resnet50

ResNet-50 convolutional neural network

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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 neural network trained on more than a million images from the ImageNet database [1]. The pretrained neural network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the neural network has learned rich feature representations for a wide range of images. The neural network has an image input size of 224-by-224. For more pretrained neural 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 neural 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.

Tip

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

net = resnet50 returns a ResNet-50 neural network trained on the ImageNet data set.

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.

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

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

Examples

Download ResNet-50 Support Package

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]

Visualize the neural network using Deep Network Designer.

deepNetworkDesigner(resnet50)

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

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

net — Pretrained ResNet-50 convolutional neural network

DAGNetwork object

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

lgraph — Untrained ResNet-50 convolutional neural network architecture

LayerGraph object

Untrained ResNet-50 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.

[3] https://keras.io/api/applications/resnet/#resnet50-function

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

For code generation, you can load the neural network by using the syntax net = resnet50 or by passing the resnet50 function to coder.loadDeepLearningNetwork (MATLAB Coder). For example: net = coder.loadDeepLearningNetwork('resnet50')

For more information, see Load Pretrained Networks for Code Generation (MATLAB Coder).

The syntax resnet50('Weights','none') is not supported for code generation.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• For code generation, you can load the neural network by using the syntax net = resnet50 or by passing the resnet50 function to coder.loadDeepLearningNetwork (GPU Coder). For example: net = coder.loadDeepLearningNetwork('resnet50')

  For more information, see Load Pretrained Networks for Code Generation (GPU Coder).

• The syntax resnet50('Weights','none') is not supported for GPU code generation.

Version History

Introduced in R2017b