Pretrained Inception-v3 convolutional neural network
Inception-v3 is a convolutional neural network that is trained on more than a million images from the ImageNet database . The network is 48 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.
To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load Inception-v3 instead of GoogLeNet.
net = inceptionv3
Download and install the Deep Learning Toolbox Model for Inception-v3 Network support package.
inceptionv3 at the command line.
If the Deep Learning
Toolbox Model for Inception-v3 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
the command line. If the required support package is installed, then the
function returns a
ans = DAGNetwork with properties: Layers: [316×1 nnet.cnn.layer.Layer] Connections: [350×2 table]
 ImageNet. http://www.image-net.org
 Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. "Rethinking the inception architecture for computer vision." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826. 2016.
For code generation, you can load the network by using the syntax
inceptionv3 or by passing the
coder.loadDeepLearningNetwork. For example:
For more information, see Load Pretrained Networks for Code Generation (MATLAB Coder).