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ultralytics 8.3.65 Rockchip RKNN Integration for Ultralytics YOLO models (#16308)

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Signed-off-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Lakshantha Dissanayake <lakshantha@ultralytics.com>
Co-authored-by: Burhan <Burhan-Q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Lakshantha Dissanayake <lakshanthad@yahoo.com>
Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/integrations/index.md
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@ -95,6 +95,8 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
- [SONY IMX500](sony-imx500.md): Optimize and deploy [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) models on Raspberry Pi AI Cameras with the IMX500 sensor for fast, low-power performance.
- [Rockchip RKNN](rockchip-rknn.md): Developed by [Rockchip](https://www.rock-chips.com/), RKNN is a specialized neural network inference framework optimized for Rockchip's hardware platforms, particularly their NPUs. It facilitates efficient deployment of AI models on edge devices, enabling high-performance inference in real-time applications.
### Export Formats
We also support a variety of model export formats for deployment in different environments. Here are the available formats: