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# wide_resnet50_2

torchvision.models.wide_resnet50_2(*, weights: Optional[Wide_ResNet50_2_Weights] = None, progress: bool = True, **kwargs: Any) → ResNet

  Wide ResNet-50-2 model from Wide Residual Networks.

  The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048.

  Parameters:

    - weights (`Wide_ResNet50_2_Weights`, optional) – The pretrained weights to use. See `Wide_ResNet50_2_Weights` below for more details, and possible values. By default, no pre-trained weights are used.

    - progress (bool, optional) – If True, displays a progress bar of the download to stderr. Default is True.

    - **kwargs – parameters passed to the `torchvision.models.resnet.ResNet` base class. Please refer to the source code for more details about this class.

  class torchvision.models.Wide_ResNet50_2_Weights(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

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