Training data from a read of the input datastore contains invalid bounding boxes?

Hello guys i'm trying this code and i don't know what to do to fix the problem

data = load('Damagevehicle.mat');
trainingData = data.Damaaaage;
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
imds = imageDatastore(trainingData.imageFilename);

blds = boxLabelDatastore(trainingData(:,2:end));
ds = combine(imds, blds);
net = load('yolov2VehicleDetector.mat');
lgraph = net.lgraph;
lgraph.Layers;
options = trainingOptions('sgdm','InitialLearnRate',0.001,...
    'Verbose',true,'MiniBatchSize',32,'MaxEpochs',30,...
    'Shuffle','never','VerboseFrequency',30,...
    'CheckpointPath',tempdir);
[detector,info] = trainYOLOv2ObjectDetector(ds,lgraph,options);

The error i got:

Invalid transform function defined on datastore.

The cause of the error was:

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.

Error in vision.internal.cnn.yolo.validateImagesAndBoxesTransform (line 22)

vision.internal.cnn.validation.checkTrainingBoxes(images, boxes);

Error in

trainYOLOv2ObjectDetector>@(data)vision.internal.cnn.yolo.validateImagesAndBoxesTransform(data,params.InputSize) (line

285)

transformFcn = @(data)vision.internal.cnn.yolo.validateImagesAndBoxesTransform(data,params.InputSize);

Error in matlab.io.datastore.TransformedDatastore/applyTransforms (line 607)

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

Error in matlab.io.datastore.TransformedDatastore/read (line 222)

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

Error in nnet.internal.cnn.DataLoader/manageReadQueue (line 182)

data = read(self.Datastore);

Error in nnet.internal.cnn.DataLoader/readAhead (line 213)

manageReadQueue(self);

Error in nnet.internal.cnn.DataLoader (line 81)

readAhead(self);

Error in nnet.internal.cnn.GeneralDatastoreDispatcher (line 275)

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

Error in nnet.internal.cnn.DataDispatcherFactory.createDataDispatcherMIMO (line 186)

nnet.internal.cnn.GeneralDatastoreDispatcher( ...

Error in vision.internal.cnn.trainNetwork>iCreateTrainingDataDispatcher (line 208)

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

Error in vision.internal.cnn.trainNetwork (line 46)

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

Error in trainYOLOv2ObjectDetector>iTrainYOLOv2 (line 434)

[yolov2Net, info] = vision.internal.cnn.trainNetwork(...

Error in trainYOLOv2ObjectDetector (line 198)

[net, info] = iTrainYOLOv2(ds, lgraph, params, mapping, options, checkpointSaver);

Error in final1 (line 17)

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

Answer 1

[Vidip]

In the case of valid bounding boxes, they must be non-empty, fully contained within their associated image and must have positive width and height.

The function helperSanitizeBoxes is used to clean up invalid bounding box data. Boxes with values <=0 are removed, and fractional values are rounded to integers. If none of the boxes are valid, this function passes the data through to enable downstream processing to issue proper errors.

You can refer to this MATLAB Answer for more information related to this topic:

• https://in.mathworks.com/matlabcentral/answers/1462499-helpersanitizeboxes-is-a-function-provided-in-some-object-detection-examples-but-is-not-a-built-in-f#answer_801004