Multi objects detection problems - YOLOv2
2 views (last 30 days)
Show older comments
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.
5 Comments
Answers (0)
See Also
Categories
Find more on Recognition, Object Detection, and Semantic Segmentation in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!