readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: delivery_truck_classification
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9733333333333334
  ---
  
  <!-- 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. -->
  
  # delivery_truck_classification
  
  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1787
  - Accuracy: 0.9733
  
  ## 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: 60
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 0.91  | 5    | 0.1787          | 0.9733   |
  | No log        | 1.91  | 10   | 0.1787          | 0.9733   |
  | No log        | 2.91  | 15   | 0.1787          | 0.9733   |
  | 0.3799        | 3.91  | 20   | 0.1787          | 0.9733   |
  | 0.3799        | 4.91  | 25   | 0.1787          | 0.9733   |
  | 0.3799        | 5.91  | 30   | 0.1787          | 0.9733   |
  | 0.3799        | 6.91  | 35   | 0.1787          | 0.9733   |
  | 0.3648        | 7.91  | 40   | 0.1787          | 0.9733   |
  | 0.3648        | 8.91  | 45   | 0.1787          | 0.9733   |
  | 0.3648        | 9.91  | 50   | 0.1787          | 0.9733   |
  | 0.3648        | 10.91 | 55   | 0.1787          | 0.9733   |
  | 0.3954        | 11.91 | 60   | 0.1787          | 0.9733   |
  | 0.3954        | 12.91 | 65   | 0.1787          | 0.9733   |
  | 0.3954        | 13.91 | 70   | 0.1787          | 0.9733   |
  | 0.3954        | 14.91 | 75   | 0.1787          | 0.9733   |
  | 0.3926        | 15.91 | 80   | 0.1787          | 0.9733   |
  | 0.3926        | 16.91 | 85   | 0.1787          | 0.9733   |
  | 0.3926        | 17.91 | 90   | 0.1787          | 0.9733   |
  | 0.3926        | 18.91 | 95   | 0.1787          | 0.9733   |
  | 0.3801        | 19.91 | 100  | 0.1787          | 0.9733   |
  | 0.3801        | 20.91 | 105  | 0.1787          | 0.9733   |
  | 0.3801        | 21.91 | 110  | 0.1787          | 0.9733   |
  | 0.3801        | 22.91 | 115  | 0.1787          | 0.9733   |
  | 0.3815        | 23.91 | 120  | 0.1787          | 0.9733   |
  | 0.3815        | 24.91 | 125  | 0.1787          | 0.9733   |
  | 0.3815        | 25.91 | 130  | 0.1787          | 0.9733   |
  | 0.3815        | 26.91 | 135  | 0.1787          | 0.9733   |
  | 0.3955        | 27.91 | 140  | 0.1787          | 0.9733   |
  | 0.3955        | 28.91 | 145  | 0.1787          | 0.9733   |
  | 0.3955        | 29.91 | 150  | 0.1787          | 0.9733   |
  | 0.3955        | 30.91 | 155  | 0.1787          | 0.9733   |
  | 0.3854        | 31.91 | 160  | 0.1787          | 0.9733   |
  | 0.3854        | 32.91 | 165  | 0.1787          | 0.9733   |
  | 0.3854        | 33.91 | 170  | 0.1787          | 0.9733   |
  | 0.3854        | 34.91 | 175  | 0.1787          | 0.9733   |
  | 0.3949        | 35.91 | 180  | 0.1787          | 0.9733   |
  | 0.3949        | 36.91 | 185  | 0.1787          | 0.9733   |
  | 0.3949        | 37.91 | 190  | 0.1787          | 0.9733   |
  | 0.3949        | 38.91 | 195  | 0.1787          | 0.9733   |
  | 0.423         | 39.91 | 200  | 0.1787          | 0.9733   |
  | 0.423         | 40.91 | 205  | 0.1787          | 0.9733   |
  | 0.423         | 41.91 | 210  | 0.1787          | 0.9733   |
  | 0.423         | 42.91 | 215  | 0.1787          | 0.9733   |
  | 0.3761        | 43.91 | 220  | 0.1787          | 0.9733   |
  | 0.3761        | 44.91 | 225  | 0.1787          | 0.9733   |
  | 0.3761        | 45.91 | 230  | 0.1787          | 0.9733   |
  | 0.3761        | 46.91 | 235  | 0.1787          | 0.9733   |
  | 0.3673        | 47.91 | 240  | 0.1787          | 0.9733   |
  | 0.3673        | 48.91 | 245  | 0.1787          | 0.9733   |
  | 0.3673        | 49.91 | 250  | 0.1787          | 0.9733   |
  | 0.3673        | 50.91 | 255  | 0.1787          | 0.9733   |
  | 0.3639        | 51.91 | 260  | 0.1787          | 0.9733   |
  | 0.3639        | 52.91 | 265  | 0.1787          | 0.9733   |
  | 0.3639        | 53.91 | 270  | 0.1787          | 0.9733   |
  | 0.3639        | 54