# gluon.model_zoo.vision¶

Module for pre-defined neural network models.

This module contains definitions for the following model architectures: - AlexNet - DenseNet - Inception V3 - ResNet V1 - ResNet V2 - SqueezeNet - VGG - MobileNet - MobileNetV2

You can construct a model with random weights by calling its constructor:

from mxnet.gluon.model_zoo import vision
resnet18 = vision.resnet18_v1()
alexnet = vision.alexnet()
squeezenet = vision.squeezenet1_0()
densenet = vision.densenet_161()


We provide pre-trained models for all the listed models. These models can constructed by passing pretrained=True:

from mxnet.gluon.model_zoo import vision
resnet18 = vision.resnet18_v1(pretrained=True)
alexnet = vision.alexnet(pretrained=True)


All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferrably happen at preprocessing. You can use mx.image.color_normalize for such transformation:

image = image/255
normalized = mx.image.color_normalize(image,
mean=mx.nd.array([0.485, 0.456, 0.406]),
std=mx.nd.array([0.229, 0.224, 0.225]))

mxnet.gluon.model_zoo.vision.get_model(name, **kwargs)[source]

Returns a pre-defined model by name

Parameters
• name (str) – Name of the model.

• pretrained (bool) – Whether to load the pretrained weights for model.

• classes (int) – Number of classes for the output layer.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. Returns The model. Return type gluon.HybridBlock  get_model(name, **kwargs) Returns a pre-defined model by name ## ResNet¶  resnet18_v1(**kwargs) ResNet-18 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet34_v1(**kwargs) ResNet-34 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet50_v1(**kwargs) ResNet-50 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet101_v1(**kwargs) ResNet-101 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet152_v1(**kwargs) ResNet-152 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet18_v2(**kwargs) ResNet-18 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet34_v2(**kwargs) ResNet-34 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet50_v2(**kwargs) ResNet-50 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet101_v2(**kwargs) ResNet-101 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet152_v2(**kwargs) ResNet-152 V2 model from “Identity Mappings in Deep Residual Networks” paper.  ResNetV1(block, layers, channels[, classes, …]) ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. ResNetV2(block, layers, channels[, classes, …]) ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper. BasicBlockV1(channels, stride[, downsample, …]) BasicBlock V1 from “Deep Residual Learning for Image Recognition” paper.This is used for ResNet V1 for 18, 34 layers.. BasicBlockV2(channels, stride[, downsample, …]) BasicBlock V2 from “Identity Mappings in Deep Residual Networks” paper.This is used for ResNet V2 for 18, 34 layers.. BottleneckV1(channels, stride[, downsample, …]) Bottleneck V1 from “Deep Residual Learning for Image Recognition” paper.This is used for ResNet V1 for 50, 101, 152 layers.. BottleneckV2(channels, stride[, downsample, …]) Bottleneck V2 from “Identity Mappings in Deep Residual Networks” paper.This is used for ResNet V2 for 50, 101, 152 layers.. get_resnet(version, num_layers[, …]) ResNet V1 model from “Deep Residual Learning for Image Recognition” paper.ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper.. ## VGG¶  vgg11(**kwargs) VGG-11 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg13(**kwargs) VGG-13 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg16(**kwargs) VGG-16 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg19(**kwargs) VGG-19 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg11_bn(**kwargs) VGG-11 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg13_bn(**kwargs) VGG-13 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg16_bn(**kwargs) VGG-16 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg19_bn(**kwargs) VGG-19 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper.  VGG(layers, filters[, classes, batch_norm]) VGG model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. get_vgg(num_layers[, pretrained, ctx, root]) VGG model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. ## Alexnet¶  alexnet([pretrained, ctx, root]) AlexNet model from the “One weird trick…” paper.  AlexNet([classes]) AlexNet model from the “One weird trick…” paper. ## DenseNet¶  densenet121(**kwargs) Densenet-BC 121-layer model from the “Densely Connected Convolutional Networks” paper. densenet161(**kwargs) Densenet-BC 161-layer model from the “Densely Connected Convolutional Networks” paper. densenet169(**kwargs) Densenet-BC 169-layer model from the “Densely Connected Convolutional Networks” paper. densenet201(**kwargs) Densenet-BC 201-layer model from the “Densely Connected Convolutional Networks” paper.  