zoo – Model Zoo - Pretrained Neural Nets

Residual Neural Networks

Adapted PyTorch’s officially pretrained ResNets on ImageNet.

class neuralnet_pytorch.zoo.resnet.ResNet(block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, default_init=True)[source]
neuralnet_pytorch.zoo.resnet.resnet18(pretrained=False, progress=True, **kwargs)[source]

ResNet-18 model from “Deep Residual Learning for Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.resnet34(pretrained=False, progress=True, **kwargs)[source]

ResNet-34 model from “Deep Residual Learning for Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.resnet50(pretrained=False, progress=True, **kwargs)[source]

ResNet-50 model from “Deep Residual Learning for Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

neuralnet_pytorch.zoo.resnet.resnet101(pretrained=False, progress=True, **kwargs)[source]

ResNet-101 model from “Deep Residual Learning for Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.resnet152(pretrained=False, progress=True, **kwargs)[source]

ResNet-152 model from “Deep Residual Learning for Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.resnext50_32x4d(pretrained=False, progress=True, **kwargs)[source]

ResNeXt-50 32x4d model from “Aggregated Residual Transformation for Deep Neural Networks”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.resnext101_32x8d(pretrained=False, progress=True, **kwargs)[source]

ResNeXt-101 32x8d model from “Aggregated Residual Transformation for Deep Neural Networks”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.wide_resnet50_2(pretrained=False, progress=True, **kwargs)[source]

Wide ResNet-50-2 model from “Wide Residual Networks” The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.resnet.wide_resnet101_2(pretrained=False, progress=True, **kwargs)[source]

Wide ResNet-101-2 model from “Wide Residual Networks” The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.

VGG

Adapted PyTorch’s officially pretrained VGG on ImageNet.

class neuralnet_pytorch.zoo.vgg.VGG(features, num_classes=1000, default_init=True)[source]
neuralnet_pytorch.zoo.vgg.vgg11(pretrained=False, progress=True, **kwargs)[source]

VGG 11-layer model (configuration “A”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg11_bn(pretrained=False, progress=True, **kwargs)[source]

VGG 11-layer model (configuration “A”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg13(pretrained=False, progress=True, **kwargs)[source]

VGG 13-layer model (configuration “B”) “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg13_bn(pretrained=False, progress=True, **kwargs)[source]

VGG 13-layer model (configuration “B”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg16(pretrained=False, progress=True, **kwargs)[source]

VGG 16-layer model (configuration “D”) “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg16_bn(pretrained=False, progress=True, **kwargs)[source]

VGG 16-layer model (configuration “D”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg19_bn(pretrained=False, progress=True, **kwargs)[source]

VGG 19-layer model (configuration ‘E’) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.
neuralnet_pytorch.zoo.vgg.vgg19(pretrained=False, progress=True, **kwargs)[source]

VGG 19-layer model (configuration “E”) “Very Deep Convolutional Networks For Large-Scale Image Recognition”

Parameters:
  • pretrained – If True, returns a model pre-trained on ImageNet.
  • progress – If True, displays a progress bar of the download to stderr.