I have installed DeepStream on my laptop using WSL2 and am trying to test my custom YOLOv8 model in a CARLA environment. In this setup, a CARLA RGB camera is attached to a vehicle, and the feed from this camera is sent to DeepStream via RTSP using GStreamer. Previously, I used an ONNX model that was converted to an engine file, following the DeepStream-YOLO procedure, and it worked fine. However, after optimizing the YOLO model and training it with additional datasets, I encountered distortion when using the new model.
I tried reducing resolution,fps,batched-push-timeout and tried x265 encoding still issue persists.
I will attach my deepstream_app_config.txt and there is a python script for whole setup of carla side but iam posting below gstreamer pipeline for carla setup only: deepstream_app_config.txt (830 Bytes)
appsrc name=mysource is-live=true block=true format=GST_FORMAT_TIME
caps=video/x-raw,format=BGR,width={self.width},height={self.height},framerate={self.fps}/1 !
videoconvert !
video/x-raw,format=I420 !
x264enc speed-preset=ultrafast tune=zerolatency !
rtph264pay config-interval=1 pt=96 name=pay0
Here width= 1280, height= 720 and fps=30.
Also rgb camera from carla also captures in same height and width.
Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) • DeepStream Version • JetPack Version (valid for Jetson only) • TensorRT Version • NVIDIA GPU Driver Version (valid for GPU only)
when i checked with htop ,There are multiple pids for deepstream-app -c deepstream-app-config.txt command, 2 pids uses most of cpu almost 93 by each of them other pids uses like 4 and 1
It seems in wsl deepstream is not using gpu only cpu, when i checked with jetson nano device there is no distortion, it is using around 15 of cpu only . But there is frame drop, it was there before also when i used old model.
Solved the problem. Before i exported the pt model to onnx model with batch 32 . When i converted the into onnx model with batch 1 problem was solved . There is no more distortion and also work properly in jetson nano.