YOLOv5 performance on Jetson Xavier AGX

Autonomous Machines Jetson & Embedded Systems Jetson AGX Xavier
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Question

Hi,
I am working on Nvidia Jetson Xavier AGX.
While using https://github.com/ultralytics/yolov5 repository as is, latest version with yolov5s model.
When running python3 detect.py --source 0 --img-size 640 inference time is 0.040s.
When running python3 detect.py --source 0 --img-size 256 inference time is 0.025s.

I would be happy to know if those results seem reasonable or am I missing something**

Additional context

OS: Ubuntu 18.04 (JetPack)
OPERATION MODE: MAXN (Maximum performance)
Python: 3.6
Pytorch: 1.6.0 ( according to this topic )
CUDA: 10
Camera: 120 FPS camera (so I wish to achieve this inference FPS)

Thanks!

3

Hi,

We don’t have a performance result for YOLOv5.
Here are some result for YOLOv3, you can compare it based on the complexity of the model:

https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream_Development_Guide/deepstream_performance.html#wwpID0E0YD0HA

Thanks.