Pretrained Inception-ResNet-v2 convolutional neural network
Inception-ResNet-v2 is a convolutional neural network that is trained on more than a million images from the ImageNet database . The network is 164 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-ResNet-v2 instead of GoogLeNet.
Download and install the Deep Learning Toolbox Model for Inception-ResNet-v2 Network support package.
inceptionresnetv2 at the command line.
If the Deep Learning
Toolbox Model for Inception-ResNet-v2 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
inceptionresnetv2 at the command line. If the required support
package is installed, then the function returns a
net = inceptionresnetv2
net = DAGNetwork with properties: Layers: [825×1 nnet.cnn.layer.Layer] Connections: [922×2 table]
 ImageNet. http://www.image-net.org
 Szegedy, Christian, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning." In AAAI, vol. 4, p. 12. 2017.