Error while training SSD object detector

i am using MATLAB 2020a to train SSD object detetctor for my database.I receive error which i am unable to troubleshoot. please help me out.

here is the code:

vehicleDataset1=vehicleDataset1.wbc;

rng(0);

shuffledIndices1 = randperm(height(vehicleDataset1));

idx1 = floor(0.6 * length(shuffledIndices1) );

trainingData1 = vehicleDataset1(shuffledIndices1(1:idx1),:);

testData1 = vehicleDataset1(shuffledIndices1(idx1+1:end),:);

imdsTrain1 = imageDatastore(trainingData1{:,'filename'});

bldsTrain1 = boxLabelDatastore(trainingData1(:,'wbc'));

imdsTest1 = imageDatastore(testData1{:,'filename'});

bldsTest1 = boxLabelDatastore(testData1(:,'wbc'));

trainingData1 = combine(imdsTrain1,bldsTrain1);

testData1 = combine(imdsTest1, bldsTest1);

data1 = read(trainingData1);

I1 = data1{1};

bbox1 = data1{2};

annotatedImage1 = insertShape(I1,'Rectangle',bbox1);

annotatedImage1 = imresize(annotatedImage1,2);

figure

imshow(annotatedImage1)

inputSize1 = [300 300 3];

numClasses1 = width(vehicleDataset1)-1;

lgraph1 = ssdLayers(inputSize1, numClasses1, 'resnet50');

augmentedTrainingData1 = transform(trainingData1,@augmentData);

augmentedData1 = cell(4,1);

for k = 1:4

data1 = read(augmentedTrainingData1);

augmentedData1{k} = insertShape(data1{1},'Rectangle',data1{2});

reset(augmentedTrainingData1);

end

figure

montage(augmentedData1,'BorderSize',10)

preprocessedTrainingData1 = transform(augmentedTrainingData1,@(data)preprocessData(data,inputSize1));

data1 = read(preprocessedTrainingData1);

I1 = data1{1};

bbox1 = data1{2};

annotatedImage1 = insertShape(I1,'Rectangle',bbox1);

annotatedImage1 = imresize(annotatedImage1,2);

figure

imshow(annotatedImage1)

options = trainingOptions('sgdm',...

'InitialLearnRate',5e-5,...

'MiniBatchSize',16,...

'Verbose',true,...

'MaxEpochs',50,...

'Shuffle','every-epoch',...

'VerboseFrequency',10,...

'CheckpointPath',tempdir);

[detector1,info1] = trainSSDObjectDetector(preprocessedTrainingData1,lgraph1,options);

data1 = read(testData1);

I1 = data1{1,1};

I1 = imresize(I1,inputSize1(1:2));

[bboxes1,scores1] = detect(detector1,I1, 'Threshold', 0.4);

I1 = insertObjectAnnotation(I1,'rectangle',bboxes1,scores1);

figure

imshow(I1)

here is error:

Invalid transform function defined on datastore.

The cause of the error was:

Error using bboxwarp>iParseInputs (line 324)

The value of 'bboxA' is invalid. Expected input number 1, bboxA, to be integer-valued.

Error in bboxwarp (line 81)

params = iParseInputs(bboxA,tform,ref,varargin{:});

Error in augmentData (line 23)

[B{2},indices] = bboxwarp(A{2},tform,rout,'OverlapThreshold',0.25);

Error in matlab.io.datastore.TransformedDatastore/applyTransforms (line 489)

data = ds.Transforms{ii}(data);

Error in matlab.io.datastore.TransformedDatastore/read (line 162)

[data, info] = ds.applyTransforms(data, info);

Error in nnet.internal.cnn.DataLoader/manageReadQueue (line 161)

data = read(self.Datastore);

Error in nnet.internal.cnn.DataLoader/readAhead (line 192)

manageReadQueue(self);

Error in nnet.internal.cnn.DataLoader (line 80)

readAhead(self);

Error in nnet.internal.cnn.GeneralDatastoreDispatcher (line 272)

this.DataLoader = nnet.internal.cnn.DataLoader(ds,...

Error in nnet.internal.cnn.DataDispatcherFactory.createDataDispatcherMIMO (line 170)

nnet.internal.cnn.GeneralDatastoreDispatcher( ...

Error in vision.internal.cnn.trainNetwork>iCreateTrainingDataDispatcher (line 190)

dispatcher = nnet.internal.cnn.DataDispatcherFactory.createDataDispatcherMIMO( ...

Error in vision.internal.cnn.trainNetwork (line 40)

trainingDispatcher = iCreateTrainingDataDispatcher(ds, mapping, trainedNet,...

Error in trainSSDObjectDetector (line 233)

[network, info] = vision.internal.cnn.trainNetwork(...

Error in wbc_detetctor_traininga (line 61)

[detector1,info1] = trainSSDObjectDetector(preprocessedTrainingData1,lgraph1,options);

Answer 1

[Divya Gaddipati]

Make sure your groundtruths are valid and non-empty i.e., the values of the bounding boxes are finite, positive, non-fractional, non-NaN and should be within the image boundary with a positive height and width. You should either discard or fix the samples with invalid bounding boxes.

[Madura Meenakshi Ramamoorthi]

Hi, Can you please tell how to discard invalid bounding boxes

[Huma Hafeez]

Thanks for your reply. You are right, there was a problem with ground truth