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Pretrained Inception-v3 convolutional neural network


net = inceptionv3



net = inceptionv3 returns a pretrained Inception-v3 model. This model is trained on a subset of the ImageNet database [1], 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.

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.

You can use classify to classify new images using the Inception-v3 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with Inception-v3.

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 'predictions', 'predictions_softmax', and 'ClassificationLayer_predictions' layers, and connect to the 'avg_pool' layer.


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Download and install the Neural Network Toolbox Model for Inception-v3 Network support package.

Type 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 inceptionv3 at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

ans = 

  DAGNetwork with properties:

         Layers: [316×1 nnet.cnn.layer.Layer]
    Connections: [350×2 table]

Output Arguments

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Pretrained Inception-v3 convolutional neural network, returned as a DAGNetwork object.


[1] ImageNet.

[2] 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.

Introduced in R2017b

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