I’m in the process of researching real time object detection and tracking, preferably around about 30fps on HD footage or more.
I came across deepstream-yolo-app and the memory/cpu optimizations that it provides seems promising to improve performance over ‘plain’ yolo (https://github.com/pjreddie/darknet). However when i run deepstream-yolo-app the results aren’t much better than plain yolo.
I wanted to ask what kind of FPS you are getting with deepstream-yolo-app for yolov3 (full, non-tiny) on the Xavier.
I’ve also tried the https://github.com/AlexeyAB/darknet darknet fork which has support for Tensor Cores and Cuda Cores.On the Jetson Xavier i’m able to get about 20FPS using AlexeyAB’s Darknet. The deepstream-yolo-app seems much slower (i’m using the same file, 720p deepstream sample stream). But I don’t know how to accurately measure it (probably need to modify the code to measure fps?)
Because the deepstream yolo app uses all the Xavier hardware parts and is optimized to not use much CPU i figured i would be getting better results than with AlexeyAB’s code.
Small sidenote; when using AlexeyAB’s yolov3, FPS significantly improves when i minimize the video preview window which leads me to believe that they render that using CPU or something. Once minimized i get the 20fps i was talking about before. Maybe the same issue is happening with deepstream-yolo-app?
Any help figuring the FPS out or optimizing the deepstream app further would be much appreciated! I’ve been experimenting for weeks and i’m starting to get a little stuck