MobileNet V3

NASNet-A large model architecture.

See http://www.codinghorror.com/blog/archives/001018.html

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

Constructs a Res2Net-50_26w_4s model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a Res2Net-50_14w_8s model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a Res2Net-50_26w_4s model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a Res2Net-50_26w_4s model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a Res2Net-50_26w_4s model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a Res2Net-50_48w_2s model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Construct Res2NeXt-50 4s Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-101 model.

Constructs a ResNet-152 model.

Constructs a ResNet-18 model.

Constructs a ResNet-26 model.

Constructs a ResNet-26 v1d model. This is technically a 28 layer ResNet, sticking with 'd' modifier from Gluon for now.

Constructs a ResNet-34 model.

Constructs a ResNet-50 model.

Constructs a ResNet-50-D model.

Constructs a ResNeXt-101 32x4d model.

Constructs a ResNeXt-101 32x8d model.

Constructs a ResNeXt101-64x4d model.

Constructs a ResNeXt50-32x4d model.

Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample

Constructs a SelecSLS42 model.

Constructs a SelecSLS42_B model.

Constructs a SelecSLS60 model.

Constructs a SelecSLS60_B model.

Constructs a SelecSLS84 model.

Selectable global pooling function with dynamic input kernel size

MNASNet A1 (w/ SE), depth multiplier of 0.5

MNASNet A1 (w/ SE), depth multiplier of 0.75.

MNASNet A1 (w/ SE), depth multiplier of 1.4.

Constructs a SE-ResNeXt-26-D model. This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for combination o

Constructs a SE-ResNet-26-T model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 48, 64 channels i

Constructs a SE-ResNeXt-26-TN model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels

Constructs a Selective Kernel ResNet-18 model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." thi

Constructs a Selective Kernel ResNet-34 model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." thi

Constructs a Select Kernel ResNet-50 model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this

Constructs a Select Kernel ResNet-50-D model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this

Constructs a Select Kernel ResNeXt50-32x4d model. This should be equivalent to the SKNet-50 model in the Select Kernel Paper

Single-Path NAS Pixel1

Constructs a semi-supervised ResNet-18 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning fo

Constructs a semi-supervised ResNet-50 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learning fo

Constructs a semi-supervised ResNeXt-101 32x16 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Lea

Constructs a semi-supervised ResNeXt-101 32x4 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Lear

Constructs a semi-supervised ResNeXt-101 32x8 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Lear

Constructs a semi-supervised ResNeXt-50 32x4 model pre-trained on YFCC100M dataset and finetuned on ImageNet `"Billion-scale Semi-Supervised Learn

Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model

Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model

Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model

Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model an