- torchvision.models.segmentation.lraspp_mobilenet_v3_large(*, weights: Optional[LRASPP_MobileNet_V3_Large_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, **kwargs: Any) LRASPP ¶
Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone from Searching for MobileNetV3 paper.
The segmentation module is in Beta stage, and backward compatibility is not guaranteed.
LRASPP_MobileNet_V3_Large_Weights, optional) – The pretrained weights to use. See
LRASPP_MobileNet_V3_Large_Weightsbelow 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.
MobileNet_V3_Large_Weights, optional) – The pretrained weights for the backbone.
**kwargs – parameters passed to the
torchvision.models.segmentation.LRASPPbase class. Please refer to the source code for more details about this class.
- class torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights(value)¶
The model builder above accepts the following values as the
LRASPP_MobileNet_V3_Large_Weights.DEFAULTis equivalent to
LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1. You can also use strings, e.g.
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
miou (on COCO-val2017-VOC-labels)
pixel_acc (on COCO-val2017-VOC-labels)
__background__, aeroplane, bicycle, … (18 omitted)
The inference transforms are available at
LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1.transformsand perform the following preprocessing operations: Accepts
(B, C, H, W)and single
(C, H, W)image
torch.Tensorobjects. The images are resized to
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].