Why do the centroids have the same size even though a cutpoint of 2 was chosen for FOMO?

Help
#1

Question/Issue:
I understood FOMO to be a classification of regions for object detection, where the regions are used as bounding boxes. If I set the cutpoint to 2, the input image should be reduced by two and the regions/receptive fields should be 2x2 in size. So why are the drawn bounding boxes larger than 2x2? I noticed that the bounding boxes are larger in the live classifications function.

Context/Use case: I am trying to classify small objects that are very close together. I hope that by using small receptive fields, I can solve the issue of tightly packed objects.

#2

When BBs are adjacent to each other they get fused into one BB. See this also.

#3

Thank you for the prompt response. The fusion of bbox sounds plausible. In a YouTube video about FOMO, it was mentioned that adjacent regions that classify the same object are discarded. Therefore, I wondered why the centroids are not 2x2 in size.