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inceptionv3

Inception-v3 convolutional neural network

Description

Inception-v3 is a convolutional neural network that is 48 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network 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.

example

net = inceptionv3 returns an Inception-v3 network trained on the ImageNet database.

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.

net = inceptionv3('Weights','imagenet') returns an Inception-v3 network trained on the ImageNet database. This syntax is equivalent to net = inceptionv3.

lgraph = inceptionv3('Weights','none') returns the untrained Inception-v3 network architecture. The untrained model does not require the support package.

Examples

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

Type inceptionv3 at the command line.

inceptionv3

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.

inceptionv3
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.

Untrained Inception-v3 convolutional neural network architecture, returned as a LayerGraph object.

References

[1] ImageNet. http://www.image-net.org

[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