fasterrcnn_mobilenet_v3_large_320_fpn

torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs)

Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tunned for mobile use-cases. It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See fasterrcnn_resnet50_fpn() for more details.

Example:

>>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), 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 3.