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@@ -0,0 +1,293 @@
+---
+title: Graphical Model Conversion Platform
+---
+
+## Introduction
+
+After reading the previous model-conversion guides, have all those commands started to blur together? Exporting ONNX, locating output nodes, trimming the model, preparing calibration images, editing configuration files, and running Docker commands—missing a single parameter can send you back through the whole process. Sometimes converting a trained `.pt` model feels harder than training it in the first place.
+
+Do not worry, and put away that command-line checklist. Maix Converter Platform brings the complicated steps together in a Web interface. Upload the model and calibration dataset, select the target device, YOLO version, and input resolution, and the platform will handle model export, node processing, quantization, MUD generation, and result packaging automatically.
+
+There is no need to memorize long commands or repeatedly enter Docker containers. A few clicks and a little patience are enough to produce a YOLO Detect model for MaixCAM, MaixCAM Pro, or MaixCAM2.
+
+## Current Support
+
+The platform currently supports the following devices and models:
+
+| Item | Supported |
+| --- | --- |
+| Target devices | MaixCAM, MaixCAM Pro, MaixCAM2 |
+| Model types | YOLO26, YOLO11, YOLOv8 |
+| Tasks | Object detection (Detect) |
+| Input models | `.pt`, `.onnx` |
+| Calibration dataset | A `.zip` file containing images |
+
+> Classification, segmentation, pose estimation, and OBB tasks are not currently supported. For other models or custom conversion parameters, use the manual conversion methods described in the previous guides.
+
+## Get the Conversion Platform
+
+Maix Converter Platform is open source. Its source code is available at [github.com/sipeed/maix_converter_platform](https://github.com/sipeed/maix_converter_platform).
+
+Clone the repository with Git:
+
+```shell
+git clone https://github.com/sipeed/maix_converter_platform.git
+cd maix_converter_platform
+```
+
+## Prepare the Python Environment
+
+Using Conda to create an independent Python 3.11 environment is recommended to avoid conflicts with existing packages:
+
+```shell
+conda create -n maix-converter python=3.11 -y
+conda activate maix-converter
+pip install -r requirements-web.txt
+```
+
+To upload a `.pt` model and let the platform export it to ONNX automatically, also install Ultralytics and ONNX:
+
+```shell
+pip install ultralytics onnx
+```
+
+If you only upload an existing `.onnx` model, `ultralytics` is not required. Installing `onnx` is still recommended for custom-trained models because the platform can try to read class names from the model metadata and write them to the MUD file.
+
+## Prepare the Docker Environment
+
+The model-conversion toolchains run inside Docker. Install Docker first and verify that the current user can run it:
+
+```shell
+docker --version
+docker ps
+```
+
+If `docker ps` completes without a permission error, Docker is ready.
+
+> Different devices use different conversion toolchains. If you only convert models for one device family, you only need to prepare its corresponding Docker image.
+
+### MaixCAM2 Conversion Image
+
+MaixCAM2 uses the Pulsar2 toolchain. The platform expects the image name `pulsar2:6.0`. For download and import instructions, see [Convert an ONNX model for MaixCAM2](./maixcam2.md).
+
+After downloading the image archive, import it and inspect the image name:
+
+```shell
+docker load -i pulsar2_vxx.tar.gz
+docker images
+```
+
+If the imported image is not named `pulsar2:6.0`, add the expected tag using its actual name. For example:
+
+```shell
+docker tag pulsar2:3.3 pulsar2:6.0
+```
+
+Finally, verify that Pulsar2 works:
+
+```shell
+docker run --rm pulsar2:6.0 -c "pulsar2 version"
+```
+
+### MaixCAM / MaixCAM Pro Conversion Image
+
+MaixCAM and MaixCAM Pro use the TPU-MLIR toolchain. The platform ultimately uses an image named `maixcam-tpumlir:v3.4`. First obtain the `sophgo/tpuc_dev` base image. For download instructions, see [Convert an ONNX model for MaixCAM](./maixcam.md).
