www.gusucode.com > vision 源码程序 matlab案例代码 > vision/NetworkForMulticlassRCNNObjectDetectionExample.m
%% Create a network for multiclass R-CNN object detection % Create an R-CNN object detector for two object classes: dogs and cats. objectClasses = {'dogs','cats'}; %% % The network must be able to classify both dogs, cats, and a "background" % class in order to be trained using |trainRCNNObjectDetector|. In this % example, a one is added to include the background. numClassesPlusBackground = numel(objectClasses) + 1; %% % The final fully connected layer of a network defines the number of % classes that the network can classify. Set the final fully connected % layer to have an output size equal to the number of classes plus a % background class. layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) fullyConnectedLayer(numClassesPlusBackground); softmaxLayer() classificationLayer()]; %% % These network layers can now be used to train an R-CNN two-class object % detector.