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xception

Pretrained Xception convolutional neural network

Xception is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 71 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 299-by-299. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.

You can use classify to classify new images using the Xception model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with Xception.

To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load Xception instead of GoogLeNet.

Syntax

net = xception

Description

example

net = xception returns a pretrained Xception convolutional neural network.

This function requires the Deep Learning Toolbox™ Model for Xception Network support package. If this support package is not installed, then the function provides a download link.

Examples

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Download and install the Deep Learning Toolbox Model for Xception Network support package.

Type xception at the command line.

xception

If the Deep Learning Toolbox Model for Xception Network support package is not installed, then the function provides a link to the required support package in the Add-On Explorer. To install the support package, click the link, and then click Install. Check that the installation is successful by typing xception at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

xception
ans = 

  DAGNetwork with properties:

         Layers: [171×1 nnet.cnn.layer.Layer]
    Connections: [182×2 table]

Output Arguments

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Pretrained Xception convolutional neural network, returned as a DAGNetwork object.

References

[1] ImageNet. http://www.image-net.org

[2] Chollet, F., 2017. "Xception: Deep Learning with Depthwise Separable Convolutions." arXiv preprint, pp.1610-02357.

Introduced in R2019a