+
+After downloading the image archive, import it and check its actual name:
+
+```shell
+docker load -i tpuc_dev_vxx.tar.gz
+docker images
+```
+
+The Dockerfile provided by the conversion platform uses `sophgo/tpuc_dev:v3.4` by default. If the imported image already has this name, no changes are needed. Otherwise, add the expected tag based on the name shown by `docker images`. For example, if the imported image is `sophgo/tpuc_dev:latest`, run:
+
+```shell
+docker tag sophgo/tpuc_dev:latest sophgo/tpuc_dev:v3.4
+```
+
+After confirming the base-image name, build the image used by the platform from the repository root:
+
+```shell
+docker build -f docker/maixcam-tpumlir.Dockerfile -t maixcam-tpumlir:v3.4 .
+```
+
+Inspect the result and verify the conversion command:
+
+```shell
+docker images
+docker run --rm maixcam-tpumlir:v3.4 model_transform.py --help
+```
+
+If the help text for `model_transform.py` appears, the conversion environment is ready.
+
+## Start the Conversion Platform
+
+Enter the repository root and activate the Conda environment created earlier:
+
+```shell
+cd maix_converter_platform
+conda activate maix-converter
+```
+
+Start the Web service:
+
+```shell
+uvicorn web.app:app --host 0.0.0.0 --port 8000
+```
+
+Open the following address in a browser:
+
+```text
+http://127.0.0.1:8000
+```
+
+When accessing the conversion server from another computer, replace `127.0.0.1` with the server's IP address.
+
+## Prepare the Model and Calibration Dataset
+
+The platform accepts `.pt` and `.onnx` model files:
+
+- When uploading `.pt`, the platform uses the width and height entered on the page to export an ONNX model with the corresponding input resolution through Ultralytics.
+- When uploading `.onnx`, the platform proceeds directly to model processing and conversion and uses the model's own static input shape. The width and height entered on the page do not resize the model, so using values that match the actual ONNX input resolution is recommended.
+
+The calibration dataset must be packaged as a `.zip` file containing images only; annotation files are not required. Supported image formats are `.jpg`, `.jpeg`, `.png`, and `.bmp`.
+
+Images may be stored directly in the archive:
+
+```text
+dataset.zip
+  000001.jpg
+  000002.jpg
+  000003.jpg
+```
+
+The archive may also contain nested directories:
+
+```text
+dataset.zip
+  images/
+    000001.jpg
+    000002.jpg
+```
+
+Calibration images should resemble the model's actual deployment environment. For example, if the model will process camera images, prefer images captured by a similar camera under realistic conditions. Start with 50–100 images for a quick test and increase the number as appropriate for the final conversion.
+
+## Create a Conversion Job
+
+Open the Web page and complete the form from top to bottom:
+
+| Option | Description |
+| --- | --- |
+| Model file | Upload the `.pt` or `.onnx` model to convert |
+| Calibration dataset | Upload a `.zip` file containing calibration images only |
+| Model name | Base name for generated files, such as `yolo11n` |
+| Target device | Select MaixCAM2 or MaixCAM / Pro for the actual device |
+| YOLO version | Must match the version of the uploaded model |
+| Image count | Number of calibration images; it cannot exceed the number of valid images in the archive |
+| Width and height | Set the exported model input resolution for `.pt` uploads. For `.onnx` uploads, the model's own static input shape is used. The platform requires both entered values to be between 32 and 4096 and multiples of 32 |
+| Fast mode | Skips some checks to shorten conversion time and is useful for testing the workflow |
+
+Click **Start Conversion** after completing the form. The page displays upload progress, the current job status, and live conversion logs. Conversion time depends on the model size, calibration image count, and computer performance.
+
+> Fast mode is useful for checking the environment and workflow. Before deployment, disable fast mode and perform a complete conversion again.
+
+## Download the Conversion Result
+
+After a successful conversion, the **Download Result** button becomes available. Click it to download a ZIP archive containing the generated model files.
