readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: microsoft-resnet-50-cartoon-emotion-detection
    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: 0.8165137614678899
      - name: Precision
        type: precision
        value: 0.8181998512273742
      - name: Recall
        type: recall
        value: 0.8165137614678899
      - name: F1
        type: f1
        value: 0.8172526992448356
  ---
  
  <!-- 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. -->
  
  # microsoft-resnet-50-cartoon-emotion-detection
  
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4801
  - Accuracy: 0.8165
  - Precision: 0.8182
  - Recall: 0.8165
  - F1: 0.8173
  
  ## 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: 0.00012
  - 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: 80
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 0.97  | 8    | 1.3855          | 0.2294   | 0.2697    | 0.2294 | 0.2165 |
  | 1.4222        | 1.97  | 16   | 1.3792          | 0.2569   | 0.2808    | 0.2569 | 0.2543 |
  | 1.4183        | 2.97  | 24   | 1.3646          | 0.3853   | 0.4102    | 0.3853 | 0.3511 |
  | 1.4097        | 3.97  | 32   | 1.3563          | 0.4128   | 0.5062    | 0.4128 | 0.3245 |
  | 1.3944        | 4.97  | 40   | 1.3462          | 0.4037   | 0.3927    | 0.4037 | 0.2939 |
  | 1.3944        | 5.97  | 48   | 1.3223          | 0.4037   | 0.5152    | 0.4037 | 0.2841 |
  | 1.411         | 6.97  | 56   | 1.3040          | 0.4128   | 0.4404    | 0.4128 | 0.2985 |
  | 1.346         | 7.97  | 64   | 1.2700          | 0.4954   | 0.4960    | 0.4954 | 0.4093 |
  | 1.3031        | 8.97  | 72   | 1.2150          | 0.5596   | 0.5440    | 0.5596 | 0.4672 |
  | 1.2371        | 9.97  | 80   | 1.1580          | 0.5963   | 0.5659    | 0.5963 | 0.5101 |
  | 1.2371        | 10.97 | 88   | 1.0670          | 0.6055   | 0.7279    | 0.6055 | 0.5211 |
  | 1.1736        | 11.97 | 96   | 0.9856          | 0.6606   | 0.5537    | 0.6606 | 0.5772 |
  | 1.0457        | 12.97 | 104  | 0.8963          | 0.6697   | 0.7631    | 0.6697 | 0.5965 |
  | 0.953         | 13.97 | 112  | 0.8547          | 0.6697   | 0.6885    | 0.6697 | 0.6081 |
  | 0.8579        | 14.97 | 120  | 0.7849          | 0.7156   | 0.7396    | 0.7156 | 0.6643 |
  | 0.8579        | 15.97 | 128  | 0.7564          | 0.7431   | 0.7372    | 0.7431 | 0.7119 |
  | 0.8167        | 16.97 | 136  | 0.7133          | 0.7615   | 0.7507    | 0.7615 | 0.7211 |
  | 0.7273        | 17.97 | 144  | 0.6888          | 0.7523   | 0.7379    | 0.7523 | 0.7202 |
  | 0.6547        | 18.97 | 152  | 0.6592          | 0.7798   | 0.7773    | 0.7798 | 0.7577 |
  | 0.5963        | 19.97 | 160  | 0.6136          | 0.7706   | 0.7642    | 0.7706 | 0.7551 |
  | 0.5963        | 20.97 | 168  | 0.5723          | 0.7890   | 0.7802    | 0.7890 | 0.7787 |
  | 0.551         | 21.97 | 176  | 0.5686          | 0.7890   | 0.7761    | 0.7890 | 0.7781 |
  | 0.4929        | 22.97 | 184  | 0.5597          | 0.7706   | 0.7649    | 0.7706 | 0.7651 |
  | 0.4309        | 23.97 | 192  | 0.5234          | 0.7890   | 0.7774    | 0.7890 | 0.7810 |
  | 0.3945        | 24.97 | 200  | 0.5008          | 0.7890   | 0.7837    | 0.7890 | 0.7813 |
  | 0.3945        | 25.97 | 208  | 0.5289          | 0.7523   | 0.7537    | 0.7523 | 0.7529 |
  | 0.3704        | 26.97 | 216  | 0.4399          | 0.7982   | 0.7958    | 0.7982 | 0.7963 |
  | 0.3267        | 27.97 | 224  | 0.4539          | 0.8073   | 0.7983    | 0.8073 | 0.8005 |
  | 0.2966        | 28.97 | 232  | 0.4735          | 0.7798   | 0.7892    | 0.7798 | 0.7837 |
  | 0.2645        | 29.97 | 240  | 0.4594          | 0.7706   | 0.7706    | 0.7706 | 0.7706 |
  | 0.2645        | 30.97 | 248  | 0.4699          | 0.7523   | 0.7554    | 0.7523 | 0.7533 |
  | 0.2527        | 31.97 | 256  | 0.4551          | 0.7890   | 0.7856    | 0.7890 | 0.7857 |
  | 0.2202        | 32.97 | 264  | 0.4458          | 0.8165   | 0.8198    | 0.8165 | 0