readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: microsoft-resnet-50-cartoon-face-recognition
    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: 0.7754629629629629
      - name: Precision
        type: precision
        value: 0.7715337787579363
      - name: Recall
        type: recall
        value: 0.7754629629629629
      - name: F1
        type: f1
        value: 0.7676128880040883
  ---
  
  <!-- 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-face-recognition
  
  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: 0.8508
  - Accuracy: 0.7755
  - Precision: 0.7715
  - Recall: 0.7755
  - F1: 0.7676
  
  ## 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: 120
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 0.89   | 6    | 3.1774          | 0.0370   | 0.0069    | 0.0370 | 0.0098 |
  | 3.4185        | 1.89   | 12   | 3.1739          | 0.0301   | 0.0100    | 0.0301 | 0.0126 |
  | 3.4185        | 2.89   | 18   | 3.1668          | 0.0440   | 0.0805    | 0.0440 | 0.0340 |
  | 3.6463        | 3.89   | 24   | 3.1583          | 0.0370   | 0.0180    | 0.0370 | 0.0151 |
  | 3.3899        | 4.89   | 30   | 3.1425          | 0.0741   | 0.0610    | 0.0741 | 0.0453 |
  | 3.3899        | 5.89   | 36   | 3.1262          | 0.0856   | 0.0334    | 0.0856 | 0.0405 |
  | 3.5947        | 6.89   | 42   | 3.1055          | 0.1019   | 0.0784    | 0.1019 | 0.0481 |
  | 3.5947        | 7.89   | 48   | 3.0841          | 0.1181   | 0.1071    | 0.1181 | 0.0500 |
  | 3.5553        | 8.89   | 54   | 3.0650          | 0.1065   | 0.0216    | 0.1065 | 0.0343 |
  | 3.2713        | 9.89   | 60   | 3.0351          | 0.1273   | 0.0323    | 0.1273 | 0.0418 |
  | 3.2713        | 10.89  | 66   | 3.0069          | 0.1227   | 0.0311    | 0.1227 | 0.0390 |
  | 3.4382        | 11.89  | 72   | 2.9754          | 0.1204   | 0.0353    | 0.1204 | 0.0366 |
  | 3.4382        | 12.89  | 78   | 2.9455          | 0.1227   | 0.0224    | 0.1227 | 0.0338 |
  | 3.3573        | 13.89  | 84   | 2.9167          | 0.1204   | 0.0213    | 0.1204 | 0.0332 |
  | 3.0549        | 14.89  | 90   | 2.8841          | 0.1227   | 0.0474    | 0.1227 | 0.0408 |
  | 3.0549        | 15.89  | 96   | 2.8534          | 0.1412   | 0.1174    | 0.1412 | 0.0540 |
  | 3.1853        | 16.89  | 102  | 2.8143          | 0.1505   | 0.1595    | 0.1505 | 0.0667 |
  | 3.1853        | 17.89  | 108  | 2.7771          | 0.1667   | 0.1693    | 0.1667 | 0.0719 |
  | 3.0871        | 18.89  | 114  | 2.7400          | 0.1759   | 0.1454    | 0.1759 | 0.0896 |
  | 2.7666        | 19.89  | 120  | 2.7048          | 0.2014   | 0.0927    | 0.2014 | 0.1051 |
  | 2.7666        | 20.89  | 126  | 2.6458          | 0.2315   | 0.1622    | 0.2315 | 0.1250 |
  | 2.846         | 21.89  | 132  | 2.5803          | 0.2569   | 0.2386    | 0.2569 | 0.1470 |
  | 2.846         | 22.89  | 138  | 2.5291          | 0.2639   | 0.2725    | 0.2639 | 0.1523 |
  | 2.7428        | 23.89  | 144  | 2.4916          | 0.2870   | 0.2114    | 0.2870 | 0.1811 |
  | 2.4183        | 24.89  | 150  | 2.4273          | 0.3079   | 0.2322    | 0.3079 | 0.2048 |
  | 2.4183        | 25.89  | 156  | 2.3923          | 0.3194   | 0.2937    | 0.3194 | 0.2238 |
  | 2.5064        | 26.89  | 162  | 2.3349          | 0.3403   | 0.3183    | 0.3403 | 0.2494 |
  | 2.5064        | 27.89  | 168  | 2.2977          | 0.3542   | 0.3554    | 0.3542 | 0.2663 |
  | 2.4046        | 28.89  | 174  | 2.2363          | 0.3773   | 0.3214    | 0.3773 | 0.2981 |
  | 2.1201        | 29.89  | 180  | 2.1791          | 0.3889   | 0.4024    | 0.3889 | 0.3179 |
  | 2.1201        | 30.89  | 186  | 2.1448          | 0.4144   | 0.4079    | 0.4144 | 0.3455 |
  | 2.1705        | 31.89  | 192  | 2.0969          | 0.4306   | 0.4214    | 0.4306 | 0.3583 |
  | 2.1705        | 32.89  | 198  | 2.0535          |