docs: add ultralytics yolo11 rknn zh/en usage

signed-off-by: "Morgan ZHANG" <zhangzifeng@radxa.com>

docs/common/dev/_rknn-ultralytics.mdx

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+:::tip
+本文档旨在演示如何在 rk3588/356X 上推理 YOLOv11 目标检测模型，所需环境配置请参考[ RKNN 安装](./rknn_install)
+:::
+
+目前 [Ultralytics](https://docs.ultralytics.com/integrations/rockchip-rknn/) 官方已经支持 rknn 平台，RK3588/356X 产品用户可以直接使用 `ultralytics` 库进行 yolov11 的模型转换和模型部署。
+
+## PC 端模型转换
+
+**Radxa 已提供预转换好的 `yolov11n.rknn` 模型，用户可直接参考[板端推理 YOLOv11 ](#板端推理-yolov11)跳过 PC 端模型转换章节**
+
+- 安装最新版本的 ultralytics
+  ```bash
+  pip3 install -U ultralytics
+  ```
+- 使用 ultralytics 导出 yolov11 rknn 格式模型,
+
+    <Tabs>
+
+        <TabItem value="CLI">
+
+        ```bash
+        # 'name' can be one of rk3588, rk3576, rk3566, rk3568, rk3562, rv1103, rv1106, rv1103b, rv1106b, rk2118
+        yolo export model=yolo11n.pt format=rknn name=rk3588
+        ```
+
+        </TabItem>
+
+        <TabItem value="Python">
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load the YOLOv11 model
+        model = YOLO("yolo11n.pt")
+
+        # Export the model to RKNN format
+        # 'name' can be one of rk3588, rk3576, rk3566, rk3568, rk3562, rv1103, rv1106, rv1103b, rv1106b, rk2118
+        model.export(format="rknn", name="rk3588")  # creates '/yolo11n_rknn_model'
+        ```
+
+        </TabItem>
+
+    </Tabs>
+
+  结果保存在 ./yolo11n_rknn_model
+
+- 将 **yolo11n_rknn_model** 目录拷贝到板端
+
+## 板端推理 YOLOv11
+
+:::tip
+RK356X 芯片用户使用 NPU 前需要在终端使用 **rsetup** 开启 NPU: sudo rsetup -> overlays -> Enable NPU，最后重启系统。
+
+如 overlays 选项中无 `Enable NPU` 选项，请通过: sudo rsetup -> system -> System Update 升级系统, 重启后执行上述步骤开启 NPU。
+:::
+
+- （可选）下载 radxa 准备的 yolov11n rknn 模型
+  | 平台 | 下载链接 |
+  | -------- | ------------------------------------------------------------ |
+  | rk3566 | [yolo11n_3566_rknn_model](https://github.com/zifeng-radxa/rknn_model_zoo/releases/download/yolov11/yolo11n_3566_rknn_model.zip) |
+  | rk3568 | [yolo11n_3568_rknn_model](https://github.com/zifeng-radxa/rknn_model_zoo/releases/download/yolov11/yolo11n_3568_rknn_model.zip) |
+  | rk3588 | [yolo11n_3588_rknn_model](https://github.com/zifeng-radxa/rknn_model_zoo/releases/download/yolov11/yolo11n_3588_rknn_model.zip) |
+
+- 在虚拟环境下安装最新版本的 ultralytics
+
+  虚拟环境安装请参考 [Python 虚拟环境使用](venv_usage)
+
+  ```bash
+  pip3 install -U ultralytics
+  ```
+
+- 板端推理
+
+      <Tabs>
+          <TabItem value="CLI">
+          ```bash
+          yolo predict model='./yolo11n_rknn_model' source='https://ultralytics.com/images/bus.jpg'
+          ```
+          </TabItem>
+          <TabItem value="Python">
+          ```python
+          from ultralytics import YOLO
+
+          # Load the exported RKNN model
+          rknn_model = YOLO("./yolo11n_rknn_model")
+
+          # Run inference
+          results = rknn_model("https://ultralytics.com/images/bus.jpg")
+          ```
+          </TabItem>
+      </Tabs>
+
+      结果保存在 runs/detect/predict 中
+
+<img width="400" src="/img/general-tutorial/rknn/yolov11.webp" />
+
+## 更多使用细节
+
+更多 Ultralytics 使用细节请参考 [Ultralytics](https://docs.ultralytics.com/) 官方文档

docs/compute-module/cm3/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/compute-module/cm3i/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/compute-module/cm5/radxa-os/app-dev/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/compute-module/nx5/radxa-os/app-dev/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock3/e25/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock3/rock3a/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock3/rock3b/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock3/rock3c/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock5/rock5a/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock5/rock5b/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock5/rock5c/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock5/rock5itx/app-development/rknn_ultralytics.md

@@ -0,0 +1,10 @@
+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock5/rock5t/app-development/rknn_ultralytics.md

@@ -0,0 +1,10 @@
+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

docs/rock5/rock5t/app-development/venv_usage.md

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+---
+sidebar_position: 22
+---
+
+# Python 虚拟环境使用
+
+import VENVUSAGE from '../../../common/dev/\_venv_usage.mdx';
+
+<VENVUSAGE />

docs/zero/zero3/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "通过 RK3588/RK356X 板端部署 YOLOv11，开启智能目标检测的新时代，让技术与人文关怀在精准识别中完美融合"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

i18n/en/docusaurus-plugin-content-docs/current/common/dev/_rknn-ultralytics.mdx

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+:::tip
+This document demonstrates how to perform inference for the YOLOv11 object detection model on RK3588/356X. For the required environment setup, please refer to [RKNN Installation](./rknn_install).
+:::
+
