Using a trained FastRCNNObjectDetector, expected LABEL to be nonempty

Hello,

I used the following trained Fast R-CNN:

trainingData = objectDetectorTrainingData(gTruth)

net = vgg16;

layersTransfer = net.Layers(1:end-3);
% numClasses = numel(categories(imdsTrain.Labels));
layers = [
    layersTransfer
    fullyConnectedLayer(9,'WeightLearnRateFactor',10,'BiasLearnRateFactor',10)
    softmaxLayer
    classificationLayer];

options = trainingOptions('sgdm', ...
    'MiniBatchSize', 1, ...
    'InitialLearnRate', 1e-6, ...
    'MaxEpochs', 10)

frcnn = trainFastRCNNObjectDetector(trainingData, layers , options, ...
    'PositiveOverlapRange',[0.1 1], ...
    'NegativeOverlapRange',[0 0.1])

The training was based on trainingData which was a an imported gTruth table which included around 8 different ROI labels for 4,000 images of 224x224 dimension.

Then I have saved the trained FRCNN to classify a sample image, the error I have getting is "Expected LABEL to be nonempty", and in workspace the labels is indeed empty, I am confused as to why this has happened though I have trained the network and everything.

load frcnn
detector = frcnn
I = imread('sampleImage.jpg')
imshow(I)
[bboxes,scores,labels] = detect(detector,I)
detectedI = insertObjectAnnotation(I,'Rectangle',bboxes,cellstr(labels))
figure
imshow(detectedI)

[Jingang Wu]

I used the pretrained detector in mathwork example "Object Detection Using Faster R-CNN Deep Learning". I encountered the same problem. I applied my own dataset and for the first image it was working well. But for other images, the bboxes was empty.