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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