readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - image_folder
  metrics:
  - accuracy
  model-index:
  - name: resnet-50-finetuned-FER2013-0.003
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: image_folder
        type: image_folder
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6971301198105322
  ---
  
  <!-- 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. -->
  
  # resnet-50-finetuned-FER2013-0.003
  
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9036
  - Accuracy: 0.6971
  
  ## 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: 0.003
  - 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: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.4393        | 1.0   | 224  | 1.2746          | 0.5173   |
  | 1.2564        | 2.0   | 448  | 1.1456          | 0.5542   |
  | 1.218         | 3.0   | 672  | 1.1102          | 0.5816   |
  | 1.1919        | 4.0   | 896  | 1.0255          | 0.6151   |
  | 1.1222        | 5.0   | 1120 | 1.0257          | 0.6167   |
  | 1.0925        | 6.0   | 1344 | 0.9676          | 0.6317   |
  | 1.0241        | 7.0   | 1568 | 0.9406          | 0.6510   |
  | 1.0015        | 8.0   | 1792 | 0.9465          | 0.6532   |
  | 0.987         | 9.0   | 2016 | 0.9002          | 0.6748   |
  | 0.9768        | 10.0  | 2240 | 0.9086          | 0.6737   |
  | 0.9408        | 11.0  | 2464 | 0.8975          | 0.6793   |
  | 0.8907        | 12.0  | 2688 | 0.8966          | 0.6769   |
  | 0.8051        | 13.0  | 2912 | 0.9142          | 0.6826   |
  | 0.8169        | 14.0  | 3136 | 0.9082          | 0.6870   |
  | 0.7729        | 15.0  | 3360 | 0.9036          | 0.6971   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  