+
+A MaixCAM2 result normally contains:
+
+```text
+model_name.mud
+model_name_npu.axmodel
+model_name_vnpu.axmodel
+```
+
+A MaixCAM or MaixCAM Pro result normally contains:
+
+```text
+model_name.mud
+model_name.cvimodel
+```
+
+Extract the archive and copy all model files to the same directory on the device. The MaixPy program only needs to load the `.mud` file, which references the actual model files in the same directory.
+
+The following example uses YOLO11:
+
+```python
+from maix import app, camera, display, image, nn
+
+detector = nn.YOLO11(model="/root/models/yolo11n.mud", dual_buff=True)
+cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
+disp = display.Display()
+
+while not app.need_exit():
+    img = cam.read()
+    objs = detector.detect(img, conf_th=0.5, iou_th=0.45)
+    for obj in objs:
+        img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=image.COLOR_RED)
+        msg = f"{detector.labels[obj.class_id]}: {obj.score:.2f}"
+        img.draw_string(obj.x, obj.y, msg, color=image.COLOR_RED)
+    disp.show(img)
+```
+
+For YOLO26 or YOLOv8, replace `nn.YOLO11` with the corresponding MaixPy model interface.
+
+## View Jobs and Logs
+
+Each conversion creates an independent job directory under `jobs/`:
+
+```text
+jobs/<job_id>/
+```
+
+The Web page lists recent jobs and provides their status, logs, result downloads, and deletion controls. If a conversion fails, check the live log first or inspect these files in the job directory:
+
+```text
+api.log
+convert.log
+job.json
+```
+
+`convert.log` contains the main output from the model-conversion toolchain and is usually the best place to diagnose output-node, calibration-dataset, Docker-image, or conversion-parameter problems.
+
+## FAQ
+
+### Docker Permission Denied
+
+If `docker ps` reports insufficient permissions, Linux users can add the current user to the Docker group:
+
+```shell
+sudo usermod -aG docker $USER
+```
+
+Log out and back in after running the command, then check `docker ps` again.
+
+### Docker Image Not Found
+
+If the log contains `Unable to find image`, inspect the available image names:
+
+```shell
+docker images
+```
+
+MaixCAM2 requires `pulsar2:6.0`, while MaixCAM and MaixCAM Pro require `maixcam-tpumlir:v3.4`. A different repository name or tag prevents the platform from locating the conversion environment.
+
+### Not Enough Calibration Images
+
+The **Image Count** value cannot exceed the number of valid images in the uploaded ZIP archive. If the archive contains only 50 images, do not set the value to 100.
+
+### Incorrect Classes in a Custom Model
+
+The platform tries to read class names from the `.pt` model or ONNX metadata. If the deployed model reports incorrect class names or counts, check whether `labels` in the generated `.mud` file matches the trained model.
+
+### Directory Mount Failure on Windows
+
+Windows users must ensure that Docker Desktop is running with the WSL2 backend enabled. Keep the project in a short path containing only ASCII characters, such as `C:\maix_converter_platform`, to avoid Docker bind-mount problems caused by non-ASCII characters, special symbols, or deeply nested paths.
