readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: vit-base-patch16-224-in21k_male_or_female_eyes
    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: 0.9726681127982646
      - name: F1
        type: f1
        value: 0.9741273100616017
      - name: Recall
        type: recall
        value: 0.9665851670741646
      - name: Precision
        type: precision
        value: 0.9817880794701986
  ---
  
  <!-- 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. -->
  
  # vit-base-patch16-224-in21k_male_or_female_eyes
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0810
  - Accuracy: 0.9727
  - F1: 0.9741
  - Recall: 0.9666
  - Precision: 0.9818
  
  ## 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: 0.0002
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.1998        | 1.0   | 577  | 0.2365          | 0.9072   | 0.9196 | 0.9976 | 0.8530    |
  | 0.0846        | 2.0   | 1154 | 0.0810          | 0.9727   | 0.9741 | 0.9666 | 0.9818    |
  | 0.0309        | 3.0   | 1731 | 0.0852          | 0.9809   | 0.9821 | 0.9837 | 0.9805    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.8.0
  - Tokenizers 0.12.1
  