Gluon Model Zoo¶
Overview¶
This document lists the model APIs in Gluon:
mxnet.gluon.model_zoo 
Predefined and pretrained models. 
mxnet.gluon.model_zoo.vision 
Module for predefined neural network models. 
The Gluon Model Zoo
API, defined in the gluon.model_zoo
package, provides predefined
and pretrained models to help bootstrap machine learning applications.
In the rest of this document, we list routines provided by the gluon.model_zoo
package.
Vision¶
Module for predefined 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 pretrained 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 pretrained models expect input images normalized in the same way,
i.e. minibatches of 3channel 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]))
The following table summarizes the available models.
Alias  Network  # Parameters  Top1 Accuracy  Top5 Accuracy  Origin 

alexnet  AlexNet  61,100,840  0.5492  0.7803  Converted from pytorch vision 
densenet121  DenseNet121  8,062,504  0.7497  0.9225  Converted from pytorch vision 
densenet161  DenseNet161  28,900,936  0.7770  0.9380  Converted from pytorch vision 
densenet169  DenseNet169  14,307,880  0.7617  0.9317  Converted from pytorch vision 
densenet201  DenseNet201  20,242,984  0.7732  0.9362  Converted from pytorch vision 
inceptionv3  Inception V3 299x299  23,869,000  0.7755  0.9364  Converted from pytorch vision 
mobilenet0.25  MobileNet 0.25  475,544  0.5185  0.7608  Trained with script 
mobilenet0.5  MobileNet 0.5  1,342,536  0.6307  0.8475  Trained with script 
mobilenet0.75  MobileNet 0.75  2,601,976  0.6738  0.8782  Trained with script 
mobilenet1.0  MobileNet 1.0  4,253,864  0.7105  0.9006  Trained with script 
resnet18_v1  ResNet18 V1  11,699,112  0.6803  0.8818  Converted from pytorch vision 
resnet34_v1  ResNet34 V1  21,814,696  0.7202  0.9066  Converted from pytorch vision 
resnet50_v1  ResNet50 V1  25,629,032  0.7540  0.9266  Trained with script 
resnet101_v1  ResNet101 V1  44,695,144  0.7693  0.9334  Trained with script 
resnet152_v1  ResNet152 V1  60,404,072  0.7727  0.9353  Trained with script 
resnet18_v2  ResNet18 V2  11,695,796  0.6961  0.8901  Trained with script 
resnet34_v2  ResNet34 V2  21,811,380  0.7324  0.9125  Trained with script 
resnet50_v2  ResNet50 V2  25,595,060  0.7622  0.9297  Trained with script 
resnet101_v2  ResNet101 V2  44,639,412  0.7747  0.9375  Trained with script 
resnet152_v2  ResNet152 V2  60,329,140  0.7833  0.9409  Trained with script 
squeezenet1.0  SqueezeNet 1.0  1,248,424  0.5611  0.7909  Converted from pytorch vision 
squeezenet1.1  SqueezeNet 1.1  1,235,496  0.5496  0.7817  Converted from pytorch vision 
vgg11  VGG11  132,863,336  0.6662  0.8734  Converted from pytorch vision 
vgg13  VGG13  133,047,848  0.6774  0.8811  Converted from pytorch vision 
vgg16  VGG16  138,357,544  0.6986  0.8945  Converted from pytorch vision 
vgg19  VGG19  143,667,240  0.7072  0.8988  Converted from pytorch vision 
vgg11_bn  VGG11 with batch normalization  132,874,344  0.6859  0.8872  Converted from pytorch vision 
vgg13_bn  VGG13 with batch normalization  133,059,624  0.6884  0.8882  Converted from pytorch vision 
vgg16_bn  VGG16 with batch normalization  138,374,440  0.7142  0.9043  Converted from pytorch vision 
vgg19_bn  VGG19 with batch normalization  143,689,256  0.7241  0.9093  Converted from pytorch vision 
get_model 
Returns a predefined model by name 
ResNet¶
resnet18_v1 
ResNet18 V1 model from “Deep Residual Learning for Image Recognition” paper. 
resnet34_v1 
ResNet34 V1 model from “Deep Residual Learning for Image Recognition” paper. 
resnet50_v1 
ResNet50 V1 model from “Deep Residual Learning for Image Recognition” paper. 
resnet101_v1 
ResNet101 V1 model from “Deep Residual Learning for Image Recognition” paper. 
resnet152_v1 
ResNet152 V1 model from “Deep Residual Learning for Image Recognition” paper. 