+Currently, the [Ultralytics](https://docs.ultralytics.com/integrations/rockchip-rknn/) library officially supports the RKNN platform. Users of RK3588/356X products can directly use the `ultralytics` library for YOLOv11 model conversion and deployment.
+
+## Model Conversion on PC
+
+**Radxa has provided a pre-converted `yolov11n.rknn` model. Users can skip the PC-side model conversion section and directly refer to [YOLOv11 Inference on Board](#yolov11-inference-on-board).**
+
+- Install the latest version of Ultralytics:
+  ```bash
+  pip3 install -U ultralytics
+  ```
+- Use Ultralytics to export the YOLOv11 model in RKNN format:
+
+    <Tabs>
+
+        <TabItem value="CLI">
+
+        ```bash
+        # 'name' can be one of rk3588, rk3576, rk3566, rk3568, rk3562, rv1103, rv1106, rv1103b, rv1106b, rk2118
+        yolo export model=yolo11n.pt format=rknn name=rk3588
+        ```
+
+        </TabItem>
+
+        <TabItem value="Python">
+
+        ```bash
+        from ultralytics import YOLO
+
+        # Load the YOLOv11 model
+        model = YOLO("yolo11n.pt")
+
+        # Export the model to RKNN format
+        # 'name' can be one of rk3588, rk3576, rk3566, rk3568, rk3562, rv1103, rv1106, rv1103b, rv1106b, rk2118
+        model.export(format="rknn", name="rk3588")  # creates '/yolo11n_rknn_model'
+        ```
+
+        </TabItem>
+
+    </Tabs>
+
+  The result is saved in `./yolo11n_rknn_model`.
+
+- Copy the **yolo11n_rknn_model** directory to the target device.
+
+## YOLOv11 Inference on Board
+
+:::tip
+For RK356X chip users, before using the NPU, enable it through the terminal using **rsetup**: `sudo rsetup -> overlays -> Enable NPU`, and then reboot the system.
+
+If the `Enable NPU` option is missing in overlays, update the system via: `sudo rsetup -> system -> System Update`. Reboot and perform the above steps to enable the NPU.
+:::
+
+- (Optional) Download the YOLOv11n RKNN model provided by Radxa:
+  | Platform | Download Link |
+  | -------- | ------------------------------------------------------------ |
+  | rk3566 | [yolo11n_3566_rknn_model](https://github.com/zifeng-radxa/rknn_model_zoo/releases/download/yolov11/yolo11n_3566_rknn_model.zip) |
+  | rk3568 | [yolo11n_3568_rknn_model](https://github.com/zifeng-radxa/rknn_model_zoo/releases/download/yolov11/yolo11n_3568_rknn_model.zip) |
+  | rk3588 | [yolo11n_3588_rknn_model](https://github.com/zifeng-radxa/rknn_model_zoo/releases/download/yolov11/yolo11n_3588_rknn_model.zip) |
+
+- Install the latest version of Ultralytics in a virtual environment:
+
+  For instructions on virtual environments, refer to [Python Virtual Environment Usage](venv_usage).
+
+  ```bash
+  pip3 install -U ultralytics
+  ```
+
+- Run inference on the board:
+
+      <Tabs>
+          <TabItem value="CLI">
+          ```bash
+          yolo predict model='./yolo11n_rknn_model' source='https://ultralytics.com/images/bus.jpg'
+          ```
+          </TabItem>
+          <TabItem value="Python">
+          ```python
+          from ultralytics import YOLO
+
+          # Load the exported RKNN model
+          rknn_model = YOLO("./yolo11n_rknn_model")
+
+          # Run inference
+          results = rknn_model("https://ultralytics.com/images/bus.jpg")
+          ```
+          </TabItem>
+      </Tabs>
+
+      The results are saved in `runs/detect/predict`.
+
+  <img width="400" src="../../../img/general-tutorial/rknn/yolov11.webp" />
+
+## Additional Usage Details
+
+For more details about using Ultralytics, refer to the [Ultralytics](https://docs.ultralytics.com/) official documentation.

i18n/en/docusaurus-plugin-content-docs/current/compute-module/cm3/app-development/rknn_ultralytics.md

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+---
+sidebar_position: 16
+description: "Deploying YOLOv11 on the RK3588/356X board side opens a new era of intelligent target detection, allowing technology and humanistic care to merge perfectly in accurate identification"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

i18n/en/docusaurus-plugin-content-docs/current/compute-module/cm3i/app-development/rknn_ultralytics.md

@@ -0,0 +1,10 @@
+---
+sidebar_position: 16
+description: "Deploying YOLOv11 on the RK3588/356X board side opens a new era of intelligent target detection, allowing technology and humanistic care to merge perfectly in accurate identification"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />

i18n/en/docusaurus-plugin-content-docs/current/compute-module/cm5/radxa-os/app-dev/rknn_ultralytics.md

@@ -0,0 +1,10 @@
+---
+sidebar_position: 16
+description: "Deploying YOLOv11 on the RK3588/356X board side opens a new era of intelligent target detection, allowing technology and humanistic care to merge perfectly in accurate identification"
+---
+
+# RKNN Ultralytics YOLOv11
+
+import RKNNULTRALYTICS from '../../../../common/dev/\_rknn-ultralytics.mdx';
+
+<RKNNULTRALYTICS />