googlenet

Pretrained GoogLeNet convolutional neural network

GoogLeNet is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 22 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 224-by-224. For more pretrained networks in MATLAB^®, see Pretrained Convolutional Neural Networks.

To classify new images using GoogLeNet, use classify. For an example, see Classify Image Using GoogLeNet.

For an example showing how to retrain GoogLeNet on a new classification task, see Train Deep Learning Network to Classify New Images

Syntax

net = googlenet

Description

net = googlenet returns a pretrained GoogLeNet network.

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

Examples

Download GoogLeNet Support Package

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

Type googlenet at the command line.

googlenet

If the Deep Learning Toolbox Model for GoogLeNet 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 googlenet at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

googlenet

ans = 

  DAGNetwork with properties:

         Layers: [144×1 nnet.cnn.layer.Layer]
    Connections: [170×2 table]

Output Arguments

net — Pretrained GoogLeNet convolutional neural network

DAGNetwork object

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

References

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

[2] Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with convolutions." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. 2015.

[3] BVLC GoogLeNet Model. https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

For code generation, you can load the network by using the syntax net = googlenet or by passing the googlenet function to coder.loadDeepLearningNetwork. For example: net = coder.loadDeepLearningNetwork('googlenet')

For more information, see Load Pretrained Networks for Code Generation (MATLAB Coder).