resnet18_v2 
ResNet18 V2 model from “Identity Mappings in Deep Residual Networks” paper. 
resnet34_v2 
ResNet34 V2 model from “Identity Mappings in Deep Residual Networks” paper. 
resnet50_v2 
ResNet50 V2 model from “Identity Mappings in Deep Residual Networks” paper. 
resnet101_v2 
ResNet101 V2 model from “Identity Mappings in Deep Residual Networks” paper. 
resnet152_v2 
ResNet152 V2 model from “Identity Mappings in Deep Residual Networks” paper. 
ResNetV1 
ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. 
ResNetV2 
ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper. 
BasicBlockV1 
BasicBlock V1 from “Deep Residual Learning for Image Recognition” paper. 
BasicBlockV2 
BasicBlock V2 from “Identity Mappings in Deep Residual Networks” paper. 
BottleneckV1 
Bottleneck V1 from “Deep Residual Learning for Image Recognition” paper. 
BottleneckV2 
Bottleneck V2 from “Identity Mappings in Deep Residual Networks” paper. 
get_resnet 
ResNet V1 model from “Deep Residual Learning for Image Recognition” paper. 
VGG¶
vgg11 
VGG11 model from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg13 
VGG13 model from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg16 
VGG16 model from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg19 
VGG19 model from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg11_bn 
VGG11 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg13_bn 
VGG13 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg16_bn 
VGG16 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
vgg19_bn 
VGG19 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
VGG 
VGG model from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
get_vgg 
VGG model from the “Very Deep Convolutional Networks for LargeScale Image Recognition” paper. 
Alexnet¶
alexnet 
AlexNet model from the “One weird trick...” paper. 
AlexNet 
AlexNet model from the “One weird trick...” paper. 
DenseNet¶
densenet121 
DensenetBC 121layer model from the “Densely Connected Convolutional Networks” paper. 
densenet161 
DensenetBC 161layer model from the “Densely Connected Convolutional Networks” paper. 
densenet169 
DensenetBC 169layer model from the “Densely Connected Convolutional Networks” paper. 
densenet201 
DensenetBC 201layer model from the “Densely Connected Convolutional Networks” paper. 
DenseNet 
DensenetBC model from the “Densely Connected Convolutional Networks” paper. 
SqueezeNet¶
squeezenet1_0 
SqueezeNet 1.0 model from the “SqueezeNet: AlexNetlevel accuracy with 50x fewer parameters and <0.5MB model size” paper. 
squeezenet1_1 
SqueezeNet 1.1 model from the official SqueezeNet repo. 
SqueezeNet 
SqueezeNet model from the “SqueezeNet: AlexNetlevel accuracy with 50x fewer parameters and <0.5MB model size” paper. 
Inception¶
inception_v3 
Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. 
Inception3 
Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper. 
MobileNet¶
mobilenet1_0 
MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 1.0. 
mobilenet0_75 
MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.75. 
mobilenet0_5 
MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.5. 
mobilenet0_25 
MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper, with width multiplier 0.25. 
MobileNet 
MobileNet model from the “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications” paper. 
API Reference¶
Predefined and pretrained models.
Module for predefined 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 pretrained 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 pretrained models expect input images normalized in the same way,
i.e. minibatches of 3channel 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 predefined 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/models') – Location for keeping the model parameters.
Returns: The model.
Return type:

class
mxnet.gluon.model_zoo.vision.
AlexNet
(classes=1000, **kwargs)[source]¶ AlexNet model from the “One weird trick...” paper.
Parameters: classes (int, default 1000) – Number of classes for the output layer.

class
mxnet.gluon.model_zoo.vision.
BasicBlockV1
(channels, stride, downsample=False, in_channels=0, **kwargs)[source]¶ 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.

class
mxnet.gluon.model_zoo.vision.
BasicBlockV2
(channels, stride, downsample=False, in_channels=0, **kwargs)[source]¶ 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.

class
mxnet.gluon.model_zoo.vision.
BottleneckV1
(channels, stride, downsample=False, in_channels=0, **kwargs)[source]¶ 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.

class
mxnet.gluon.model_zoo.vision.
BottleneckV2
(channels, stride, downsample=False, in_channels=0, **kwargs)[source]¶ 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.

class
mxnet.gluon.model_zoo.vision.
DenseNet
(num_init_features, growth_rate, block_config, bn_size=4, dropout=0, classes=1000, **kwargs)[source]¶ DensenetBC 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.

class
mxnet.gluon.model_zoo.vision.
Inception3
(classes=1000, **kwargs)[source]¶ Inception v3 model from “Rethinking the Inception Architecture for Computer Vision” paper.
Parameters: classes (int, default 1000) – Number of classification classes.

class
mxnet.gluon.model_zoo.vision.
MobileNet
(multiplier=1.0, classes=1000, **kwargs)[source]¶ 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.

class
mxnet.gluon.model_zoo.vision.
MobileNetV2
(multiplier=1.0, classes=1000, **kwargs)[source]¶ MobileNetV2 model from the `“Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation”<https://arxiv.org/abs/1801.04381>`_ 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.

class
mxnet.gluon.model_zoo.vision.
ResNetV1
(block, layers, channels, classes=1000, thumbnail=False, **kwargs)[source]¶ ResNet V1 model from “Deep Residual Learning for Image Recognition” paper.
Parameters:  block (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.

class
mxnet.gluon.model_zoo.vision.
ResNetV2
(block, layers, channels, classes=1000, thumbnail=False, **kwargs)[source]¶ ResNet V2 model from “Identity Mappings in Deep Residual Networks” paper.
Parameters:  block (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.

class
mxnet.gluon.model_zoo.vision.
SqueezeNet
(version, classes=1000, **kwargs)[source]¶ SqueezeNet model from the “SqueezeNet: AlexNetlevel 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.

class
mxnet.gluon.model_zoo.vision.
VGG
(layers, filters, classes=1000, batch_norm=False, **kwargs)[source]¶ VGG model from the “Very Deep Convolutional Networks for LargeScale 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.

mxnet.gluon.model_zoo.vision.
alexnet
(pretrained=False, ctx=cpu(0), root='~/.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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
densenet121
(**kwargs)[source]¶ DensenetBC 121layer 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
densenet161
(**kwargs)[source]¶ DensenetBC 161layer 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
densenet169
(**kwargs)[source]¶ DensenetBC 169layer 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
densenet201
(**kwargs)[source]¶ DensenetBC 201layer 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
get_mobilenet
(multiplier, pretrained=False, ctx=cpu(0), root='~/.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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
get_mobilenet_v2
(multiplier, pretrained=False, ctx=cpu(0), root='~/.mxnet/models', **kwargs)[source]¶ MobileNetV2 model from the `“Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation”<https://arxiv.org/abs/1801.04381>`_ 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
get_resnet
(version, num_layers, pretrained=False, ctx=cpu(0), root='~/.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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
get_vgg
(num_layers, pretrained=False, ctx=cpu(0), root='~/.mxnet/models', **kwargs)[source]¶ VGG model from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
inception_v3
(pretrained=False, ctx=cpu(0), root='~/.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/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”<https://arxiv.org/abs/1801.04381>`_ 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”<https://arxiv.org/abs/1801.04381>`_ 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”<https://arxiv.org/abs/1801.04381>`_ 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”<https://arxiv.org/abs/1801.04381>`_ 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]¶ ResNet101 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet101_v2
(**kwargs)[source]¶ ResNet101 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet152_v1
(**kwargs)[source]¶ ResNet152 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet152_v2
(**kwargs)[source]¶ ResNet152 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet18_v1
(**kwargs)[source]¶ ResNet18 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet18_v2
(**kwargs)[source]¶ ResNet18 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet34_v1
(**kwargs)[source]¶ ResNet34 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet34_v2
(**kwargs)[source]¶ ResNet34 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet50_v1
(**kwargs)[source]¶ ResNet50 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
resnet50_v2
(**kwargs)[source]¶ ResNet50 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
squeezenet1_0
(**kwargs)[source]¶ SqueezeNet 1.0 model from the “SqueezeNet: AlexNetlevel 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/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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg11
(**kwargs)[source]¶ VGG11 model from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg11_bn
(**kwargs)[source]¶ VGG11 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg13
(**kwargs)[source]¶ VGG13 model from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg13_bn
(**kwargs)[source]¶ VGG13 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg16
(**kwargs)[source]¶ VGG16 model from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg16_bn
(**kwargs)[source]¶ VGG16 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg19
(**kwargs)[source]¶ VGG19 model from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.

mxnet.gluon.model_zoo.vision.
vgg19_bn
(**kwargs)[source]¶ VGG19 model with batch normalization from the “Very Deep Convolutional Networks for LargeScale 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/models') – Location for keeping the model parameters.