DenseNet(num_init_features, growth_rate, …) Densenet-BC model from the “Densely Connected Convolutional Networks” paper. ## SqueezeNet¶  squeezenet1_0(**kwargs) SqueezeNet 1.0 model from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper. squeezenet1_1(**kwargs) SqueezeNet 1.1 model from the official SqueezeNet repo.SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy..  SqueezeNet(version[, classes]) SqueezeNet model from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper.SqueezeNet 1.1 model from the official SqueezeNet repo.SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy.. ## Inception¶  inception_v3([pretrained, ctx, root]) Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper.  Inception3([classes]) Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. ## MobileNet¶  mobilenet1_0(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 1.0. mobilenet0_75(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.75. mobilenet0_5(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.5. mobilenet0_25(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.25. mobilenet_v2_1_0(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. mobilenet_v2_0_75(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. mobilenet_v2_0_5(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. mobilenet_v2_0_25(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper.  MobileNet([multiplier, classes]) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper. MobileNetV2([multiplier, classes]) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. ## API Reference¶ Module for pre-defined neural network models. This module contains definitions for the following model architectures: - AlexNet - DenseNet - Inception V3 - ResNet V1 - ResNet V2 - SqueezeNet - VGG - MobileNet - MobileNetV2 You can construct a model with random weights by calling its constructor: from mxnet.gluon.model_zoo import vision resnet18 = vision.resnet18_v1() alexnet = vision.alexnet() squeezenet = vision.squeezenet1_0() densenet = vision.densenet_161()  We provide pre-trained models for all the listed models. These models can constructed by passing pretrained=True: from mxnet.gluon.model_zoo import vision resnet18 = vision.resnet18_v1(pretrained=True) alexnet = vision.alexnet(pretrained=True)  All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferrably happen at preprocessing. You can use mx.image.color_normalize for such transformation: image = image/255 normalized = mx.image.color_normalize(image, mean=mx.nd.array([0.485, 0.456, 0.406]), std=mx.nd.array([0.229, 0.224, 0.225]))  Classes  AlexNet([classes]) AlexNet model from the “One weird trick…” paper. BasicBlockV1(channels, stride[, downsample, …]) BasicBlock V1 from “Deep Residual Learning for Image Recognition” paper. BasicBlockV2(channels, stride[, downsample, …]) BasicBlock V2 from “Identity Mappings in Deep Residual Networks” paper. BottleneckV1(channels, stride[, downsample, …]) Bottleneck V1 from “Deep Residual Learning for Image Recognition” paper. BottleneckV2(channels, stride[, downsample, …]) Bottleneck V2 from “Identity Mappings in Deep Residual Networks” paper. DenseNet(num_init_features, growth_rate, …) Densenet-BC model from the “Densely Connected Convolutional Networks” paper. Inception3([classes]) Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. MobileNet([multiplier, classes]) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper. MobileNetV2([multiplier, classes]) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. ResNetV1(block, layers, channels[, classes, …]) ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. ResNetV2(block, layers, channels[, classes, …]) ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper. SqueezeNet(version[, classes]) SqueezeNet model from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper. VGG(layers, filters[, classes, batch_norm]) VGG model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. Functions  alexnet([pretrained, ctx, root]) AlexNet model from the “One weird trick…” paper. densenet121(**kwargs) Densenet-BC 121-layer model from the “Densely Connected Convolutional Networks” paper. densenet161(**kwargs) Densenet-BC 161-layer model from the “Densely Connected Convolutional Networks” paper. densenet169(**kwargs) Densenet-BC 169-layer model from the “Densely Connected Convolutional Networks” paper. densenet201(**kwargs) Densenet-BC 201-layer model from the “Densely Connected Convolutional Networks” paper. get_mobilenet(multiplier[, pretrained, ctx, …]) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper. get_mobilenet_v2(multiplier[, pretrained, …]) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. get_model(name, **kwargs) Returns a pre-defined model by name get_resnet(version, num_layers[, …]) ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. get_vgg(num_layers[, pretrained, ctx, root]) VGG model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. inception_v3([pretrained, ctx, root]) Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. mobilenet0_25(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.25. mobilenet0_5(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.5. mobilenet0_75(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.75. mobilenet1_0(**kwargs) MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 1.0. mobilenet_v2_0_25(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. mobilenet_v2_0_5(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. mobilenet_v2_0_75(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. mobilenet_v2_1_0(**kwargs) MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. resnet101_v1(**kwargs) ResNet-101 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet101_v2(**kwargs) ResNet-101 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet152_v1(**kwargs) ResNet-152 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet152_v2(**kwargs) ResNet-152 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet18_v1(**kwargs) ResNet-18 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet18_v2(**kwargs) ResNet-18 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet34_v1(**kwargs) ResNet-34 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet34_v2(**kwargs) ResNet-34 V2 model from “Identity Mappings in Deep Residual Networks” paper. resnet50_v1(**kwargs) ResNet-50 V1 model from “Deep Residual Learning for Image Recognition” paper. resnet50_v2(**kwargs) ResNet-50 V2 model from “Identity Mappings in Deep Residual Networks” paper. squeezenet1_0(**kwargs) SqueezeNet 1.0 model from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper. squeezenet1_1(**kwargs) SqueezeNet 1.1 model from the official SqueezeNet repo. vgg11(**kwargs) VGG-11 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg11_bn(**kwargs) VGG-11 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg13(**kwargs) VGG-13 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg13_bn(**kwargs) VGG-13 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg16(**kwargs) VGG-16 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg16_bn(**kwargs) VGG-16 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg19(**kwargs) VGG-19 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. vgg19_bn(**kwargs) VGG-19 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. class mxnet.gluon.model_zoo.vision.AlexNet(classes=1000, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. AlexNet model from the “One weird trick…” paper. Parameters classes (int, default 1000) – Number of classes for the output layer. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.BasicBlockV1(channels, stride, downsample=False, in_channels=0, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. BasicBlock V1 from “Deep Residual Learning for Image Recognition” paper. This is used for ResNet V1 for 18, 34 layers. Parameters • channels (int) – Number of output channels. • stride (int) – Stride size. • downsample (bool, default False) – Whether to downsample the input. • in_channels (int, default 0) – Number of input channels. Default is 0, to infer from the graph. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.BasicBlockV2(channels, stride, downsample=False, in_channels=0, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. BasicBlock V2 from “Identity Mappings in Deep Residual Networks” paper. This is used for ResNet V2 for 18, 34 layers. Parameters • channels (int) – Number of output channels. • stride (int) – Stride size. • downsample (bool, default False) – Whether to downsample the input. • in_channels (int, default 0) – Number of input channels. Default is 0, to infer from the graph. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.BottleneckV1(channels, stride, downsample=False, in_channels=0, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. Bottleneck V1 from “Deep Residual Learning for Image Recognition” paper. This is used for ResNet V1 for 50, 101, 152 layers. Parameters • channels (int) – Number of output channels. • stride (int) – Stride size. • downsample (bool, default False) – Whether to downsample the input. • in_channels (int, default 0) – Number of input channels. Default is 0, to infer from the graph. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.BottleneckV2(channels, stride, downsample=False, in_channels=0, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. Bottleneck V2 from “Identity Mappings in Deep Residual Networks” paper. This is used for ResNet V2 for 50, 101, 152 layers. Parameters • channels (int) – Number of output channels. • stride (int) – Stride size. • downsample (bool, default False) – Whether to downsample the input. • in_channels (int, default 0) – Number of input channels. Default is 0, to infer from the graph. