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)