readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  widget:
    - src: >-
        https://huggingface.co/julenalvaro/cat_vs_dogs_vit_model/blob/main/dog.png
      example_title: dog
    - src: >-
        https://huggingface.co/julenalvaro/cat_vs_dogs_vit_model/blob/main/dog.png
      example_title: cat
    
  model-index:
  - name: cat_vs_dogs_vit_model
    results: []
  ---
  
  <!-- 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. -->
  
  # cat_vs_dogs_vit_model
  
  This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6518
  - Accuracy: 0.6095
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.771         | 0.5   | 500  | 0.6933          | 0.4995   |
  | 0.6967        | 1.0   | 1000 | 0.6882          | 0.556    |
  | 0.695         | 1.5   | 1500 | 0.6728          | 0.581    |
  | 0.6733        | 2.0   | 2000 | 0.6950          | 0.5915   |
  | 0.6768        | 2.5   | 2500 | 0.6694          | 0.5855   |
  | 0.6639        | 3.0   | 3000 | 0.6829          | 0.5795   |
  | 0.652         | 3.5   | 3500 | 0.6637          | 0.5925   |
  | 0.642         | 4.0   | 4000 | 0.6518          | 0.6095   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  