+
+## Source Code
+
+To report a problem, suggest an improvement, or request support for additional models, open an Issue in the project repository:
+
+[github.com/sipeed/maix_converter_platform](https://github.com/sipeed/maix_converter_platform)
diff --git a/docs/doc/en/sidebar.yaml b/docs/doc/en/sidebar.yaml
index a84309ef..2d9d4439 100644
--- a/docs/doc/en/sidebar.yaml
+++ b/docs/doc/en/sidebar.yaml
@@ -143,6 +143,10 @@ items:
         label: ONNX model to MaixCAM2's
     -   file: ai_model_converter/maixcam.md
         label: ONNX model to MaixCAM's
+    -   file: ai_model_converter/onnx_export.md
+        label: Trim ONNX model output nodes
+    -   file: ai_model_converter/web_converter.md
+        label: Convert YOLO models with Web UI
     -   file: pro/datasets.md
         label: Where to find models & datasets
     -   file: pro/customize_model.md
@@ -262,4 +266,3 @@ items:
         label: MaixCDK development
     -   file: pro/compile_os.md
         label: Build OS
-
diff --git a/docs/doc/zh/ai_model_converter/web_converter.md b/docs/doc/zh/ai_model_converter/web_converter.md
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+---
+title: 图形化模型转换平台
+---
+
+## 简介
+
+看完前面的模型转换教程，是不是已经被一长串命令绕晕了？导出 ONNX、查找输出节点、裁剪模型、准备量化数据集、修改配置文件、运行 Docker……步骤一个接一个，少写一个参数都可能要从头检查。好不容易训练出了 `.pt` 模型，却发现“转换模型”似乎比“训练模型”还让人头大。
+
+别担心，也不用再抱着命令行逐个参数对答案了！Maix Converter Platform 把这些复杂步骤都搬进了一个 Web 页面。你只需要上传模型和量化图片数据集，再选择目标设备、YOLO 版本和输入分辨率，剩下的模型导出、节点处理、量化转换、MUD 文件生成和结果打包，就交给平台自动完成。
+
+不用记复杂命令，也不用反复进出 Docker。点一点、等一等，就可以拿到能够部署到 MaixCAM、MaixCAM Pro 或 MaixCAM2 的 YOLO Detect 模型文件。
+
+## 当前支持
+
+目前平台支持的设备和模型如下：
+
+| 项目 | 支持范围 |
+| --- | --- |
+| 目标设备 | MaixCAM、MaixCAM Pro、MaixCAM2 |
+| 模型类型 | YOLO26、YOLO11、YOLOv8 |
+| 任务类型 | 目标检测（Detect） |
+| 输入模型 | `.pt`、`.onnx` |
+| 量化数据集 | 包含图片的 `.zip` 文件 |
+
+> 当前暂不支持分类、分割、姿态检测和 OBB 等任务。其他模型或者需要自定义转换参数时，请使用前面介绍的手动转换方法。
+
+## 获取转换平台
+
+Maix Converter Platform 是一个开源工具，源代码在 [github.com/sipeed/maix_converter_platform](https://github.com/sipeed/maix_converter_platform)。
+
+使用 Git 下载源代码：
+
+```shell
+git clone https://github.com/sipeed/maix_converter_platform.git
+cd maix_converter_platform
+```
+
+## 准备 Python 环境
+
+建议使用 Conda 创建一个独立的 Python 3.11 环境，避免与电脑中已有的软件包产生冲突：
+
+```shell
+conda create -n maix-converter python=3.11 -y
+conda activate maix-converter
+pip install -r requirements-web.txt
+```
+
+如果需要上传 `.pt` 模型并让平台自动导出 ONNX，还需要安装 Ultralytics 和 ONNX：
+
+```shell
+pip install ultralytics onnx
+```
+
