u/n1k0n1k0n1

r/RockchipNPU r/computervision r/SBCs r/Ultralytics

Anyone tried YOLO26n-Depth on RK3576 or other SBCs?

I recently tried YOLO26n-Depth on an RK3576 SBC. Ultralytics officially supports exporting it to RKNN, so getting it running was pretty straightforward.

With a simple Python video inference test, I’m getting around 3–4 FPS for now. Nothing is really optimized yet, so I think there is still quite a bit of room for improvement.

Has anyone tried YOLO26-Depth on RK3576/RK3588, Raspberry Pi, Jetson, or other SBCs?

Curious what performance you’re getting and what optimizations made the biggest difference.

https://reddit.com/link/1vqgztz/video/la6mytoltujh1/player

reddit.com

u/n1k0n1k0n1 — 4 days ago

▲ 28 r/Ultralytics+2 crossposts

Tried YOLO26n-depth in RK3576

Since Ultralytics officially supports exporting YOLO26n-Depth to RKNN, I tried running it on RK3576.

With a simple Python video inference test, I’m getting around 3–4 FPS for now. The code and model haven’t been optimized yet, so I think there is still quite a lot of room for improvement.

One thing I noticed is that the RKNN model exported directly by Ultralytics has only one output.

For comparison, the optimized YOLO11 / YOLOv10 models from Rockchip’s rknn_model_zoo use 9 outputs, which seems to be better optimized for the Rockchip NPU and moves some of the decoding/post-processing outside the model.

So 3–4 FPS is just the initial result. I’m going to see if YOLO26n-Depth can be optimized in a similar way.

Has anyone already tried optimizing YOLO26 or YOLO26-Depth for RK3576/RK3588?

u/n1k0n1k0n1 — 4 days ago