readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - image_folder
  metrics:
  - accuracy
  model-index:
  - name: violation-classification-bantai-vit-v80ep
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: image_folder
        type: image_folder
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9559725730783111
  ---
  
  <!-- 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. -->
  
  # violation-classification-bantai-vit-v80ep
  
  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 image_folder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1974
  - Accuracy: 0.9560
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 80
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.797         | 4.95  | 500  | 0.3926          | 0.8715   |
  | 0.3095        | 9.9   | 1000 | 0.2597          | 0.9107   |
  | 0.1726        | 14.85 | 1500 | 0.2157          | 0.9253   |
  | 0.1259        | 19.8  | 2000 | 0.1870          | 0.9392   |
  | 0.0959        | 24.75 | 2500 | 0.1797          | 0.9444   |
  | 0.0835        | 29.7  | 3000 | 0.2293          | 0.9354   |
  | 0.0722        | 34.65 | 3500 | 0.1921          | 0.9441   |
  | 0.0628        | 39.6  | 4000 | 0.1897          | 0.9491   |
  | 0.059         | 44.55 | 4500 | 0.1719          | 0.9520   |
  | 0.0531        | 49.5  | 5000 | 0.1987          | 0.9513   |
  | 0.046         | 54.45 | 5500 | 0.1713          | 0.9556   |
  | 0.0444        | 59.4  | 6000 | 0.2016          | 0.9525   |
  | 0.042         | 64.36 | 6500 | 0.1950          | 0.9525   |
  | 0.0363        | 69.31 | 7000 | 0.2017          | 0.9549   |
  | 0.037         | 74.26 | 7500 | 0.1943          | 0.9551   |
  | 0.0343        | 79.21 | 8000 | 0.1974          | 0.9560   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  