(num_classes=1000, pretrained='imagenet')
return model
r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
    <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
    SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
    than SqueezeNet 1.0, without sacrificing accuracy.
r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
    <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
    SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
    than SqueezeNet 1.0, without sacrificing accuracy.
r SqueezeNet 1 . 1 model from the official SqueezeNet repo <https : // github . com / DeepScale / SqueezeNet / tree / master / SqueezeNet_v1 . 1 > _ . SqueezeNet 1 . 1 has 2 . 4x less computation and slightly fewer parameters than SqueezeNet 1 . 0 without sacrificing accuracy .
def squeezenet1_1(num_classes=1000, pretrained='imagenet'):
    r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
    <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
    SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
    than SqueezeNet 1.0, without sacrificing accuracy.
    """
    model = models.squeezenet1_1(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['squeezenet1_1'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_squeezenets(model)
    return model
def squeezenet1_1 ( num_classes = 1000 , pretrained = 'imagenet' ) : model = models . squeezenet1_1 ( pretrained = False ) if pretrained is not None : settings = pretrained_settings [ 'squeezenet1_1' ] [ pretrained ] model = load_pretrained ( model , num_classes , settings ) model = modify_squeezenets ( model ) return model