First, I’m assuming that the patching is done to make PJ Reddies’s darknet-yolov3 to work with it. There was about a 15% performance loss with 8.0. I saw mention of some bug fixes, is one of them restoring the performance hit that darknet saw ? Cheers, Kim Hendrikse

Hi, Do you have a RTSP input? If yes, you can check the document below: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_ref_app_deepstream.html#source-group For output data, please check below sample for some ideas: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS…

Hi, There is a performance regression in cuDNN 8.0 on Nano. And our internal team is still working on it. But we don’t observe the similar regression on the XavierNX board. Do you also find the performance issue on cuDNN 8.0 + XavierNX? Thanks.

Well… with the original darknet from PJ Reddie I thought that the newer CUDNN didn’t compile without a patch. I tried the patch a long time ago when it first came out but then darknet didn’t start properly. I think it ran out of memory. At this stage I’m not sure which patch is the correct one to fi…

Hi, Please check this comment for the details: [image] Darknet compile error with cudnn 8 Jetson Xavier NX Hi, Here are the detail steps to run darknet on JetPack4.4 GA. 1. Clone source and apply cuDNN patch $ git clone https://github.com/pjreddie/darknet.g…

Thanks! I’ll try it and report back.

Unfortunately. This doesn’t work. I applied the patch and rebuilt using GPU=1 CUDNN=1 OPENCV=0 OPENMP=0 DEBUG=0 ARCH= -gencode arch=compute_72,code=sm_72 -gencode arch=compute_72,code=[sm_72,compute_72] But it loads really slowing and then dies before finishing with: 67 conv 1024 3 x 3 …

I’m trying to run full yolov3 with 608x608 image size

With memory usage as follows: tegrastats RAM 6659/7766MB (lfb 174x4MB) SWAP 31/3883MB (cached 0MB) CPU [53%@1420,8%@1420,0%@1420,0%@1420,16%@1420,2%@1420] EMC_FREQ 39%@1600 GR3D_FREQ 99%@1109 VIC_FREQ 0% @115 APE 150 MTS fg 0% bg 0% AO@26C GPU@27.5C PMIC@100C AUX@26C CPU@27.5C thermal@27.05C VDD_IN …

Hi, You are right. We can also reproduce this issue in our environment. Sorry that we are testing the YOLOv3 Tiny model before. We will check this internally and share more information with you later. Thanks.

Excellent! My new project to connect YOLO implementations to useful preventative security on jetson platforms is getting closer to release I hope it will be useful to a lot of people. I’m getting excited, but there’s still a little work left. In the meantime, it will support using AlexeyAB’s versio…

Hi, Thanks for the feedback. We didn’t notice the accuracy issue between these two repository. It seems lots of our user switch to AlexeyAB version after cuDNNv8 release for the update. We are still working on the YOLOv3 issue. Hope to give you a feedback soon. Thanks.

Does cuDNN 8.1 solve the performance problem when running with darknet yolov3?

Robotics & Edge Computing Jetson Systems Jetson Xavier NX

AastaLLL February 18, 2021, 8:41am 5

Hi,

Please check this comment for the details:

It should also work on the JetPack 4.5.

Thanks.

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Does cuDNN 8.1 solve the performance problem when running with darknet yolov3?

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