Darknet YOLO slowing down when using Jetpack4.4's cuDNN(8.0.0) on Jetson Xavier NX and Jetson Nano

Hi.

In general, FPS increases when using darknet Yolo’s cuDNN(8.0.0). However, in the new Jetpack 4.4, FPS is decreases when using cuDNN.

  1. Jetson Xavier NX
    nvpmodel -m2, jetson_clock
  • Used yolov3-tiny weights
    Not use cuDNN : 45.2 fps
    Use cuDNN : 40.4 fps

  • Used yolov3(320*320) weights
    Not use cuDNN : 8.5 fps
    Use cuDNN : 7.2 fps

  1. Jetson Nano
    nvpmodel -m0, jetson_clock
  • Used yolov3-tiny weights
    Not use cuDNN : 11.3 fps
    Use cuDNN : 6.6 fps

  • Used yolov3(320*320) weights
    Not use cuDNN : 3.1 fps
    Use cuDNN : 2.9 fps

  1. Jetson Nano (Use Jetpack 4.3, cuDNN 7.6.3)
    nvpmodel -m0, jetson_clock
  • Used yolov3-tiny weights
    Not use cuDNN : 11.3 fps
    Use cuDNN : 13.9 fps

I got the same results at GitHub - AlexeyAB/darknet: YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet ) as well as GitHub - pjreddie/darknet: Convolutional Neural Networks.

I hope this problem will be solved quickly.
Thanks.

1 Like

Hi,

We found that darknet run slower with cuDNN 8.0 but the root cause is still under investigation.
https://forums.developer.nvidia.com/t/121579

By the way, our DeepstreamSDK also has YOLO related sample.
It’s recommended to give it a try. Deepstream has optimized for the Jetson device.

/opt/nvidia/deepstream/deepstream-4.0/sources/objectDetector_Yolo/

Thanks.

Hi yacad,

I can only achieve to 7fps when test Jetson Xavier NX with yolov3-tiny weights. I see that you have achieved to 40.4 fps and 45.2 fps. could you give me instruction to achieve that?

I can only get 60fps,when i use deepstream_yolo3_tiny_int8 on the xavier nx。why is so slow? how get higher fps that above 100fps in many nvidia advertisment?

Hi chang-quan,

Please help to open a new topic if it’s still an issue to support.

Thanks