In the research paper, for S=7,B=2, the model predicts 2 bounding boxes for every 7x7 grid cell hence 7x7x2=98 images are predicted per image. Yet the demo output image only has 3 boxes. Why is that?
My theory is that since thickness of the lines is proportionate to the confidence scores of the bounding box, after the model is trained, the "lousy" bounding boxes are so thin that they don't even appear.
The paper also says "Often it is clear which grid cell an object falls in to and the network only predicts one box for each object".