readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: google-vit-base-patch16-224-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: 0.9004629629629629
      - name: Precision
        type: precision
        value: 0.9066341895316832
      - name: Recall
        type: recall
        value: 0.9004629629629629
      - name: F1
        type: f1
        value: 0.8984296743444529
  ---
  
  <!-- 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. -->
  
  # google-vit-base-patch16-224-cartoon-face-recognition
  
  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3707
  - Accuracy: 0.9005
  - Precision: 0.9066
  - Recall: 0.9005
  - F1: 0.8984
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 0.89  | 6    | 0.5459          | 0.8611   | 0.8683    | 0.8611 | 0.8577 |
  | 0.0812        | 1.89  | 12   | 0.4703          | 0.8796   | 0.8833    | 0.8796 | 0.8764 |
  | 0.0812        | 2.89  | 18   | 0.4430          | 0.8935   | 0.8969    | 0.8935 | 0.8906 |
  | 0.0307        | 3.89  | 24   | 0.4045          | 0.8819   | 0.8849    | 0.8819 | 0.8767 |
  | 0.0091        | 4.89  | 30   | 0.3672          | 0.9005   | 0.9025    | 0.9005 | 0.8980 |
  | 0.0091        | 5.89  | 36   | 0.3841          | 0.9028   | 0.9125    | 0.9028 | 0.9011 |
  | 0.0043        | 6.89  | 42   | 0.3926          | 0.9005   | 0.9073    | 0.9005 | 0.8972 |
  | 0.0043        | 7.89  | 48   | 0.3786          | 0.8958   | 0.9005    | 0.8958 | 0.8931 |
  | 0.0031        | 8.89  | 54   | 0.3791          | 0.9028   | 0.9091    | 0.9028 | 0.9007 |
  | 0.002         | 9.89  | 60   | 0.3677          | 0.9028   | 0.9106    | 0.9028 | 0.9001 |
  | 0.002         | 10.89 | 66   | 0.3740          | 0.9028   | 0.9099    | 0.9028 | 0.9007 |
  | 0.0027        | 11.89 | 72   | 0.3869          | 0.8981   | 0.9043    | 0.8981 | 0.8956 |
  | 0.0027        | 12.89 | 78   | 0.3801          | 0.8981   | 0.9021    | 0.8981 | 0.8954 |
  | 0.004         | 13.89 | 84   | 0.3674          | 0.9051   | 0.9113    | 0.9051 | 0.9028 |
  | 0.0024        | 14.89 | 90   | 0.3620          | 0.9051   | 0.9096    | 0.9051 | 0.9027 |
  | 0.0024        | 15.89 | 96   | 0.3670          | 0.9028   | 0.9089    | 0.9028 | 0.9006 |
  | 0.0021        | 16.89 | 102  | 0.3827          | 0.9005   | 0.9065    | 0.9005 | 0.8980 |
  | 0.0021        | 17.89 | 108  | 0.3748          | 0.8981   | 0.9049    | 0.8981 | 0.8958 |
  | 0.0022        | 18.89 | 114  | 0.3825          | 0.9028   | 0.9101    | 0.9028 | 0.9006 |
  | 0.0019        | 19.89 | 120  | 0.3707          | 0.9005   | 0.9066    | 0.9005 | 0.8984 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  