How to train a Faster R-CNN with a Multi-labeled images table?

I have a 8792x11 table with my dataset labeled with the Image Labeler:

1st column: imageFilename;

from 2nd to 11th column: different labels with the rectangles' dimensions, exactly as how the ground-truth dataset must be for Faster R-CNN training.

But MathWorks' examples show how to train the network with a one-labeled dataset.

How to train the net with a multi-labeled ground-truth table?

Thank you.

Answer 1

[Jack Xiao]

what does multi-labeled ground-truth mean? the MathWorks' rcnn example is for object detection with mutilple instances but one-labeld ground-truth.