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deeplabv3_resnet101

torchvision.models.segmentation.deeplabv3_resnet101(*, weights: Optional[torchvision.models.segmentation.deeplabv3.DeepLabV3_ResNet101_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, aux_loss: Optional[bool] = None, weights_backbone: Optional[torchvision.models.resnet.ResNet101_Weights] = ResNet101_Weights.IMAGENET1K_V1, **kwargs: Any)torchvision.models.segmentation.deeplabv3.DeepLabV3[source]

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)[source]

The model builder above accepts the following values as the weights parameter. DeepLabV3_ResNet101_Weights.DEFAULT is equivalent to DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1. You can also use strings, e.g. weights='DEFAULT' or weights='COCO_WITH_VOC_LABELS_V1'.

DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1:

These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. Also available as DeepLabV3_ResNet101_Weights.DEFAULT.

miou (on COCO-val2017-VOC-labels)

67.4

pixel_acc (on COCO-val2017-VOC-labels)

92.4

categories

__background__, aeroplane, bicycle, … (18 omitted)

min_size

height=1, width=1

num_params

60996202

recipe

link

The inference transforms are available at DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are resized to resize_size=[520] using interpolation=InterpolationMode.BILINEAR. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].

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