def fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs):
    '\n    Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tunned for mobile use-cases.\n    It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See\n    :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more\n    details.\n\n    Example::\n\n        >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)\n        >>> model.eval()\n        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]\n        >>> predictions = model(x)\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on COCO train2017\n        progress (bool): If True, displays a progress bar of the download to stderr\n        num_classes (int): number of output classes of the model (including the background)\n        pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet\n        trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.\n            Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If ``None`` is\n            passed (the default) this value is set to 3.\n    '
    weights_name = 'fasterrcnn_mobilenet_v3_large_320_fpn_coco'
    defaults = {'min_size': 320, 'max_size': 640, 'rpn_pre_nms_top_n_test': 150, 'rpn_post_nms_top_n_test': 150, 'rpn_score_thresh': 0.05}
    kwargs = {**defaults, **kwargs}
    return _fasterrcnn_mobilenet_v3_large_fpn(weights_name, pretrained=pretrained, progress=progress, num_classes=num_classes, pretrained_backbone=pretrained_backbone, trainable_backbone_layers=trainable_backbone_layers, **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
:func:`~torchvision.models.detection.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)

Args:
    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.
def fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs):
    '\n    Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tunned for mobile use-cases.\n    It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See\n    :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more\n    details.\n\n    Example::\n\n        >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)\n        >>> model.eval()\n        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]\n        >>> predictions = model(x)\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on COCO train2017\n        progress (bool): If True, displays a progress bar of the download to stderr\n        num_classes (int): number of output classes of the model (including the background)\n        pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet\n        trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.\n            Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If ``None`` is\n            passed (the default) this value is set to 3.\n    '
    weights_name = 'fasterrcnn_mobilenet_v3_large_320_fpn_coco'
    defaults = {'min_size': 320, 'max_size': 640, 'rpn_pre_nms_top_n_test': 150, 'rpn_post_nms_top_n_test': 150, 'rpn_score_thresh': 0.05}
    kwargs = {**defaults, **kwargs}
    return _fasterrcnn_mobilenet_v3_large_fpn(weights_name, pretrained=pretrained, progress=progress, num_classes=num_classes, pretrained_backbone=pretrained_backbone, trainable_backbone_layers=trainable_backbone_layers, **kwargs)