integrations/rockchip-rknn/ #18812
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👋 Hello, thank you for starting this discussion about Rockchip RKNN integration 🚀! We appreciate your interest in leveraging YOLO models for efficient deployment on Rockchip platforms. We recommend checking out the integration guide for Rockchip-RKNN for step-by-step instructions on exporting and deploying YOLO models to RKNN format. If this is a 🐛 Bug Report regarding the RKNN integration, could you please provide a minimum reproducible example if not already available? This will help us better diagnose and address any issues. For general questions about custom deployments or usage ❓, please share more information on your setup, including system specifications, any modifications you’ve made, and the complete logs of any errors encountered. Be sure to verify that your environment is configured correctly by following the Requirements and upgrading to the latest version of the pip install -U ultralytics Join our thriving Ultralytics community for additional resources and support: YOLO can also be tested or deployed in verified environments with preinstalled dependencies:
Lastly, if the CI badge is green, this confirms all tests are successfully passing and the latest code is verified across all platforms. This is an automated response 🤖, and an Ultralytics engineer will review this discussion and provide further assistance soon. Let us know how we can help further—happy coding! 🚀 |
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@IvorZhu331 |
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Hello, |
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integrations/rockchip-rknn/
Learn how to export YOLO11 models to RKNN format for efficient deployment on Rockchip platforms with enhanced performance.
https://docs.ultralytics.com/integrations/rockchip-rknn/
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