(num_classes=1000, pretrained='imagenet')
return model
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
    accuracy with 50x fewer parameters and <0.5MB model size"
    <https://arxiv.org/abs/1602.07360>`_ paper.
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
    accuracy with 50x fewer parameters and <0.5MB model size"
    <https://arxiv.org/abs/1602.07360>`_ paper.
r SqueezeNet model architecture from the SqueezeNet : AlexNet - level accuracy with 50x fewer parameters and <0 . 5MB model size <https : // arxiv . org / abs / 1602 . 07360 > _ paper .
def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
    r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
    accuracy with 50x fewer parameters and <0.5MB model size"
    <https://arxiv.org/abs/1602.07360>`_ paper.
    """
    model = models.squeezenet1_0(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['squeezenet1_0'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_squeezenets(model)
    return model
def squeezenet1_0 ( num_classes = 1000 , pretrained = 'imagenet' ) : model = models . squeezenet1_0 ( pretrained = False ) if pretrained is not None : settings = pretrained_settings [ 'squeezenet1_0' ] [ pretrained ] model = load_pretrained ( model , num_classes , settings ) model = modify_squeezenets ( model ) return model