ssdlite320_mobilenet_v3_large¶
-
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.Example
>>> 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)
- Parameters
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
: