Derek Zheng

Same result as you. I use 2019 R1 version, but still some false positives. See https://github.com/PINTO0309/OpenVINO-YoloV3/issues/32

> @derek-zr did you find any way out ? Still trying. I test the pb model, the result is good. But the IR model have many false positives. So i...

> Yes. Same results here. Seems I find the reasons. I try cpp script with coco weights and the result is pretty good. So I guess there's some problems when...

After some experiments, I find some reasons. First, for image aspect ratio that are not 1:1, the draw location calculate may be wrong, so there will be some boxes which...

> I recognize that there is an aspect rate bug. > Please modify the cpp program referring to the Python program. Thanks for your reply. I also find that the...

After a longer training process ,the new model still performs bad. And pb model performs great, but IR model produce many false positives.

yeah. The loss of mine is even lower but still some FP and the detection results is bad. I think it's a bug in the intel conversion python code.

Have you solved it? One author from the intel forum says that it may be the bug in the logistic layer code. https://software.intel.com/en-us/forums/computer-vision/topic/808504#comment-1938506 I try to change the code,but still...

The confidence threshold is same. Both are 0.5

> You can update openvino version to 2019,and try it again It's already the 2019 version. 2019.1.094