readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: google-vit-base-patch16-224-face
    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: 0.7248574809078198
      - name: Precision
        type: precision
        value: 0.717172031675939
      - name: Recall
        type: recall
        value: 0.7248574809078198
      - name: F1
        type: f1
        value: 0.7195690317790054
  ---
  
  <!-- 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-face
  
  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: 1.4531
  - Accuracy: 0.7249
  - Precision: 0.7172
  - Recall: 0.7249
  - F1: 0.7196
  
  ## 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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.8514        | 1.0   | 290  | 0.8464          | 0.7048   | 0.7035    | 0.7048 | 0.6909 |
  | 0.7202        | 2.0   | 580  | 0.7791          | 0.7283   | 0.7297    | 0.7283 | 0.7111 |
  | 0.5455        | 3.0   | 870  | 0.7950          | 0.7285   | 0.7174    | 0.7285 | 0.7171 |
  | 0.334         | 4.0   | 1160 | 0.8948          | 0.7155   | 0.7152    | 0.7155 | 0.7145 |
  | 0.1644        | 5.0   | 1450 | 1.0820          | 0.7239   | 0.7189    | 0.7239 | 0.7194 |
  | 0.0482        | 6.0   | 1740 | 1.2792          | 0.7204   | 0.7144    | 0.7204 | 0.7160 |
  | 0.0236        | 7.0   | 2030 | 1.4162          | 0.7279   | 0.7195    | 0.7279 | 0.7209 |
  | 0.0049        | 8.0   | 2320 | 1.4531          | 0.7249   | 0.7172    | 0.7249 | 0.7196 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  