What's the output features shape for regression? about keras-centernet

[see--]

Both are wrong. Read the code or the paper. You have 3 outputs. Centers are （N，128，128，80).

[becauseofAI]

@see--
Centers are （N，128，128，80)
W and H of Centers are（N，128，128，2)
Offset of Centers are（N，128，128，2)

So only one target box can be predicted for the same or different categories with overlapping centers?

For example, a cat and an elephant coincide at the center, but their sizes vary greatly. But the center overlap of a cat and an elephant can only predict one category of elephant or cat, but can not simultaneously predict elephant and cat?

[see--]

The paper has some great sections answering your questions. In short: You are right. But the important part is that it rarely happens. You have much fewer collisions/lost boxes with CenterNet than with any other approach.