Pretrained ResNet-18 convolutional neural network
ResNet-18 is a convolutional neural network that is trained on more than a million images from the ImageNet database . The network is 18 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.
To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images. Load the ResNet-18 model instead of GoogLeNet
and change the names of the layers that you replace to
net = resnet18
Download and install the Deep Learning Toolbox Model for ResNet-18 Network support package.
resnet18 at the command line.
If the Deep Learning
Toolbox Model for ResNet-18 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
resnet18 at the command line. If the required support package is
installed, then the function returns a
ans = DAGNetwork with properties: Layers: [72×1 nnet.cnn.layer.Layer] Connections: [79×2 table]
 ImageNet. http://www.image-net.org
 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.