readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: ViTFineTuned
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: KTH-TIPS2-b
        type: images
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 1.0
  ---
  
  <!-- 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. -->
  
  # ViTFineTuned
  
  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 KTH-TIPS2-b dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0075
  - Accuracy: 1.0
  
  ## Model description
  
  Transfer learning by fine tuning the Vision Transformer by Google on KTP-TIP2-b dataset.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0005
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.2859        | 0.99  | 67   | 0.2180          | 0.9784   |
  | 0.293         | 1.99  | 134  | 0.3308          | 0.9185   |
  | 0.1444        | 2.99  | 201  | 0.1532          | 0.9568   |
  | 0.0833        | 3.99  | 268  | 0.0515          | 0.9856   |
  | 0.1007        | 4.99  | 335  | 0.0295          | 0.9904   |
  | 0.0372        | 5.99  | 402  | 0.0574          | 0.9808   |
  | 0.0919        | 6.99  | 469  | 0.0537          | 0.9880   |
  | 0.0135        | 7.99  | 536  | 0.0117          | 0.9952   |
  | 0.0472        | 8.99  | 603  | 0.0075          | 1.0      |
  | 0.0151        | 9.99  | 670  | 0.0048          | 1.0      |
  | 0.0052        | 10.99 | 737  | 0.0073          | 0.9976   |
  | 0.0109        | 11.99 | 804  | 0.0198          | 0.9952   |
  | 0.0033        | 12.99 | 871  | 0.0066          | 0.9976   |
  | 0.011         | 13.99 | 938  | 0.0067          | 0.9976   |
  | 0.0032        | 14.99 | 1005 | 0.0060          | 0.9976   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  