• Hardware Platform Nvidia Tesla T4 • DeepStream Version 5.1
• TensorRT Version 7.2.2.3 • NVIDIA GPU Driver Version (valid for GPU only) 460.32…03 • Issue Type( questions, new requirements, bugs) question • How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
use primary-gie and secondary-gie with different detector models.
Hi can i run secondary-gie as detector on full frame? (keeping primary also on full frame)
and can i get tracker ID for secondary-gie detections? (My ans - if class ID is different from primary detections, then i can get tracker ID for secondary detections also…Is this correct?)
Note-
I have seen back-to-back detector, i want to implement in deepstream-app.c. So may be simple parameters can do that in config file?
2 instances of deepstream with 2 config files with separate detectors. But there is cost of decoder as for some sources, same frame has to be decoded in both the instances.
back-to-back detector approach, but there secondary detections are depending on primary detected objects. I just wanted to know if i can run secondary detection on full frame?
3rd approach is i can use gst-dsexample, and get the frame with get_converted_mat API and do the face detection over there. But get_converted_mat API utilise CPU and my overall performance reduced. Is there method to get frame in gst-dsexample.cpp with out CPU involvement? like get frame from GPU directly?
currently i am a bit confused. Like does secondary detection runs on every detected primary object? or primary-gie-id frame_meta(operate-on-gie-id)? as if it runs on primary-gie-id frame_meta that means it runs in full frame.
Still FPS goes bizarre when i use both as back-to-back detector. You can refer my post-
hi, i updated process-mode=1 in property group of secondary-gie.
But still no improvement on overall performance.
FYI-
4 streams with yolo_tiny gives 250 FPS each
4 streams with centerface only gives 200 FPS each.
4 streams with both model (as back-to-back detector yolo_tiny as primary-gie and centerface as secondary-gie) gives 30 FPS each. This is drastically low FPS.