torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(*, weights: Optional[FasterRCNN_MobileNet_V3_Large_FPN_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, trainable_backbone_layers: Optional[int] = None, **kwargs: Any) FasterRCNN[source]

Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone.


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

It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See fasterrcnn_resnet50_fpn() for more details.


>>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=FasterRCNN_MobileNet_V3_Large_FPN_Weights.DEFAULT)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
  • weights (FasterRCNN_MobileNet_V3_Large_FPN_Weights, optional) – The pretrained weights to use. See FasterRCNN_MobileNet_V3_Large_FPN_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)

  • weights_backbone (MobileNet_V3_Large_Weights, optional) – The pretrained weights for the backbone.

  • trainable_backbone_layers (int, optional) – number of trainable (not frozen) layers starting from final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If None is passed (the default) this value is set to 3.

  • **kwargs – parameters passed to the torchvision.models.detection.faster_rcnn.FasterRCNN base class. Please refer to the source code for more details about this class.

class torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights(value)[source]

An enumeration.


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