Pretrained ResNet-101 convolutional neural network
ResNet-101 is a convolutional neural network that is trained on more than a million images from the ImageNet database . The network is 101 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-101 model instead of
GoogLeNet and change the names of the layers that you replace to
net = resnet101
Download and install the Deep Learning Toolbox Model for ResNet-101 Network support package.
resnet101 at the command line.
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
the command line. If the required support package is installed, then the
function returns a
ans = DAGNetwork with properties: Layers: [347×1 nnet.cnn.layer.Layer] Connections: [379×2 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.
For code generation, you can load the network by using the syntax
resnet101 or by passing the
resnet101 function to
coder.loadDeepLearningNetwork. For example:
For more information, see Load Pretrained Networks for Code Generation (MATLAB Coder).