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.DenseNet(num_init_features, growth_rate, block_config, bn_size=4, dropout=0, classes=1000, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. Densenet-BC model from the “Densely Connected Convolutional Networks” paper. Parameters • num_init_features (int) – Number of filters to learn in the first convolution layer. • growth_rate (int) – Number of filters to add each layer (k in the paper). • block_config (list of int) – List of integers for numbers of layers in each pooling block. • bn_size (int, default 4) – Multiplicative factor for number of bottle neck layers. (i.e. bn_size * k features in the bottleneck layer) • dropout (float, default 0) – Rate of dropout after each dense layer. • classes (int, default 1000) – Number of classification classes. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.Inception3(classes=1000, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. Parameters classes (int, default 1000) – Number of classification classes. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.MobileNet(multiplier=1.0, classes=1000, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper. Parameters • multiplier (float, default 1.0) – The width multiplier for controling the model size. Only multipliers that are no less than 0.25 are supported. The actual number of channels is equal to the original channel size multiplied by this multiplier. • classes (int, default 1000) – Number of classes for the output layer. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.MobileNetV2(multiplier=1.0, classes=1000, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. Parameters • multiplier (float, default 1.0) – The width multiplier for controling the model size. The actual number of channels is equal to the original channel size multiplied by this multiplier. • classes (int, default 1000) – Number of classes for the output layer. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.ResNetV1(block, layers, channels, classes=1000, thumbnail=False, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. Parameters • block (gluon.HybridBlock) – Class for the residual block. Options are BasicBlockV1, BottleneckV1. • layers (list of int) – Numbers of layers in each block • channels (list of int) – Numbers of channels in each block. Length should be one larger than layers list. • classes (int, default 1000) – Number of classification classes. • thumbnail (bool, default False) – Enable thumbnail. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.ResNetV2(block, layers, channels, classes=1000, thumbnail=False, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • block (gluon.HybridBlock) – Class for the residual block. Options are BasicBlockV1, BottleneckV1. • layers (list of int) – Numbers of layers in each block • channels (list of int) – Numbers of channels in each block. Length should be one larger than layers list. • classes (int, default 1000) – Number of classification classes. • thumbnail (bool, default False) – Enable thumbnail. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.SqueezeNet(version, classes=1000, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. SqueezeNet model from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper. SqueezeNet 1.1 model from the official SqueezeNet repo. SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy. Parameters • version (str) – Version of squeezenet. Options are ‘1.0’, ‘1.1’. • classes (int, default 1000) – Number of classification classes. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. class mxnet.gluon.model_zoo.vision.VGG(layers, filters, classes=1000, batch_norm=False, **kwargs)[source] Bases: mxnet.gluon.block.HybridBlock Methods  hybrid_forward(F, x) Overrides to construct symbolic graph for this Block. VGG model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. Parameters • layers (list of int) – Numbers of layers in each feature block. • filters (list of int) – Numbers of filters in each feature block. List length should match the layers. • classes (int, default 1000) – Number of classification classes. • batch_norm (bool, default False) – Use batch normalization. hybrid_forward(F, x)[source] Overrides to construct symbolic graph for this Block. Parameters • x (Symbol or NDArray) – The first input tensor. • *args (list of Symbol or list of NDArray) – Additional input tensors. mxnet.gluon.model_zoo.vision.alexnet(pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)[source] AlexNet model from the “One weird trick…” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default$MXNET_HOME/models) – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.densenet121(**kwargs)[source]

