inceptionresnetv2

Pretrained Inception-ResNet-v2 convolutional neural network

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Syntax

net = inceptionresnetv2

Description

Inception-ResNet-v2 is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. 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.

You can use classify to classify new images using the Inception-ResNet-v2 network. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with Inception-ResNet-v2.

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.

net = inceptionresnetv2 returns a pretrained Inception-ResNet-v2 network.

This function requires the Deep Learning Toolbox™ Model for Inception-ResNet-v2 Network support package. If this support package is not installed, then the function provides a download link.

Examples

Load Inception-ResNet-v2 Network

Download and install the Deep Learning Toolbox Model for Inception-ResNet-v2 Network support package.

Type inceptionresnetv2 at the command line.

inceptionresnetv2

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 DAGNetwork object.

net = inceptionresnetv2

net = 

  DAGNetwork with properties:

         Layers: [825×1 nnet.cnn.layer.Layer]
    Connections: [922×2 table]

Visualize the network using Deep Network Designer.

deepNetworkDesigner(inceptionresnetv2)

Explore other pretrained neural networks in Deep Network Designer by clicking New.

If you need to download a neural network, pause on the desired neural network and click Install to open the Add-On Explorer.

Output Arguments

net — Pretrained Inception-ResNet-v2 convolutional neural network

DAGNetwork object

Pretrained Inception-ResNet-v2 convolutional neural network, returned as a DAGNetwork object.

References

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

[2] 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.

[3] https://keras.io/api/applications/inceptionresnetv2/

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

For code generation, you can load the network by using the syntax net = inceptionresnetv2 or by passing the inceptionresnetv2 function to coder.loadDeepLearningNetwork (MATLAB Coder). For example: net = coder.loadDeepLearningNetwork('inceptionresnetv2')

For more information, see Load Pretrained Networks for Code Generation (MATLAB Coder).

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

For code generation, you can load the network by using the syntax net = inceptionresnetv2 or by passing the inceptionresnetv2 function to coder.loadDeepLearningNetwork (GPU Coder). For example: net = coder.loadDeepLearningNetwork('inceptionresnetv2')

For more information, see Load Pretrained Networks for Code Generation (GPU Coder).

Version History

Introduced in R2017b