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resnet101

ResNet-101 convolutional neural network

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  • ResNet-101 network architecture

Syntax

net = resnet101
net = resnet101('Weights','imagenet')
lgraph = resnet101('Weights','none')

Description

ResNet-101 is a convolutional neural network that is 101 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®, see Pretrained Deep Neural Networks.

You can use classify to classify new images using the ResNet-101 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with ResNet-101.

To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load ResNet-101 instead of GoogLeNet.

Tip

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

example

net = resnet101 returns a ResNet-101 network trained on the ImageNet data set.

This function requires the Deep Learning Toolbox™ Model for ResNet-101 Network support package. If this support package is not installed, then the function provides a download link.

net = resnet101('Weights','imagenet') returns a ResNet-101 network trained on the ImageNet data set. This syntax is equivalent to net = resnet101.

lgraph = resnet101('Weights','none') returns the untrained ResNet-101 network architecture. The untrained model does not require the support package.

Examples

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Download and install the Deep Learning Toolbox Model for ResNet-101 Network support package.

Type resnet101 at the command line.

resnet101

If the Deep Learning Toolbox Model for ResNet-101 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 resnet101 at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

resnet101
ans = 

  DAGNetwork with properties:

         Layers: [347×1 nnet.cnn.layer.Layer]
    Connections: [379×2 table]

Visualize the network using Deep Network Designer. 

deepNetworkDesigner(resnet101)

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

Deep Network Designer start page showing available pretrained neural networks

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

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Pretrained ResNet-101 convolutional neural network, returned as a DAGNetwork object.

Untrained ResNet-101 convolutional neural network architecture, returned as a LayerGraph object.

References

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

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

Extended Capabilities

Version History

Introduced in R2017b

See Also

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