readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: vit-base-patch16-224-in21k_dog_vs_cat_image_classification
    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: 0.99
      - name: F1
        type: f1
        value: 0.9897161661867544
      - name: Recall
        type: recall
        value: 0.9909390444810544
      - name: Precision
        type: precision
        value: 0.9884963023829088
  ---
  
  <!-- 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_dog_vs_cat_image_classification
  
  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: 0.0404
  - Accuracy: 0.99
  - F1: 0.9897
  - Recall: 0.9909
  - Precision: 0.9885
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.0896        | 1.0   | 1250 | 0.0590          | 0.979    | 0.9783 | 0.9728 | 0.9838    |
  | 0.0253        | 2.0   | 2500 | 0.0543          | 0.9842   | 0.9837 | 0.9802 | 0.9871    |
  | 0.0066        | 3.0   | 3750 | 0.0404          | 0.99     | 0.9897 | 0.9909 | 0.9885    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.8.0
  - Tokenizers 0.12.1
  