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Ultralytics Officially Announces Support for RKNN

· 2 min read

Recently, Ultralytics officially announced its support for the RKNN platform. From now on, users of RK3588/356X series products can easily complete the model conversion and deployment of yolov11 by simply using the ultralytics library, pressing the "accelerate button" for the practical application of computer vision technology.

In this technological innovation, Radxa's star products, Radxa Rock 5B and Radxa Zero 3W, have stood out. As the core test platforms, they have provided a solid guarantee for the deployment and testing of the Ultralytics yolov11 model. Rock 5B is equipped with the high - performance Rockchip RK3588 processor, and Zero 3W is equipped with the powerful Rockchip RK3566 processor. With their excellent performance, stable performance, and strong compatibility, they have become the hardware cornerstone of technological breakthroughs.

YOLOv11 Inference on Board

RKNN Label on Board

For a long time, the complex processes of model conversion and deployment and hardware adaptation problems in the computer vision field have seriously restricted the promotion of technology. This official support of Ultralytics for the RKNN platform, combined with the successful tests based on Radxa products, has completely overcome this difficulty, making the implementation of technology more efficient.

RKNN Toolkit​

The RKNN Toolkit, developed by Rockchip, was crucial in exporting the Ultralytics YOLO11 model to RKNN. This toolkit, a set of professional tools for deep - learning model deployment on Rockchip hardware, features the RKNN format. Optimized for Rockchip's NPU, RKNN unlocks full hardware acceleration on devices like RK3588 and RK3566, ensuring high - performance AI task execution.

Rockchip RKNN

The RKNN model offers many unique benefits. Its NPU - optimized design maximizes performance on Rockchip's NPU. Its low - latency trait suits real - time edge - device apps. Also, it can be customized for different Rockchip platforms, enhancing hardware resource use and overall efficiency.

For more details​

For more details, see the Rockchip RKNN Export for Ultralytics YOLO11 Models and Radxa Docs.