improvements with openvino_tiny-yolov3_test.py about openvino-yolov3

[PINTO0309]

Please upgrade the version of OpenVINO to 2019 R1. #33

[naufil601]

@PINTO0309 Thanks for quick reply.
I believe there must be some issue with Myriad plugin of 2018 version.

But if I convert the model for CPU, it even then gives some False Positives with very high confidence. And when I test the same video with darknet, it gives no such False Positives.

Can you suggest some possible reasons for it ?

[PINTO0309]

I don't know exactly what you generated .pb, .bin, .xml, so I can't answer exactly.

1. Lack of training epoch
2. Incorrect definition of .cfg
3. Model conversion forgot the "--tiny" option
4. BGR to RGB, or RGB to BGR
5. mean value mistake
6. normalization mistake

[derek-zr]

Same result as you. I use 2019 R1 version, but still some false positives. See #32

[naufil601]

@derek-zr did you find any way out ?

[derek-zr]

@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 think the reason is the bin and xml conversion.

[naufil601]

Yes. Same results here.

[derek-zr]

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 we modify the test.py. So if i want to use the local video with a more high resolution, what should I modify the preprocess code? # @PINTO0309
Then I find that even though i use cpp version with my own model ,there are some false positives, i guess it shows that the original weights should retrain more epoch and be more accurate.

[derek-zr]

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 are displaced. Second, I wonder that if the preprocess to keep the aspect ration is necessary. Because I find that there are still a lot of false positives in my own model and the coco model is not so accurate.

[PINTO0309]

I recognize that there is an aspect rate bug.
Please modify the cpp program referring to the Python program.

[derek-zr]

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 cpp version don't have the same preprocess. But i find that even if i use the preprocess in python, there is still a lot of false positives in my own model.

[derek-zr]

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

[naufil601]

@derek-zr I converged my network to 0.5 loss and even then getting FP with this python script.

[derek-zr]

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.

[derek-zr]

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 bad results.

[ybloch]

I have the same issue, bad results with IR, good results with TF...
Did someone find a solution for this?