Improper initialization of classification layer in rcnn
Hello, I'm a relative newbie to MATLAB and neural networks, and I'm looking at disease spread and analysis in crop fields. I wanted to make an RCNN to help with this. I have some skeleton code, but I'm getting errors I don't understand and don't have the skill to debug. Here is the code: load 'D:\Documents\MATLAB\bridgeLabels.mat', 'gTruth'; %these are the labels I made in the image labeler app trainingData = objectDetectorTrainingData(gTruth); %this apparently makes the training data for me layers = [imageInputLayer([2160 3840 3]) convolution2dLayer([5 5],10) reluLayer() fullyConnectedLayer(10) softmaxLayer() classificationLayer()]; %I understand what all these things do, kind of. %I just copied this code from the demonstration in the reference %I'm getting some error with the classification layer I don't know how to fix options = trainingOptions('sgdm',... 'LearnRateSchedule','piecewise',... 'LearnRateDropFactor',0.2,... 'LearnRateDropPeriod',5,... 'MaxEpochs',20,... 'MiniBatchSize',64,... 'Plots','training-progress'); %again, most of this makes sense to me detector = trainRCNNObjectDetector(trainingData, layers, options); %ok so now the network is made apparently image = imread('D:\Documents\MATLAB\clubroot_shots\lcbo1.png'); %this is my testing image wid = 10; rois = zeros(1, (image.width/wid)*(image.height/wid)); for i=1:image.width/wid for j=1:image.height/wid rois(i+j*width) = [1+(i-1)*wid, 1+(j-1)*wid, wid, wid]; end end %I believe this code will split up the image into 10x10 regions of interest. %I wrote this block myself. classifyRegions(detector, image, rois) %and here the regions get classified. Semicolon off because i want to see what happens When I run this code, I get the following errors: Error using vision.internal.cnn.validation.checkNetworkClassificationLayer (line 9) 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 vision.internal.rcnn.parseInputs (line 35) vision.internal.cnn.validation.checkNetworkClassificationLayer(network, trainingData); Error in trainRCNNObjectDetector (line 185) params = vision.internal.rcnn.parseInputs(trainingData, network, options, mfilename, varargin{:}); Error in imagenn (line 20) detector = trainRCNNObjectDetector(trainingData, layers, options); Error in run (line 91) evalin('caller', strcat(script, ';')); I'm not sure, but I believe all these errors stem from an improperly declared classificationLayer. I have two classes, called 'clubroot' and 'healthy'. I'm not sure how to set up the network so it recognizes these two classes. If anyone could offer help, I would be eternally grateful. Getting this to work is very important to me.
Kshitij Singh answered .
2025-11-20
As given here, you are improperly initializing the fullyConnectedLayer. Instead of using fullyConnectedLayer(10) try something like this.
classes = {'first', 'second'}
outputs = 1+numel(classes); % +1 for background class
layers = [imageInputLayer([2160 3840 3])
convolution2dLayer([5 5],10)
reluLayer()
fullyConnectedLayer(outputs)
softmaxLayer()
classificationLayer()];