readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - image_folder
  metrics:
  - accuracy
  model-index:
  - name: violation-classification-bantai-vit-v100ep
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: image_folder
        type: image_folder
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9157343919162757
  ---
  
  <!-- 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-v100ep
  
  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: 0.2557
  - Accuracy: 0.9157
  
  ## 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: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.2811        | 1.0   | 101  | 0.2855          | 0.9027   |
  | 0.2382        | 2.0   | 202  | 0.2763          | 0.9085   |
  | 0.2361        | 3.0   | 303  | 0.2605          | 0.9109   |
  | 0.196         | 4.0   | 404  | 0.2652          | 0.9110   |
  | 0.1395        | 5.0   | 505  | 0.2648          | 0.9134   |
  | 0.155         | 6.0   | 606  | 0.2656          | 0.9152   |
  | 0.1422        | 7.0   | 707  | 0.2607          | 0.9141   |
  | 0.1511        | 8.0   | 808  | 0.2557          | 0.9157   |
  | 0.1938        | 9.0   | 909  | 0.2679          | 0.9049   |
  | 0.2094        | 10.0  | 1010 | 0.2392          | 0.9137   |
  | 0.1835        | 11.0  | 1111 | 0.2400          | 0.9156   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  