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YOLOv5 output is not matching with DLStreamer openVINO python application

KarthickM
Beginner
547 Views

Hi all,

 

I have trained a model to detect seven-segment display numbers on yolov5 and I was able to convert to intel's IR model as well. Followed the following steps to convert:

 

python.exe yolov5\export.py --weights yolov5s640x.pt --include onnx --data customdata.yaml

 

python.exe mo.py --input_model yolov5s640x.onnx --model_name seven-segment-yolov5 --reverse_input_channels -s 255 --output /model.24/m.0/Conv,/model.24/m.1/Conv,/model.24/m.2/Conv

 

I have run the python based https://github.com/violet17/yolov5_demo (using openVINO 2021.4 )to test the IR model and the outputs are as same onnx model. Attaching the output (good) for reference.  

KarthickM_0-1673362213175.png

 

However, when I ran the same IR model through DLstreamer, the detection is not fine. Attaching the output (bad) for reference. 

KarthickM_1-1673362302407.png

I have tested in openVINO 2022 based DLstreamer as well but the results are the same.

I have used yolov5 + openVINO combination before and it all worked fine. 

 

I doubt the yolo parsing region either has bug or I am missing something. Attaching model proc I used. 

 

Any help would be welcome. Thank you.

 

Warm Regards,

Karthick 

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1 Solution
Peh_Intel
Moderator
348 Views

Hi Karthick,

 

Yes, I am able to duplicate the issue and getting the same bad inference result.

 

The bad inference result can be improved by resizing the image with aspect-ratio.  This can be done by defining “input_preproc” in the model-proc file.

 

Add the following parameter in “input_preproc”.

   "input_preproc": [

      {

          "format": "image",

          "layer_name": "images",

          "params":

          {

              "resize": "aspect-ratio"

          }

}

], 

 

The inference result looks nice now.

result.png

 

  

I attach the model-proc file as well.

 

 

Regards,

Peh

 

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15 Replies
Peh_Intel
Moderator
486 Views

Hi Karthick,


Support for Yolo-V5 was added in DL Streamer 2022.2 version:

https://github.com/dlstreamer/dlstreamer/releases/tag/2022.2-release


You can find a model-proc file now as part of the samples, here:

https://github.com/dlstreamer/dlstreamer/blob/master/samples/gstreamer/model_proc/public/yolo-v5.jso...



Please try to change the following parameters:

"converter": "yolo_v5",

 

"do_cls_softmax": true,

 



Regards,

Peh


KarthickM
Beginner
478 Views

Hi Peh, @Peh_Intel ,

 

Thank you for the response.
One thing I forgot to write was that I had tried the same model in 2022 version as you mentioned and the results were the same (the detection was terrible).

However, the python code (The link I provided above ) works without issue. 

 

I see the problem with only this model. I tried coco yolov5s IR converted model and that is working fine. 

 

Warm Regards,

Karthick

Peh_Intel
Moderator
447 Views

Hi Karthick,


In this case, we would like to request your model (yolov5s640x.onnx) for further investigation.

 

Besides, please also share with us on how you run IR model through DL Streamer for our ease to duplicate.

 

 

Regards,

Peh


KarthickM
Beginner
442 Views

Hi @Peh_Intel ,

 

This is the command I used to convert from pytorch to onnx. 

yolov5\export.py --weights yolov5s640x.pt --include onnx --data data.yaml --batch-size 1 --nms

 

mo.py --input_model yolov5s640x.onnx --model_name seven-segment-yolov5 --reverse_input_channels -s 255 --output /model.24/m.0/Conv,/model.24/m.1/Conv,/model.24/m.2/Conv

 

gst-launch-1.0 uridecodebin uri=file:/dsp/Dec-2022/TextDetection/inputMultimeter/n2.jpg ! queue ! gvadetect model=./seven-segment-yolov5.xml model-proc=./seven-segment-yolov5.json threshold=0.2 ! gvawatermark ! videoconvert ! jpegenc
! multifilesink location=./display4Orig1.jpeg

 

Please rename the files as below:

 

seven-segment-yolov5.txt ==> seven-segment-yolov5.json

yolov5s640x_Onnx.zip == > yolov5s640x.onnx

yolov5s640x_pt.zip ==> yolov5s640x.pt

 

Warm Regards,

Karthick

KarthickM
Beginner
438 Views

Hi @Peh_Intel ,

 

Please use the image I have used to reproduce the issue.

 

Warm regards,

Karthick

n2.jpg

Peh_Intel
Moderator
439 Views

Hi Karthick,

 

Thanks for sharing. Let me duplicate from my side first.

 

In a meanwhile, could you also share the test image as well?

 

 

Regards,

Peh


KarthickM
Beginner
433 Views

Hi @Peh_Intel ,

 

Please find the image. 

n2.jpg

Warm Regards,

Karthick

KarthickM
Beginner
371 Views

Hi @Peh_Intel ,

 

 

Are you able to reproduce the issue?

 

Warm Regards,

Karthick

Peh_Intel
Moderator
349 Views

Hi Karthick,

 

Yes, I am able to duplicate the issue and getting the same bad inference result.

 

The bad inference result can be improved by resizing the image with aspect-ratio.  This can be done by defining “input_preproc” in the model-proc file.

 

Add the following parameter in “input_preproc”.

   "input_preproc": [

      {

          "format": "image",

          "layer_name": "images",

          "params":

          {

              "resize": "aspect-ratio"

          }

}

], 

 

The inference result looks nice now.

result.png

 

  

I attach the model-proc file as well.

 

 

Regards,

Peh

 

KarthickM
Beginner
332 Views

Hi @Peh_Intel ,

 

Thank you very much. This has really helped us get the proper detection results.

 

The only thing I see is that the detected object's confidence result is 2 % down compared to python openVINO output, but that is expected I guess?

 

Thanks again for the quick responses. 

 

My result:

KarthickM_0-1673603818139.png

 

Warm Regards,

Karthick

Peh_Intel
Moderator
309 Views

Hi Karthick,


I am glad that I was able to help.


Yes, a little difference is expected. By the way, you can play a trick of adjusting the threshold value to obtain the optimal performance.



Regards,

Peh


KarthickM
Beginner
294 Views

Hi @Peh_Intel ,

 

Do you mean input_preproc parameters? I will adjust those parameters and see if I can improve the performance.

 

Warm Regards,

Karthick

Peh_Intel
Moderator
186 Views

Hi Karthick,


I actually mean adjust threshold value for gvadetect as I noticed that you only set threshold=0.2 ! in your previous gst-launch-1.0 command.



Regards,

Peh


KarthickM
Beginner
168 Views

Hi @Peh_Intel ,

 

 I kept threshold=0.2 just to see if there was any detection. I have already fine-tuned the threshold. Thanks for all the help.

 

Warm Regards,

Karthick

Peh_Intel
Moderator
145 Views

Hi Karthick,


This thread will no longer be monitored since this issue has been resolved. If you need any additional information from Intel, please submit a new question.



Regards,

Peh


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