Error using vision.internal.cnn.validation.checkNetworkClassificationLayer (line 11)
The number object classes in the network classification layer must be equal to the number of classes defined in the input
trainingData plus 1 for the "Background" class.
Error in trainRCNNObjectDetector>checkNetworkAndFillRemainingParameters (line 290)
vision.internal.cnn.validation.checkNetworkClassificationLayer(analysis, trainingData);
Error in trainRCNNObjectDetector (line 256)
params = checkNetworkAndFillRemainingParameters(trainingData, network, params);
Error in test19 (line 54)
rcnn = trainRCNNObjectDetector(Wgtruth, Newlayers, options, 'NegativeOverlapRange', [0 0.3])
>>
Newlayers = [
imageInputLayer([32 32 3],"Mean",[],"Normalization","zerocenter", "Name","imageinput")
convolution2dLayer([5 5],32,"Name","conv","BiasLearnRateFactor",2,"Padding",[2 2 2 2],"WeightsInitializer","narrow-normal")
maxPooling2dLayer([3 3],"Name","maxpool","Stride",[2 2])
reluLayer("Name","relu")
convolution2dLayer([5 5],32,"Name","conv_1","BiasLearnRateFactor",2,"Padding",[2 2 2 2],"WeightsInitializer","narrow-normal")
reluLayer("Name","relu_1")
averagePooling2dLayer([3 3],"Name","avgpool","Stride",[2 2])
convolution2dLayer([5 5],64,"Name","conv_2","BiasLearnRateFactor",2,"Padding",[2 2 2 2],"WeightsInitializer","narrow-normal")
reluLayer("Name","relu_2")
averagePooling2dLayer([3 3],"Name","avgpool_1","Stride",[2 2])
fullyConnectedLayer(64,"Name","fc","BiasLearnRateFactor",2,"WeightsInitializer","narrow-normal")
reluLayer("Name","relu_3")
fullyConnectedLayer(2,"Name","fc_rcnn","BiasL2Factor",1,"BiasLearnRateFactor",5,"WeightLearnRateFactor",8,"WeightsInitializer","narrow-normal")
softmaxLayer("Name","softmax")
classificationLayer('Name','classoutput')]
Newlayers(15)
Newlayers(14)