How about the inference accuracy

[GallonDeng]

Hi, great work. I'm also trying to use yolov3 in openvino. Have you checked the inference accuracy for the converted yolov3 model? I trained my own dataset with new model(based on the structure of yolov3). It works fine in the darknet, but the inference accuracy drops a lot. I found that the conversion from darknet to tensorflow may have some problems. The converted tensorflow model performs badly for inference, the accuracy drops a lot. Have you tried other yolov3-based model, especially your own model?

[GallonDeng]

BTW, I use https://github.com/mystic123/tensorflow-yolo-v3.git

[PINTO0309]

@AllenDun

Have you checked the inference accuracy for the converted yolov3 model?

Unfortunately, I have not confirmed it in detail yet. However, it feels pretty inferior in terms of experience. For now, MobileNet-SSD is more accurate but a bit slow. By the way, it is the same situation as when I implemented tiny-YoloV2

It works fine in the darknet, but the inference accuracy drops a lot.

I am in the same situation as you.

Have you tried other yolov3-based model, especially your own model?

No, I have not tried it yet. First of all, I prioritized the establishment of a procedure to make it work.

BTW, I use https://github.com/mystic123/tensorflow-yolo-v3.git

I am using the same repository.

[deblauwetom]

Hi! I also tried the regular yolo v3 on a intel compute stick 1, by following the guide for creating the .pb file, and then using this compile command:

mo_tf.py --input_model yolo_v3.pb --tensorflow_use_custom_operations_config yolo_v3.json --input_shape "(1,413,413,3)" --data_type=FP16

I also compile it without the --data_type=FP16 and ran it on the CPU.

There is a difference in accuracy for sure, especially in the confidence scores. for example, i saw a difference with "sports ball" of 98% on fp32 vs 54% on fp16. Also, the bounding box was less accurate with fp16, but still ok. Some fp16 detections are rather around 10%, so definitely I would say that it is really different depending on where you will be doing the inference on!

[PINTO0309]

@AllenDun, @deblauwetom

Thank you for providing the information. I can not find a valid solution so far. :cry:

[ngeorgis]

Are you all using https://github.com/mystic123/tensorflow-yolo-v3 to convert from Darknet to IR? There are a few open issues that may explain the accuracy issues ( https://github.com/mystic123/tensorflow-yolo-v3/issues )

Have you tried another way to convert Darknet?

Cheers,

nikos

[PINTO0309]

• Reference isssue https://github.com/mystic123/tensorflow-yolo-v3/issues/50

Have you tried another way to convert Darknet?

I have not tried it yet. I think I will search for it.

[PINTO0309]

I began regenerating the model with reference to the following repository. So far, the loss value is steadily decreasing.

https://github.com/khanh1412/tiny-yolo-tensorflow.git

• ms-coco
• pure Tensorflow model

2, #4

[PINTO0309]

Hello everyone. I solved the problem of low precision. There was a mistake in the logic of preprocessing and postprocessing.

[ohUMR]

I meet the same problem. Have your modified the code of "openvino_tiny-yolov3_test.py" ? As my weights file has a good performence on cpu, but when comes to RaspberryPi with a Intel Neural Compute Stick v1, the accurracy is pretty slow. Still, thanks for your work!

[Jucjiaswiss]

Hello everyone. I solved the problem of low precision. There was a mistake in the logic of preprocessing and postprocessing.

Hi thanks for your work! How to fix this "logic of preprocessing and postprocessing" bug?

[andeyeluguo]

Have you really sovled this problem, someone says it is the resize of darknet is letterbox, but it has no this operation in openvino, I have no idea how to solve it.

[Jucjiaswiss]

Have you really sovled this problem, someone says it is the resize of darknet is letterbox, but it has no this operation in openvino, I have no idea how to solve it.

Yes, I used letter-box to resize after reading darknet's source code in the inference. But I haven't looked up the postporocessing code. Maybe you have to be the same with that when inference.

[andeyeluguo]

@Jucjiaswiss after use the letter-box resize ,have you solved it ?