Densenet-BC 121-layer model from the “Densely Connected Convolutional Networks” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.densenet161(**kwargs)[source] Densenet-BC 161-layer model from the “Densely Connected Convolutional Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.densenet169(**kwargs)[source]

Densenet-BC 169-layer model from the “Densely Connected Convolutional Networks” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.densenet201(**kwargs)[source] Densenet-BC 201-layer model from the “Densely Connected Convolutional Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.get_mobilenet(multiplier, pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)[source]

MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper.

Parameters
• multiplier (float) – The width multiplier for controling the model size. Only multipliers that are no less than 0.25 are supported. The actual number of channels is equal to the original channel size multiplied by this multiplier.

• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default $MXNET_HOME/models) – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.get_mobilenet_v2(multiplier, pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)[source] MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper. Parameters • multiplier (float) – The width multiplier for controling the model size. Only multipliers that are no less than 0.25 are supported. The actual number of channels is equal to the original channel size multiplied by this multiplier. • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default$MXNET_HOME/models) – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.get_model(name, **kwargs)[source]

Returns a pre-defined model by name

Parameters
• name (str) – Name of the model.

• pretrained (bool) – Whether to load the pretrained weights for model.

• classes (int) – Number of classes for the output layer.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. Returns The model. Return type gluon.HybridBlock mxnet.gluon.model_zoo.vision.get_resnet(version, num_layers, pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)[source] ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • version (int) – Version of ResNet. Options are 1, 2. • num_layers (int) – Numbers of layers. Options are 18, 34, 50, 101, 152. • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default$MXNET_HOME/models) – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.get_vgg(num_layers, pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)[source]

VGG model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper.

Parameters
• num_layers (int) – Number of layers for the variant of densenet. Options are 11, 13, 16, 19.

• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default $MXNET_HOME/models) – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.inception_v3(pretrained=False, ctx=cpu(0), root='/home/jenkins_slave/.mxnet/models', **kwargs)[source] Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default$MXNET_HOME/models) – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.mobilenet0_25(**kwargs)[source]

MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.25.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet0_5(**kwargs)[source]

MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.5.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet0_75(**kwargs)[source]

MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.75.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet1_0(**kwargs)[source]

MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 1.0.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25(**kwargs)[source]

MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5(**kwargs)[source]

MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75(**kwargs)[source]

MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0(**kwargs)[source]

MobileNetV2 model from the “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

mxnet.gluon.model_zoo.vision.resnet101_v1(**kwargs)[source]

ResNet-101 V1 model from “Deep Residual Learning for Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.resnet101_v2(**kwargs)[source] ResNet-101 V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.resnet152_v1(**kwargs)[source]

ResNet-152 V1 model from “Deep Residual Learning for Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.resnet152_v2(**kwargs)[source] ResNet-152 V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.resnet18_v1(**kwargs)[source]

ResNet-18 V1 model from “Deep Residual Learning for Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.resnet18_v2(**kwargs)[source] ResNet-18 V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.resnet34_v1(**kwargs)[source]

ResNet-34 V1 model from “Deep Residual Learning for Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.resnet34_v2(**kwargs)[source] ResNet-34 V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.resnet50_v1(**kwargs)[source]

ResNet-50 V1 model from “Deep Residual Learning for Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.resnet50_v2(**kwargs)[source] ResNet-50 V2 model from “Identity Mappings in Deep Residual Networks” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.squeezenet1_0(**kwargs)[source]

SqueezeNet 1.0 model from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.squeezenet1_1(**kwargs)[source] SqueezeNet 1.1 model from the official SqueezeNet repo. SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.vgg11(**kwargs)[source]

VGG-11 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.vgg11_bn(**kwargs)[source] VGG-11 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.vgg13(**kwargs)[source]

VGG-13 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.vgg13_bn(**kwargs)[source] VGG-13 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.vgg16(**kwargs)[source]

VGG-16 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.vgg16_bn(**kwargs)[source] VGG-16 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.vgg19(**kwargs)[source]

VGG-19 model from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper.

Parameters
• pretrained (bool, default False) – Whether to load the pretrained weights for model.

• ctx (Context, default CPU) – The context in which to load the pretrained weights.

• root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters. mxnet.gluon.model_zoo.vision.vgg19_bn(**kwargs)[source] VGG-19 model with batch normalization from the “Very Deep Convolutional Networks for Large-Scale Image Recognition” paper. Parameters • pretrained (bool, default False) – Whether to load the pretrained weights for model. • ctx (Context, default CPU) – The context in which to load the pretrained weights. • root (str, default '$MXNET_HOME/models') – Location for keeping the model parameters.