This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

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 Convolutional 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.

Examples

collapse all

Download and install the Deep Learning Toolbox Model for DenseNet-201 Network support package using the Add-On Explorer. You can also download the networks from MathWorks Deep Learning Toolbox Team.

After installing the support package, load the network as a DAGNetwork object using densenet201.

net = densenet201
net = 

  DAGNetwork with properties:

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

Output Arguments

collapse all

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