Removing invalid bounding boxes from datastore

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I am trying to use object detector training data create using the image data labeler to train a YOLOv2 model. I keep getting the error:
Error using vision.internal.cnn.validation.checkTrainingBoxes (line 12)
Training data from a read of the input datastore contains invalid bounding boxes. Bounding boxes must be non-empty, fully contained within their
associated image and must have positive width and height. Use datastore transform method and remove invalid bounding boxes.
All entries in the dataset have a corresponding bounding box in the correct format. Does anyone know how to do this as suggested?
% sort data
shuffledIndices = randperm(height(Data));
idx = floor(0.7 * length(shuffledIndices) );
trainingDataTbl = Data(shuffledIndices(1:idx),:);
testDataTbl = Data(shuffledIndices(idx+1:end),:);
% create image datastore
imdsTrain = imageDatastore(trainingDataTbl{:,'imageFilename'});
bldsTrain = boxLabelDatastore(trainingDataTbl(:,'Tip'));
imdsTest = imageDatastore(testDataTbl{:,'imageFilename'});
bldsTest = boxLabelDatastore(testDataTbl(:,'Tip'));
trainingData = combine(imdsTrain,bldsTrain);
testData = combine(imdsTest,bldsTest);
%% Create YOLOv2 network
% Input size for detector.
imageInputSize = [224 224 3];
% define classes
numClasses = width(Data)-1;
% estimate anchor boxes
numAnchors = 7;
trainingDataForEstimation = transform(trainingData,@(data)preprocessData(data,imageInputSize));
[anchorBoxes, meanIoU] = estimateAnchorBoxes(trainingDataForEstimation, numAnchors);
% define feature extraction network
featureExtractionNetwork = resnet50;
featureLayer = 'activation_40_relu';
lgraph = yolov2Layers(imageInputSize,numClasses,anchorBoxes,featureExtractionNetwork,featureLayer);
% train YOLOv2 object detector
options = trainingOptions('sgdm', ...
'MiniBatchSize', 16, ....
'InitialLearnRate',1e-3, ...
% augment data
augmentedTrainingData = transform(trainingData,@augmentData);
% Train the YOLO v2 detector
[detector,info] = trainYOLOv2ObjectDetector(trainingData,lgraph,options);

Answers (3)

ahmed shahin
ahmed shahin on 19 Mar 2020
please if you try to detect multi object and it is not necessary that each image have all classes
is this causes error?
i will appreciate your help
  1 Comment
wang Wang
wang Wang on 18 Oct 2021
I encountered this problem when detecting multiple objects,does anyone know how to solve it?

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Tunai Marques
Tunai Marques on 11 Oct 2019
Hi Kyle, I am facing a very similar problem. In my case, I can see exactly what augmented samples/BBs are giving me trouble.
Try running the following code to check the transformed data. One of them should have a funky bounding box (in my case, one of them did not have a BB at all after the transform).
Change "25" by the number of samples you want to check. "trainingData" is the 1x1 TransformedDatastore.
Hope that helps.
augmentedData = cell(25,1);
k = 1;
while hasdata(trainingData)
T = read(trainingData);
I = T{1};
bbox = T{2};
augmentedData{k} = insertShape(I,'Rectangle',bbox);
k = k+1;
  1 Comment
Madura Meenakshi Ramamoorthi
Hi Tunai Marques,
How did u remove that funky bounding box from trainingdata.

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Cong Dong Ngoc Minh
Cong Dong Ngoc Minh on 30 Jul 2020
Edited: Cong Dong Ngoc Minh on 30 Jul 2020
Hi Kyke,
Please check the bounding boxes whether it has '0' in coordinate. Due to MATLAB starts at '1' in the image, so that you can modify from '0' to '1' of the bounding box's coordinate. Scale it if it is larger than the image's size.
  1 Comment
Hamza Afzal
Hamza Afzal on 4 Feb 2021
I think 0 in bboxes doesnot make an error, since also in matlab personal .mat files, there were empty sets

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