How to fix: "Index Exceeds number of array element " when training faster R-CNN

I am trying to train a faster rcnn detector to detect 5 classes. I created the network according to the mathworks "create faster rcnn network". when i try to train, i get the "Index Exceeds number of array element " error as shown in the attached file. Thank you in advance.

The created network

% load pretrained Resnet-50. 
net = resnet50();

% convert network into layer graph
lgraph = layerGraph(net);

%plot lgraph
figure
plot(lgraph)
ylim([-5 16])
layersToRemove = {'fc1000'
    'fc1000_softmax'
    'ClassificationLayer_fc1000'};
lgraph = removeLayers(lgraph,layersToRemove);

%plot lgraph
figure
plot(lgraph)
ylim([-5 16])
% Specify the number of classes the network should classify.
numClasses = 5;
numClassesPlusBackground = 5 + 1;

%Define new classification layers

newLayers = [
    fullyConnectedLayer(numClassesPlusBackground, 'Name', 'rcnnFc')
    softmaxLayer('Name', 'rcnnSoftmax')
    classificationLayer('Name', 'rcnnClassification')];

%Add new layers
lgraph = addLayers(lgraph, newLayers);
lgraph = connectLayers(lgraph, 'avg_pool', 'rcnnFc');

%plot lgraph
figure
plot(lgraph)
ylim([-5 16])
%define the number of outputs of the fully connected layer
numOutputs = 4 * numClasses;

%Create the box regression layers

boxRegressionLayers = [
    fullyConnectedLayer(numOutputs, 'Name', 'rcnnBoxFc')
    rcnnBoxRegressionLayer('Name', 'rcnnBoxDeltas')];

%Add the layers to the network
lgraph = addLayers(lgraph, boxRegressionLayers);

%connect the added layers
lgraph = connectLayers(lgraph, 'avg_pool', 'rcnnBoxFc');

%display the classification and regression branches of the fast rcnn lgraph
figure
plot(lgraph)
ylim([-5 16])
%typical feature extraction layer of fast rcnn
featureExtractionLayer = 'activation_40_relu';

figure
plot(lgraph)
ylim([30 42])
% Disconnect layers attached to the feature extraction layer
lgraph = disconnectLayers(lgraph, featureExtractionLayer, 'res5a_branch1');
lgraph = disconnectLayers(lgraph, featureExtractionLayer, 'res5a_branch2a');

%Add ROI max pooling layer
OutputSize = [14 14]
roiPool = roiMaxPooling2dLayer(OutputSize, 'Name', 'roiPool');

lgraph = addLayers(lgraph, roiPool);

%connect feature extraction layer to roi max pooling layer
lgraph = connectLayers(lgraph, featureExtractionLayer, 'roiPool/in');

%connect the roi max pool layer to the previously disconnected layers
lgraph = connectLayers(lgraph,'roiPool', 'res5a_branch2a');
lgraph = connectLayers(lgraph, 'roiPool', 'res5a_branch1');

% Display the resulting graph 
figure
plot(lgraph)
ylim([30 42])

% Define anchor boxes.
anchorBoxes = [
    16 16
    32 16
    16 32
    ];

% Create the region proposal layer.
proposalLayer = regionProposalLayer(anchorBoxes,'Name','regionProposal');

lgraph = addLayers(lgraph, proposalLayer);

% Number of anchor boxes.
numAnchors = size(anchorBoxes,1);

% Number of feature maps in coming out of the feature extraction layer. 
numFilters = 1024;

rpnLayers = [
    convolution2dLayer(3, numFilters,'padding',[1 1],'Name','rpnConv3x3')
    reluLayer('Name','rpnRelu')
    ];

lgraph = addLayers(lgraph, rpnLayers);

% Connect to RPN to feature extraction layer.
lgraph = connectLayers(lgraph, featureExtractionLayer, 'rpnConv3x3');

% Add RPN classification layers.
rpnClsLayers = [
    convolution2dLayer(1, numAnchors*2,'Name', 'rpnConv1x1ClsScores')
    rpnSoftmaxLayer('Name', 'rpnSoftmax')
    rpnClassificationLayer('Name','rpnClassification')
    ];
lgraph = addLayers(lgraph, rpnClsLayers);

% Connect the classification layers to the RPN network.
lgraph = connectLayers(lgraph, 'rpnRelu', 'rpnConv1x1ClsScores');

% Add RPN regression layers.
rpnRegLayers = [
    convolution2dLayer(1, numAnchors*4, 'Name', 'rpnConv1x1BoxDeltas')
    rcnnBoxRegressionLayer('Name', 'rpnBoxDeltas');
    ];

lgraph = addLayers(lgraph, rpnRegLayers);

% Connect the regression layers to the RPN network.
lgraph = connectLayers(lgraph, 'rpnRelu', 'rpnConv1x1BoxDeltas');

% Connect region proposal network.
lgraph = connectLayers(lgraph, 'rpnConv1x1ClsScores', 'regionProposal/scores');
lgraph = connectLayers(lgraph, 'rpnConv1x1BoxDeltas', 'regionProposal/boxDeltas');

% Connect region proposal layer to roi pooling.
lgraph = connectLayers(lgraph, 'regionProposal', 'roiPool/roi');

% Show the network after adding the RPN layers.
figure
plot(lgraph)
ylim([30 42])

The training

data = load('objectgroundtruth.mat');
ObjectDataset(1:4,:);
I = imread(ObjectDataset.Imagefilename{10});
I = insertShape(I, 'Rectangle', ObjectDataset.Broom{10});
I = imresize(I,3);
figure;
imshow(I);
Layers = lgraph.Layers;
% Set random seed to ensure example training reproducibility.
rng(0);
%set training data

trainingData = ObjectDataset;
% Randomly split data into a training and test set.

shuffledIdx = randperm(height(ObjectDataset));
idx = floor(0.7 * height(ObjectDataset));
trainingData = ObjectDataset(shuffledIdx(1:idx),:);
testData = ObjectDataset(shuffledIdx(idx+1:end),:);


options = trainingOptions('sgdm', ...
      'MiniBatchSize', 1, ...
      'InitialLearnRate', 1e-3, ...
      'MaxEpochs', 10, ...
      'VerboseFrequency', 200, ...
      'CheckpointPath', tempdir);
  
[detector, info] = trainFasterRCNNObjectDetector(trainingData, Layers, options);

[Zohar Rimon]

Hello!

Did you end up solving this?

I am having the same error :(

SOLVED:

See solution in the Answers section

Answer 1

[Zohar Rimon]

You should change this line:

[detector, info] = trainFasterRCNNObjectDetector(trainingData, Layers, options);

To this one:

[detector, info] = trainFasterRCNNObjectDetector(trainingData, lgraph, options);