How to avoid groupRectangles, boundingboxes and get only class probabilty map

What should I do, if i want to get that output before the clustering Python Laye i dont want to get back boundigboxes. (How to get “real-valued multi-dimensional array that can be overlaid on the image, much like the DetectNet input labels described above”, how to avoid Python Layer that run groupRectangles funciton?)


Thanks for your question.

May I know which model and inference framework you used?
If you using DetectNet with tensorRT, please remove last python layer and use bboxes and coverage as output.

Thanks for the answer, and the coverage output is a value for each grid (between 0-1) and tell the the likelihood of the class present or not in the grid or in bbox? If it tell in the bbox, how can i get the likelihood of the class present in the grid?


Thanks for your feedback.

Bbox contains detail location information while coverage is the object probability.
If you are interested in likelihood, please use coverage.

Thanks for your answer!