YOLO26n RKNN inference speed on RK3588 (Orange Pi 5 Plus) + segmentation alternatives?

Discussion
1

Hi all,

I’m deploying YOLO26n (detect) on an Orange Pi 5 Plus (RK3588, 6 TOPS NPU) via RKNN export (end2end=False, per issue #23753) for a real-time edge use case with a ~500ms end-to-end latency budget (detection + tracking + embedding).

  1. Has anyone benchmarked real inference latency/fps for YOLO26n on RK3588/RK3588S via RKNN? Looking for actual on-device numbers (ms/frame for detect + NMS postprocessing), not just the 6 TOPS spec sheet.

  2. As I understand it, RKNN export currently only officially supports the detect task for YOLO26 (no seg/obb). Is that still accurate? If so, are there recommended alternatives for instance segmentation on this NPU — community RKNN export tooling, a different architecture, or anything else worth looking at?

Thanks!

2

Hello.

  1. There are benchmarks with FPS numbers on our docs page: YOLO26 RKNN Export for Rockchip NPU | Ultralytics
  2. That’s correct. Only detect models are currently supported. You can check this. It seems to support RKNN export for YOLO11-seg. ultralytics_yolo11/RKOPT_README.md at 0692e9297670acf4cc6d0cec773d7a9493cb8a5f · airockchip/ultralytics_yolo11 · GitHub