readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: vc-bantai-vit-withoutAMBI-adunest
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        args: Violation-Classification---Raw-6
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9388646288209607
  ---
  
  <!-- 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. -->
  
  # vc-bantai-vit-withoutAMBI-adunest
  
  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: 0.1950
  - Accuracy: 0.9389
  
  ## 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: 0.0005
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4821        | 0.11  | 100  | 0.7644          | 0.6714   |
  | 0.7032        | 0.23  | 200  | 0.5568          | 0.75     |
  | 0.5262        | 0.34  | 300  | 0.4440          | 0.7806   |
  | 0.4719        | 0.45  | 400  | 0.3893          | 0.8144   |
  | 0.5021        | 0.57  | 500  | 0.5129          | 0.8090   |
  | 0.3123        | 0.68  | 600  | 0.4536          | 0.7980   |
  | 0.3606        | 0.79  | 700  | 0.3679          | 0.8483   |
  | 0.4081        | 0.91  | 800  | 0.3335          | 0.8559   |
  | 0.3624        | 1.02  | 900  | 0.3149          | 0.8592   |
  | 0.1903        | 1.14  | 1000 | 0.3296          | 0.8766   |
  | 0.334         | 1.25  | 1100 | 0.2832          | 0.8897   |
  | 0.2731        | 1.36  | 1200 | 0.2546          | 0.8930   |
  | 0.311         | 1.48  | 1300 | 0.2585          | 0.8908   |
  | 0.3209        | 1.59  | 1400 | 0.2701          | 0.8854   |
  | 0.4005        | 1.7   | 1500 | 0.2643          | 0.8897   |
  | 0.3128        | 1.82  | 1600 | 0.2864          | 0.8843   |
  | 0.3376        | 1.93  | 1700 | 0.2882          | 0.8657   |
  | 0.2698        | 2.04  | 1800 | 0.2876          | 0.9028   |
  | 0.2347        | 2.16  | 1900 | 0.2405          | 0.8974   |
  | 0.2436        | 2.27  | 2000 | 0.2804          | 0.8886   |
  | 0.1764        | 2.38  | 2100 | 0.2852          | 0.8952   |
  | 0.1197        | 2.5   | 2200 | 0.2312          | 0.9127   |
  | 0.1082        | 2.61  | 2300 | 0.2133          | 0.9116   |
  | 0.1245        | 2.72  | 2400 | 0.2677          | 0.8985   |
  | 0.1335        | 2.84  | 2500 | 0.2098          | 0.9181   |
  | 0.2194        | 2.95  | 2600 | 0.1911          | 0.9127   |
  | 0.089         | 3.06  | 2700 | 0.2062          | 0.9181   |
  | 0.0465        | 3.18  | 2800 | 0.2414          | 0.9247   |
  | 0.0985        | 3.29  | 2900 | 0.1869          | 0.9389   |
  | 0.1113        | 3.41  | 3000 | 0.1819          | 0.9323   |
  | 0.1392        | 3.52  | 3100 | 0.2101          | 0.9312   |
  | 0.0621        | 3.63  | 3200 | 0.2201          | 0.9367   |
  | 0.1168        | 3.75  | 3300 | 0.1935          | 0.9389   |
  | 0.059         | 3.86  | 3400 | 0.1946          | 0.9367   |
  | 0.0513        | 3.97  | 3500 | 0.1950          | 0.9389   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  