Troubleshooting

Q: Why is the performance on CPU sometimes better than on GPU, when running pipeline benchmarking like stream density?

A: The performance of pipeline benchmarking strongly depends on the models. Specifically for yolov5s object detection, it is recommended to use the model precision FP32 when it is running on device GPU. If supported, then you can change the model precision by updating the pipeline script to point to the precision of your choice. For example, you can change the model of FP16 to FP32 assuming the precision FP32 of the target model is available:

    src/pipelines/yolov5s.sh

    ...
    model=models/object_detection/yolov5s/FP16-INT8/yolov5s.xml
    ...

Q: What happens if the system keeps crashing when building the dlstreamer-realsense image?

A: Some systems may run into issues with memory when building the dlstreamer-realsense image. In the Dockerfile.dlstreamer file, change the make command to not use the -j threading option.

- make -j"$(cat < /proc/cpuinfo |grep -c proc)" &&
+ make &&

Q: What happens if the RTSP source is not found?

A: Depending on your systems performance it is possible that the RTSP simulator may take additional time to initialize and start streaming. To avoid issues you can add a waiting period before the pipeline starts. For example you can add a 5 second sleep timer to /src/entrypoint.sh

sleep 5 # sleep for 5 seconds before starting the pipeline
eval $gstLaunchCmd