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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 [1]. 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.

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 Train Deep Learning Network to Classify New Images and load Inception-v3 instead of GoogLeNet.


net = inceptionv3



net = inceptionv3 returns a pretrained Inception-v3 network.

This function requires the Deep Learning Toolbox™ Model for Inception-v3 Network support package. If this support package is not installed, then the function provides a download link.


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

Type 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 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.

Extended Capabilities

Introduced in R2017b