readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  datasets:
  - cats_vs_dogs
  metrics:
  - accuracy
  model-index:
  - name: vit-base-cats-vs-dogs
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cats_vs_dogs
        type: cats_vs_dogs
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9883257403189066
  ---
  
  <!-- 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-cats-vs-dogs
  
  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 cats_vs_dogs dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0369
  - Accuracy: 0.9883
  
  ## how to use
  ```python
  from transformers import ViTFeatureExtractor, ViTModel
  from PIL import Image
  import requests
  
  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
  model = ViTModel.from_pretrained('akahana/vit-base-cats-vs-dogs')
  inputs = feature_extractor(images=image, return_tensors="pt")
  
  outputs = model(**inputs)
  last_hidden_states = outputs.last_hidden_state
  ```
  
  ## 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: 1337
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.0949        | 1.0   | 2488 | 0.0369          | 0.9883   |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.10.0+cu111
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  