www.gusucode.com > vision 源码程序 matlab案例代码 > vision/ResumeTrainingRCNNExample.m
%% Resume Training an R-CNN Object Detector % Resume training an R-CNN object detector using additional data. To % illustrate this procedure, half the ground truth data will be used to % initially train the detector. Then, training is resumed using all the % data. %% % Load training data and initialize training options. load('rcnnStopSigns.mat', 'stopSigns', 'layers') stopSigns.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ... stopSigns.imageFilename); options = trainingOptions('sgdm', ... 'MiniBatchSize', 32, ... 'InitialLearnRate', 1e-6, ... 'MaxEpochs', 10, ... 'Verbose', false); %% % Train the R-CNN detector with a portion of the ground truth. rcnn = trainRCNNObjectDetector(stopSigns(1:10,:), layers, options, 'NegativeOverlapRange', [0 0.3]); %% % Get the trained network layers from the detector. When you pass in an % array of network layers to |trainRCNNObjectDetector|, they are used as-is % to continue training. network = rcnn.Network; layers = network.Layers; %% % Resume training using all the training data. rcnnFinal = trainRCNNObjectDetector(stopSigns, layers, options);