def detr_resnet101_panoptic(pretrained=False, num_classes=250, threshold=0.85, return_postprocessor=False):
    '\n    DETR-DC5 R101 with 6 encoder and 6 decoder layers.\n\n    Achieves 45.1 PQ on COCO val5k.\n\n   threshold is the minimum confidence required for keeping segments in the prediction\n    '
    model = _make_detr('resnet101', dilation=False, num_classes=num_classes, mask=True)
    is_thing_map = {i: (i <= 90) for i in range(250)}
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(url='https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth', map_location='cpu', check_hash=True)
        model.load_state_dict(checkpoint['model'])
    if return_postprocessor:
        return (model, PostProcessPanoptic(is_thing_map, threshold=threshold))
    return model
DETR-DC5 R101 with 6 encoder and 6 decoder layers.

 Achieves 45.1 PQ on COCO val5k.

threshold is the minimum confidence required for keeping segments in the prediction
def detr_resnet101_panoptic(pretrained=False, num_classes=250, threshold=0.85, return_postprocessor=False):
    '\n    DETR-DC5 R101 with 6 encoder and 6 decoder layers.\n\n    Achieves 45.1 PQ on COCO val5k.\n\n   threshold is the minimum confidence required for keeping segments in the prediction\n    '
    model = _make_detr('resnet101', dilation=False, num_classes=num_classes, mask=True)
    is_thing_map = {i: (i <= 90) for i in range(250)}
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(url='https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth', map_location='cpu', check_hash=True)
        model.load_state_dict(checkpoint['model'])
    if return_postprocessor:
        return (model, PostProcessPanoptic(is_thing_map, threshold=threshold))
    return model