mxnet.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.np.array([0.485, 0.456, 0.406]),
                                      std=mx.np.array([0.229, 0.224, 0.225]))

Functions

get_model(name, **kwargs)

Returns a pre-defined model by name

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

Modules

`alexnet`([pretrained, device, root])	AlexNet model from the "One weird trick..." paper.
`densenet`	DenseNet, implemented in Gluon.
`inception`	Inception, implemented in Gluon.
`mobilenet`	MobileNet and MobileNetV2, implemented in Gluon.
`resnet`	ResNets, implemented in Gluon.
`squeezenet`	SqueezeNet, implemented in Gluon.
`vgg`	VGG, implemented in Gluon.