torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained: bool = False, progress: bool = True, num_classes: int = 91, pretrained_backbone: bool = False, trainable_backbone_layers: Optional[int] = None, norm_layer: Optional[Callable[[], torch.nn.modules.module.Module]] = None, **kwargs: Any)[source]

Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone, as described at “Searching for MobileNetV3” and “MobileNetV2: Inverted Residuals and Linear Bottlenecks”.

See ssd300_vgg16() for more details.


>>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)]
>>> predictions = model(x)
  • pretrained (bool) – If True, returns a model pre-trained on COCO train2017

  • progress (bool) – If True, displays a progress bar of the download to stderr

  • num_classes (int) – number of output classes of the model (including the background)

  • pretrained_backbone (bool) – If True, returns a model with backbone pre-trained on Imagenet

  • trainable_backbone_layers (int) – number of trainable (not frozen) resnet 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 6.

  • norm_layer (callable, optional) – Module specifying the normalization layer to use.

Examples using ssdlite320_mobilenet_v3_large:


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