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Pretrained MobileNet-v2 convolutional neural network

MobileNet-v2 is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 54 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 224-by-224. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.

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

To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load MobileNet-v2 instead of GoogLeNet.


net = mobilenetv2



net = mobilenetv2 returns a pretrained MobileNet-v2 convolutional neural network.

This function requires the Deep Learning Toolbox™ Model for MobileNet-v2 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 MobileNet-v2 Network support package.

Type mobilenetv2 at the command line.


If the Deep Learning Toolbox Model for MobileNet-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 mobilenetv2 at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

ans = 

  DAGNetwork with properties:

         Layers: [155×1 nnet.cnn.layer.Layer]
    Connections: [164×2 table]

Output Arguments

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


[1] ImageNet.

[2] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.C. "MobileNetV2: Inverted Residuals and Linear Bottlenecks." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4510-4520). IEEE.

Introduced in R2019a