Table of Contents
Shortcuts

# deeplabv3_resnet101

torchvision.models.segmentation.deeplabv3_resnet101(*, weights: Optional[DeepLabV3_ResNet101_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, aux_loss: Optional[bool] = None, weights_backbone: Optional[ResNet101_Weights] = ResNet101_Weights.IMAGENET1K_V1, **kwargs: Any) → DeepLabV3

  Constructs a DeepLabV3 model with a ResNet-101 backbone.

  Warning

  The segmentation module is in Beta stage, and backward compatibility is not guaranteed.

  Reference: Rethinking Atrous Convolution for Semantic Image Segmentation.

  Parameters:

    - weights (`DeepLabV3_ResNet101_Weights`, optional) – The pretrained weights to use. See `DeepLabV3_ResNet101_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.

    - num_classes (int, optional) – number of output classes of the model (including the background)

    - aux_loss (bool, optional) – If True, it uses an auxiliary loss

    - weights_backbone (`ResNet101_Weights`, optional) – The pretrained weights for the backbone

    - **kwargs – unused

  class torchvision.models.segmentation.DeepLabV3_ResNet101_Weights(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.