Megvii-BaseDetection
Overlapping Object Detection
Hi,
I've trained YOLOX on custom data and get good performance with various YOLOX architectures. However my output is always constrained such that objects with overlapping bounding boxes always only have one detection.
Is it possible to modify the network head to be able to generate multiple overlapping bounding box predictions? (particularly for objects of the same class very close together or partially occluding each other)
Thanks!
@FateScript Thanks for your reply. Sorry to ask but exactly how do I use that function? Is it different to just setting class_agnostic=True when calling post_process() e.g. in demo.py? (as below)
You may import demo_postprocessing from yolox.utils.demo_utils and just simply replace it with postprocess.
@FateScript But arguments are different to each? And they seem to work completely differently..?
def postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45, class_agnostic=False):
def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr):
@ajwl27 sorry for not noticing your reply and in postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45, class_agnostic=False), setting class_agnostic=True could help. I mis-remember sth.
@ajwl27 hi, what's the result about the overlapped objects? i have met the same question with you. And @FateScript I want to know have you verify whether the simota sovle the overlapped objects label assignment? Currently, i am sovling the overlapped objects detection, and i think simota is a good idea, but it seems does not work when i try yolox.
@rOtking We haven't tried YOLOX on overlapped object detection task. However, someone try yolox on crowd-human dataset and get a better result.
@FateScript Thank you for your replying!!! And Do i need 300 epoch for a good performance? In order to get a quick verification i just trained 80 epoch. In theory, do you think simota is a good way to sovle overlapped objects label assignmnet?