Multi objects detection problems - YOLOv2

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Hi,

I would like to use YOLOv2, for detecting differents classes (20 to be exact, but I'm going to start with 2): airplane and ship. When I train with just one class there is no problem, I can detect all airplane testing images. The problem is when I add a second class (ship). When I add this class I cant't detect airplanes or ships. It trains but no detects. Do you know why?

I'm using 854 images for airplanes and 1701 for ships.

I've following the official tutorial: https://www.mathworks.com/help/deeplearning/ug/object-detection-using-yolo-v2.html, but it only use one class, as all example I've found.

Here is my code:

inputSize = [400 400 3];

doTraining = true;

classes = ["airplane","ship"];

pathToImages = 'path';

images = imageDatastore(pathToImages, 'IncludeSubfolders',true);

annotations = images.Files(1:end);

for i=1:length(annotations)

file = strrep(char(annotations(i)),"images","annotations");

file = strrep(file,"jpg","txt");

class = split(file,"\");

position = (find(contains(classes,class))) + 1;

annotations(i,position) = {load(file)};

end

annotations = cell2table(annotations,'VariableNames',{'imageFilename' 'airplane' 'ship'});

rng(0);

shuffledIndices = randperm(height(annotations));

idx = floor(0.6 * length(shuffledIndices) );

trainingIdx = 1:idx;

trainingDataTbl = annotations(shuffledIndices(trainingIdx),:);

validationIdx = idx+1 : idx + 1 + floor(0.1 * length(shuffledIndices) );

valDataTbl = annotations(shuffledIndices(validationIdx),:);

imdsTrain = imageDatastore(trainingDataTbl{:,'imageFilename'});

bldsTrain = boxLabelDatastore(trainingDataTbl(:,2:end));

imdsVal = imageDatastore(valDataTbl{:,'imageFilename'});

bldsVal = boxLabelDatastore(valDataTbl(:,2:end));

trainingData = combine(imdsTrain,bldsTrain);

valData = combine(imdsVal,bldsVal);

data = read(trainingData);

I = data{1};

bbox = data{2};

annotatedImage = insertShape(I,'Rectangle',bbox);

annotatedImage = imresize(annotatedImage,2);

figure

imshow(annotatedImage)

numClasses = length(classes);

trainingDataForEstimation = transform(trainingData,@(data)preprocessData(data,inputSize));

numAnchors = 7;

[anchorBoxes, meanIoU] = estimateAnchorBoxes(trainingData, numAnchors)

featureExtractionNetwork = resnet50;

featureLayer = 'activation_40_relu';

lgraph = yolov2Layers(inputSize,numClasses,anchorBoxes,featureExtractionNetwork,featureLayer);

augmentedTrainingData = transform(trainingData,@augmentData);

preprocessedTrainingData = transform(augmentedTrainingData,@(data)preprocessData(data,inputSize));

data = read(preprocessedTrainingData);

options = trainingOptions('sgdm',...

'MiniBatchSize', 16,...

'InitialLearnRate',1e-3,...

'MaxEpochs',20,...

'CheckpointPath',tempdir,...

'Shuffle','never');

if doTraining

% Train the YOLO v2 detector.

[detector,info] = trainYOLOv2ObjectDetector(preprocessedTrainingData,lgraph,options);

else

pretrained = load('yolov2ResNet50VehicleExample_19b.mat');

detector = pretrained.detector;

end

% Detector that not detects

I = imread('pathToAirPlaneTestImage);

[bboxes,scores] = detect(detector,I);

if ~isempty(bboxes)

I = insertObjectAnnotation(I,'rectangle',bboxes,scores);

figure

imshow(I)

end

Thank you.

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Oscar Lema 2020-11-22

While training or before? I trainned with that table (later I create DataStores). Maybe your error is that you've incorrent BoundingBoxes, I mean a 416x416 image and a [400 400 50 50] BoundingBox, so xmax and ymax would be 450 and 450. This maybe your problem, becasuse you have a 416x416 image so it's imposible to have [xmax ymax] = [450 450].

Are you using the correct BoundingBox format [xmin ymin with height]?

Oscar Lema 2020-11-22

ds.mat

I attach the test DataStore. The train DataStore should follow the same format (just changing data). You can load it on Matlab and see its structure. It's working for me.

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