Model Zoo¶

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]))

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([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.