Pretrained Inception-v3 convolutional neural network
net = inceptionv3
returns a pretrained
Inception-v3 model. This model is trained on a subset of the ImageNet database , which is used in the
ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). The model is trained on
more than a million images and can classify images into 1000 object categories, such
as keyboard, mouse, pencil, and many animals. As a result, the model has learned
rich feature representations for a wide range of images.
net = inceptionv3
This function requires the Neural Network Toolbox™ Model for Inception-v3 Network support package. If this support package is not installed, then the function provides a download link.
To retrain the network on a new classification task, follow the steps of Transfer Learning Using GoogLeNet. Load the Inception-v3 model instead
of GoogLeNet and change the names of the layers that you remove and connect to match
the names of the Inception-v3 layers: remove the
'ClassificationLayer_predictions' layers, and connect to the
Download and install the Neural Network Toolbox Model for Inception-v3 Network support package.
inceptionv3 at the command line.
If the Neural Network
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.