densenet201

Pretrained DenseNet-201 convolutional neural network

DenseNet-201 is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 201 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 DenseNet-201 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with DenseNet-201.

To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load DenseNet-201 instead of GoogLeNet.

Syntax

net = densenet201

Description

example

net = densenet201 returns a pretrained DenseNet-201 convolutional neural network.

This function requires the Deep Learning Toolbox™ Model for DenseNet-201 Network support package. If this support package is not installed, then the function provides a download link.

Examples

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Download and install the Deep Learning Toolbox Model for DenseNet-201 Network support package.

Type densenet201 at the command line.

densenet201

If the Deep Learning Toolbox Model for DenseNet-201 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 densenet201 at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

densenet201
ans = 

  DAGNetwork with properties:

         Layers: [709×1 nnet.cnn.layer.Layer]
    Connections: [806×2 table]

Output Arguments

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Pretrained DenseNet-201 convolutional neural network, returned as a DAGNetwork object.

References

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

[2] Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. "Densely Connected Convolutional Networks." In CVPR, vol. 1, no. 2, p. 3. 2017.

Introduced in R2018a