hunk.code_window: @pytest.mark.parametrize('batch_size', [2])
   def test_model_backward(model_name, batch_size):
       """Run a single forward pass with each model"""
       model = create_model(model_name, pretrained=False, num_classes=42)
       num_params = sum([x.numel() for x in model.parameters()])
       model.train()
  
hunk.labels: keep keep add keep keep keep keep
hunk.after_edit:     input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
       if max(input_size) > MAX_BWD_SIZE:
           pytest.skip("Fixed input size model > limit.")
  
hunk.file_path: tests/test_models.py
hunk.type: add
file: # CSP-DarkNet
  
  **CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. 
  
  This CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).
  
  {% include 'code_snippets.md' %}
  
  ## How do I train this model?
  
  You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
  
  ## Citation
  
  ```BibTeX
  @misc{bochkovskiy2020yolov4,
        title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, 
        author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
        year={2020},
        eprint={2004.10934},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: CSP DarkNet
    Paper:
      Title: 'YOLOv4: Optimal Speed and Accuracy of Object Detection'
      URL: https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
  Models:
  - Name: cspdarknet53
    In Collection: CSP DarkNet
    Metadata:
      FLOPs: 8545018880
      Parameters: 27640000
      File Size: 110775135
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Mish
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - CutMix
      - Label Smoothing
      - Mosaic
      - Polynomial Learning Rate Decay
      - SGD with Momentum
      - Self-Adversarial Training
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 1x NVIDIA RTX 2070 GPU
      ID: cspdarknet53
      LR: 0.1
      Layers: 53
      Crop Pct: '0.887'
      Momentum: 0.9
      Batch Size: 128
      Image Size: '256'
      Warmup Steps: 1000
      Weight Decay: 0.0005
      Interpolation: bilinear
      Training Steps: 8000000
      FPS (GPU RTX 2070): 66
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/cspnet.py#L441
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.05%
        Top 5 Accuracy: 95.09%
  -->
  