Pretrained GoogLeNet convolutional neural network
GoogLeNet is a convolutional neural network that is trained on more than a million images from the ImageNet database . 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.
For an example showing how to retrain GoogLeNet on a new classification task, see Train Deep Learning Network to Classify New Images
net = googlenet
Download and install the Deep Learning Toolbox Model for GoogLeNet Network support package.
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
ans = DAGNetwork with properties: Layers: [144×1 nnet.cnn.layer.Layer] Connections: [170×2 table]
 ImageNet. http://www.image-net.org
 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.
 BVLC GoogLeNet Model. https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
For code generation, you can load the network by using the syntax
googlenet or by passing the
googlenet function to
coder.loadDeepLearningNetwork. For example:
For more information, see Load Pretrained Networks for Code Generation (MATLAB Coder).