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resnet50

ResNet-50 convolutional neural network

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  • ResNet-50 architecture

Syntax

net = resnet50
net = resnet50('Weights','imagenet')
lgraph = resnet50('Weights','none')

Description

ResNet-50 is a convolutional neural network that is 50 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 224-by-224. For more pretrained networks in MATLAB®, seePretrained Deep Neural Networks.

You can useclassifyto classify new images using the ResNet-50 model. Follow the steps ofClassify Image Using GoogLeNetand replace GoogLeNet with ResNet-50.

To retrain the network on a new classification task, follow the steps ofTrain Deep Learning Network to Classify New Imagesand load ResNet-50 instead of GoogLeNet.

Tip

To create an untrained residual network suitable for image classification tasks, useresnetLayers.

example

net= resnet50returns a ResNet-50 network trained on the ImageNet data set.

This function requires the Deep Learning Toolbox™ Modelfor ResNet-50 Networksupport package. If this support package is not installed, then the function provides a download link.

net= resnet50('Weights','imagenet')returns a ResNet-50 network trained on the ImageNet data set. This syntax is equivalent tonet = resnet50.

lgraph= resnet50('Weights','none')returns the untrained ResNet-50 network architecture. The untrained model does not require the support package.

Examples

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Download and install the Deep Learning Toolbox Modelfor ResNet-50 Networksupport package.

Typeresnet50at the command line.

resnet50

If the Deep Learning Toolbox Modelfor ResNet-50 Networksupport 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 clickInstall. Check that the installation is successful by typingresnet50at the command line. If the required support package is installed, then the function returns aDAGNetworkobject.

resnet50
ans = DAGNetwork with properties: Layers: [177×1 nnet.cnn.layer.Layer] Connections: [192×2 table]

Visualize the network using Deep Network Designer.

deepNetworkDesigner(resnet50)

Explore other pretrained networks in Deep Network Designer by clickingNew.

Deep Network Designer start page showing available pretrained networks

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

Output Arguments

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Pretrained ResNet-50 convolutional neural network, returned as aDAGNetworkobject.

Untrained ResNet-50 convolutional neural network architecture, returned as aLayerGraphobject.

References

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

[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." InProceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.

Extended Capabilities

See Also

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Topics

Introduced in R2017b

Deep Learning Toolbox Documentation

金宝app

Introducing Deep Learning with MATLAB

Introducing Deep Learning with MATLAB

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