r"""Inception v3 model architecture from `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

r"""NASNetALarge model architecture from the `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.

r"""NASNetALarge model architecture from the `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.

r"""PNASNet-5 model architecture from the `"Progressive Neural Architecture Search" <https://arxiv.org/abs/1712.00559>`_ paper.

PolyNet architecture from the paper 'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks' https://arxiv.org/abs/1611.05725

Constructs a ResNet-101 model.

Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-152 model.

Constructs a ResNet-18 model.

Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-34 model.

Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-50 model.

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxi

r"""SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. SqueezeNe

Returns the model's average precision for each class Return: ap (FloatTensor): 1xK tensor, with avg precision for each class k

VGG 11-layer model (configuration "A")

VGG 11-layer model (configuration "A") with batch normalization

VGG 13-layer model (configuration "B")

VGG 13-layer model (configuration "B") with batch normalization

VGG 16-layer model (configuration "D")

VGG 16-layer model (configuration "D") with batch normalization

VGG 19-layer model (configuration "E")

VGG 19-layer model (configuration 'E') with batch normalization