Hope this information helps those who want to train their own mask R-CNN on MATLAB.
Error executing of the example code for training a custom Mask
Error executing of the example code for training a custom Mask R-CNN using cocodataset 2014 I followed the instructions in "Instance Segmentation Using Mask R-CNN Deep Learning" (ref[1]). All the code worked perfectly until the last section "Train network" (ref[2]). iteration = 1; start = tic; % Create subplots for the learning rate and mini-batch loss fig = figure; [lossPlotter] = helper.configureTrainingProgressPlotter(fig); % Initialize verbose output helper.initializeVerboseOutput([]); % Custom training loop for epoch = 1:numEpochs reset(mbqTrain) shuffle(mbqTrain) while hasdata(mbqTrain) % Get next batch from minibatchqueue [X,gtBox,gtClass,gtMask] = next(mbqTrain); % Evaluate the model gradients and loss using dlfeval [gradients,loss,state] = dlfeval(@networkGradients,X,gtBox,gtClass,gtMask,dlnet,params); dlnet.State = state; % Compute the learning rate for the current iteration learnRate = initialLearnRate/(1 + decay*iteration); if(~isempty(gradients) && ~isempty(loss)) [dlnet.Learnables,velocity] = sgdmupdate(dlnet.Learnables,gradients,velocity,learnRate,momentum); else continue; end helper.displayVerboseOutputEveryEpoch(start,learnRate,epoch,iteration,loss); % Plot loss/accuracy metric D = duration(0,0,toc(start),'Format','hh:mm:ss'); addpoints(lossPlotter,numdetectMaskRCNN,Iteration,double(gather(extractdata(loss)))) subplot(2,1,2) title(strcat("Epoch: ",num2str(epoch),", Elapsed: "+string(D))) drawnow iteration = iteration + 1; end end net = dlnet; % Save the trained network modelDateTime = string(datetime('now','Format',"yyyy-MM-dd-HH-mm-ss")); save(strcat("trainedMaskRCNN-",modelDateTime,"-Epoch-",num2str(numEpochs),".mat"),'net'); First, there is no "numdetectMaskRCNN" predefined. I simply deleted it and reexecuted the section. It then showes the following error: Error using nnet.internal.cnn.dlnetwork/forward (line 239) Layer 'bn2a_branch2a': Invalid input data. The value of 'Variance' is invalid. Expected input to be positive. Error in nnet.internal.cnn.dlnetwork/CodegenOptimizationStrategy/propagateWithFallback (line 122) [varargout{1:nargout}] = fcn(net, X, layerIndices, layerOutputIndices); Error in nnet.internal.cnn.dlnetwork/CodegenOptimizationStrategy/forward (line 62) [varargout{1:nargout}] = propagateWithFallback(strategy, functionSlot, @forward, net, X, layerIndices, layerOutputIndices); Error in nnet.internal.cnn.dlnetwork/DefaultOptimizationStrategy/propagate (line 143) [varargout{1:nargout}] = inferenceMethod(strategy.CodegenStrategyOriginal,... Error in nnet.internal.cnn.dlnetwork/DefaultOptimizationStrategy/forward (line 77) [varargout{1:nargout}] = propagate(strategy, net, X, ... Error in dlnetwork/forward (line 503) [varargout{1:nargout}] = strategy.forward(net.PrivateNetwork, x, layerIndices, layerOutputIndices); Error in networkGradients (line 21) [YRPNRegDeltas, proposal, YRCNNClass, YRCNNReg, YRPNClass, YMask, state] = forward(... Error in deep.internal.dlfeval (line 18) [varargout{1:nout}] = fun(x{:}); Error in dlfeval (line 41) [varargout{1:nout}] = deep.internal.dlfeval(fun,varargin{:}); I am wondering if there is anything I misunderstood so that the code doesn't work for me. It will be of great help if this could be figured out or fixed.
Neeta Dsouza answered .
2025-11-20
[gradients,loss,state] = dlfeval(@networkGradients,X,gtBox,gtClass,gtMask,dlnet,params); dlnet.State = state;
idx = dlnet.State.Parameter == "TrainedVariance";
boundAwayFromZero = @(X) max(X, eps('single'));
dlnet.State(idx,:) = dlupdate(boundAwayFromZero, dlnet.State(idx,:));
Name: 'prashant 1080'
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