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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


net = googlenet



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.


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

Type googlenet at the command line.


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.

ans = 

  DAGNetwork with properties:

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

Output Arguments

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


[1] ImageNet.

[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.

Extended Capabilities

Introduced in R2017b