Gluon Model Zoo

Overview

This document lists the model APIs in Gluon:

mxnet.gluon.model_zoo Predefined and pretrained models.

The Gluon Model Zoo API, defined in the gluon.model_zoo package, provides pre-defined and pre-trained models to help bootstrap machine learning applications.

Warning

This package contains experimental APIs and may change in the near future.

In the rest of this document, we list routines provided by the gluon.model_zoo package.

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

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

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 models except ResNet V2. These can constructed by passing pretrained=True: .. code:

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

Pretrained models are converted from torchvision. 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]))
get_model Returns a pre-defined model by name

ResNet

resnet18_v1 ResNet-18 V1 model from “Deep Residual Learning for Image Recognition” paper.
resnet34_v1 ResNet-34 V1 model from “Deep Residual Learning for Image Recognition” paper.
resnet50_v1 ResNet-50 V1 model from “Deep Residual Learning for Image Recognition” paper.
resnet101_v1 ResNet-101 V1 model from “Deep Residual Learning for Image Recognition” paper.
resnet152_v1 ResNet-152 V1 model from “Deep Residual Learning for Image Recognition” paper.
resnet18_v2 ResNet-18 V2 model from “Identity Mappings in Deep Residual Networks” paper.
resnet34_v2 ResNet-34 V2 model from “Identity Mappings in Deep Residual Networks” paper.
resnet50_v2 ResNet-50 V2 model from “Identity Mappings in Deep Residual Networks” paper.
resnet101_v2 ResNet-101 V2 model from “Identity Mappings in Deep Residual Networks” paper.
resnet152_v2 ResNet-152 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.

Alexnet

alexnet AlexNet model from the “One weird trick...” paper.
AlexNet AlexNet model from the “One weird trick...” paper.

DenseNet

densenet121 Densenet-BC 121-layer model from the “Densely Connected Convolutional Networks” paper.
densenet161 Densenet-BC 161-layer model from the “Densely Connected Convolutional Networks” paper.
densenet169 Densenet-BC 169-layer model from the “Densely Connected Convolutional Networks” paper.
densenet201 Densenet-BC 201-layer model from the “Densely Connected Convolutional Networks” paper.
DenseNet Densenet-BC model from the “Densely Connected Convolutional Networks” 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

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

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 models except ResNet V2. These can constructed by passing pretrained=True: .. code:

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

Pretrained models are converted from torchvision. 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)

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/models') – Location for keeping the model parameters.
Returns:

The model.

Return type:

HybridBlock

class mxnet.gluon.model_zoo.vision.AlexNet(classes=1000, **kwargs)

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)

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)

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)

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)

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)

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.
class mxnet.gluon.model_zoo.vision.Inception3(classes=1000, **kwargs)

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)

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.ResNetV1(block, layers, channels, classes=1000, thumbnail=False, **kwargs)

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)

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)

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.
class mxnet.gluon.model_zoo.vision.VGG(layers, filters, classes=1000, batch_norm=False, **kwargs)

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.
mxnet.gluon.model_zoo.vision.alexnet(pretrained=False, ctx=cpu(0), root='~/.mxnet/models', **kwargs)

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)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.densenet161(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.densenet169(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.densenet201(**kwargs)

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

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_resnet(version, num_layers, pretrained=False, ctx=cpu(0), root='~/.mxnet/models', **kwargs)

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)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.inception_v3(pretrained=False, ctx=cpu(0), root='~/.mxnet/models', **kwargs)

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)

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)

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)

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)

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.resnet101_v1(**kwargs)

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

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

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

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

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

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

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

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

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

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.squeezenet1_0(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.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.

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)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg11_bn(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg13(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg13_bn(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg16(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg16_bn(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg19(**kwargs)

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/models') – Location for keeping the model parameters.
mxnet.gluon.model_zoo.vision.vgg19_bn(**kwargs)

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/models') – Location for keeping the model parameters.