Creating Fast R-CNN for Object Detection

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I am trying to create Fast-R-CNN for Object Detection, but I am taking error. I have 1 class on the dataset 90 sample.
imds = imageDatastore('C:\Users\', 'IncludeSubfolders',...
true, 'FileExtensions','.jpg','LabelSource','foldernames');
rng(0)
shuffledIndices = randperm(height(gTruthTBL));
idx = floor(0.6 * height(gTruthTBL));
trainingIdx = 1:idx;
trainingDataTbl = gTruthTBL(shuffledIndices(trainingIdx),:);
validationIdx = idx+1 : idx + 1 + floor(0.1 * length(shuffledIndices) );
validationDataTbl = gTruthTBL(shuffledIndices(validationIdx),:);
testIdx = validationIdx(end)+1 : length(shuffledIndices);
testDataTbl = gTruthTBL(shuffledIndices(testIdx),:);
imdsTrain = imageDatastore(trainingDataTbl{:,'imageFilename'});
bldsTrain = boxLabelDatastore(trainingDataTbl(:,'Unviable'));
imdsValidation = imageDatastore(validationDataTbl{:,'imageFilename'});
bldsValidation = boxLabelDatastore(validationDataTbl(:,'Unviable'));
imdsTest = imageDatastore(testDataTbl{:,'imageFilename'});
bldsTest = boxLabelDatastore(testDataTbl(:,'Unviable'));
trainingData = combine(imdsTrain,bldsTrain);
validationData = combine(imdsValidation,bldsValidation);
testData = combine(imdsTest,bldsTest);
data = read(trainingData);
inputSize = [224 224 3];
preprocessedTrainingData = transform(trainingData, @(data) preprocessData(data,inputSize));
net = resnet50;
lgraph = layerGraph(net);
% Remove the the last 3 layers from ResNet-50.
layersToRemove = {
'fc1000'
'fc1000_softmax'
'ClassificationLayer_fc1000'
};
lgraph = removeLayers(lgraph, layersToRemove);
% Specify the number of classes the network should classify.
numClasses = 1;
numClassesPlusBackground = numClasses + 1;
% Define new classification layers.
newLayers = [
fullyConnectedLayer(numClassesPlusBackground, 'Name', 'rcnnFC')
softmaxLayer('Name', 'rcnnSoftmax')
classificationLayer('Name', 'rcnnClassification')
];
% Add new layers.
lgraph = addLayers(lgraph, newLayers);
% Connect the new layers to the network.
lgraph = connectLayers(lgraph, 'avg_pool', 'rcnnFC');
% 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);
lgraph = connectLayers(lgraph,'avg_pool','rcnnBoxFC');
lgraph = disconnectLayers(lgraph, featureExtractionLayer,'res5a_branch2a');
lgraph = disconnectLayers(lgraph, featureExtractionLayer,'res5a_branch1');
% 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, 'activation_40_relu','roiPool/in');
% Connect the output of ROI max pool to the disconnected layers from above.
lgraph = connectLayers(lgraph, 'roiPool','res5a_branch2a');
lgraph = connectLayers(lgraph, 'roiPool','res5a_branch1');
roiInput = roiInputLayer('Name','roiInput');
lgraph = addLayers(lgraph, roiInput);
% Connect ROI input layer to the 'roi' input of the ROI max pooling layer.
lgraph = connectLayers(lgraph, 'roiInput','roiPool/roi');
netOptions = trainingOptions('sgdm', ...
'MaxEpochs',10,...
'InitialLearnRate',1e-3,...
'MaxEpochs',50, ...
'CheckpointPath',tempdir,...
'ValidationData',validationData,...
'MiniBatchSize',64, ...
'Plots','training-progress')
detector = trainFasterRCNNObjectDetector(trainingData,lgraph,netOptions, ...
'NegativeOverlapRange',[0 0.3], ...
'PositiveOverlapRange',[0.6 1]);
Error using vision.internal.cnn.validation.checkGroundTruthDatastore (line 32)
The read method of the training input datastore must return an M-by-3 cell or table.
Error in trainFasterRCNNObjectDetector>iParseInputs (line 1037)
params.ClassNames = vision.internal.cnn.validation.checkGroundTruthDatastore(trainingDs);
Error in trainFasterRCNNObjectDetector (line 408)
[trainingData, options, params] = iParseInputs(...
Caused by:
Error using checkGroundTruthDatastore
Expected input number 1, Read output of training datastore input, to be an array with number of
columns equal to 3.

Answers (1)

Shiva Kalyan Diwakaruni
Shiva Kalyan Diwakaruni on 3 May 2021
Hi,
For the issue 'The read method of the training input datastore must return an M-by-3 cell or table.'
Please refer to following documentation example to understand complete workflow of training object detection networks.
Hope it helps,
thanks.

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