hunk.code_window:     model = create_model(model_name, pretrained=False, num_classes=42)
       num_params = sum([x.numel() for x in model.parameters()])
       model.train()
       input_size = _get_input_size(model, TARGET_BWD_SIZE)
       if max(input_size) > MAX_BWD_SIZE:
           pytest.skip("Fixed input size model > limit.")
       inputs = torch.randn((batch_size, *input_size))
       outputs = model(inputs)
       if isinstance(outputs, tuple):
  
hunk.labels: keep keep keep keep replace replace replace replace keep keep keep
hunk.file_path: tests/test_models.py
hunk.type: replace
file: # Inception v4
  
  **Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
  {% 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{szegedy2016inceptionv4,
        title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning}, 
        author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
        year={2016},
        eprint={1602.07261},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: Inception v4
    Paper:
      Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
        Learning
      URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
  Models:
  - Name: inception_v4
    In Collection: Inception v4
    Metadata:
      FLOPs: 15806527936
      Parameters: 42680000
      File Size: 171082495
      Architecture:
      - Average Pooling
      - Dropout
      - Inception-A
      - Inception-B
      - Inception-C
      - Reduction-A
      - Reduction-B
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - Label Smoothing
      - RMSProp
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 20x NVIDIA Kepler GPUs
      ID: inception_v4
      LR: 0.045
      Dropout: 0.2
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '299'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 1.01%
        Top 5 Accuracy: 16.85%
  -->
  