resnet50

Pretrained ResNet-50 convolutional neural network

Description

ResNet-50 is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 50 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 ResNet-50 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with ResNet-50.

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

example

net = resnet50 returns a pretrained ResNet-50 network.

This function requires the Deep Learning Toolbox™ Model for ResNet-50 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 ResNet-50 Network support package.

Type resnet50 at the command line.

resnet50

If the Deep Learning Toolbox Model for ResNet-50 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 resnet50 at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

resnet50
ans = 

  DAGNetwork with properties:

         Layers: [177×1 nnet.cnn.layer.Layer]
    Connections: [192×2 table]

Output Arguments

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Pretrained ResNet-50 convolutional neural network, returned as a DAGNetwork 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

Introduced in R2017b