  | 0.9700          | 0.6362   |
  | 1.0244        | 3.0   | 672  | 0.8859          | 0.6757   |
  | 1.016         | 4.0   | 896  | 0.8804          | 0.6787   |
  | 0.9089        | 5.0   | 1120 | 0.8611          | 0.6897   |
  | 0.8935        | 6.0   | 1344 | 0.8283          | 0.7028   |
  | 0.8403        | 7.0   | 1568 | 0.8116          | 0.7102   |
  | 0.8179        | 8.0   | 1792 | 0.7934          | 0.7166   |
  | 0.7764        | 9.0   | 2016 | 0.7865          | 0.7208   |
  | 0.771         | 10.0  | 2240 | 0.7881          | 0.7221   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  