+如果只上传已经导出的 `.onnx` 模型，可以不安装 `ultralytics`。对于自己训练的模型，仍然建议安装 `onnx`，平台可以尝试从模型信息中读取类别名称并写入 MUD 文件。
+
+## 准备 Docker 环境
+
+模型转换工具链运行在 Docker 中。请先安装 Docker，并确认当前用户可以正常运行：
+
+```shell
+docker --version
+docker ps
+```
+
+如果 `docker ps` 没有出现权限错误，说明 Docker 已经可以使用。
+
+> 不同设备使用不同的转换工具链。如果只需要转换一种设备的模型，只准备对应的 Docker 镜像即可。
+
+### MaixCAM2 转换镜像
+
+MaixCAM2 使用 Pulsar2 工具链，平台默认使用的镜像名称是 `pulsar2:6.0`。Pulsar2 镜像的下载和导入方法请参考[将 ONNX 模型转换为 MaixCAM2 模型](./maixcam2.md)。
+
+下载镜像包后使用下面的命令导入：
+
+```shell
+docker load -i pulsar2_vxx.tar.gz
+docker images
+```
+
+如果导入后的镜像名称不是 `pulsar2:6.0`，需要根据实际名称添加一个标签，例如：
+
+```shell
+docker tag pulsar2:3.3 pulsar2:6.0
+```
+
+最后检查 Pulsar2 是否可以正常运行：
+
+```shell
+docker run --rm pulsar2:6.0 -c "pulsar2 version"
+```
+
+### MaixCAM / MaixCAM Pro 转换镜像
+
+MaixCAM 和 MaixCAM Pro 使用 TPU-MLIR 工具链。平台最终使用的镜像名称是 `maixcam-tpumlir:v3.4`。首先需要获取 `sophgo/tpuc_dev` 基础镜像，镜像的下载方法可以参考[将 ONNX 模型转换为 MaixCAM 模型](./maixcam.md)。
+
+下载镜像包后使用下面的命令导入，并查看实际的镜像名称：
+
+```shell
+docker load -i tpuc_dev_vxx.tar.gz
+docker images
+```
+
+转换平台提供的 Dockerfile 默认使用 `sophgo/tpuc_dev:v3.4`。如果导入后的镜像名称已经是这个名称，就不需要修改；如果名称或标签不同，再根据 `docker images` 中显示的实际名称添加标签。例如导入后显示为 `sophgo/tpuc_dev:latest`，执行：
+
+```shell
+docker tag sophgo/tpuc_dev:latest sophgo/tpuc_dev:v3.4
+```
+
+确认基础镜像名称正确后，在转换平台根目录构建平台使用的镜像：
+
+```shell
+docker build -f docker/maixcam-tpumlir.Dockerfile -t maixcam-tpumlir:v3.4 .
+```
+
+构建完成后再次查看镜像，并验证转换命令：
+
+```shell
+docker images
+docker run --rm maixcam-tpumlir:v3.4 model_transform.py --help
+```
+
+看到 `model_transform.py` 的帮助信息，就说明转换环境准备完成了。
+
+## 启动转换平台
+
+进入项目根目录并激活前面创建的 Conda 环境：
+
+```shell
+cd maix_converter_platform
+conda activate maix-converter
+```
+
+启动 Web 服务：
+
+```shell
+uvicorn web.app:app --host 0.0.0.0 --port 8000
+```
+
+在浏览器中访问：
+
+```text
+http://127.0.0.1:8000
+```
+
+如果在另一台电脑上访问这台转换服务器，请将 `127.0.0.1` 替换为服务器的 IP 地址。
+
+## 准备模型和量化数据集
+
+模型文件支持 `.pt` 和 `.onnx` 两种格式：
+
+- 上传 `.pt` 时，平台会使用页面中填写的宽度和高度，通过 Ultralytics 自动导出对应输入分辨率的 ONNX。
+- 上传 `.onnx` 时，平台会直接进行后续处理和转换，并使用模型自身的静态输入尺寸，不会根据页面中填写的宽度和高度重新调整模型尺寸。建议填写与 ONNX 模型实际输入尺寸相同的值。
+
+量化数据集需要打包为 `.zip` 文件，压缩包中只需要图片，不需要标注文件。支持 `.jpg`、`.jpeg`、`.png` 和 `.bmp` 格式。
+
+压缩包中可以直接放置图片：
+
+```text
+dataset.zip
+  000001.jpg
+  000002.jpg
+  000003.jpg
+```
+
+也可以包含多层目录：
+
+```text
+dataset.zip
+  images/
+    000001.jpg
+    000002.jpg
+```
+
+量化图片应该尽量接近模型部署后的真实使用场景。例如模型将用于摄像头拍摄，就优先使用同类摄像头在实际环境中采集的图片。快速测试可以先准备 50～100 张，正式转换时可以根据数据集情况适当增加。
+
+## 创建转换任务
+
+打开网页后，按照页面从上到下填写转换参数：
+
+| 参数 | 说明 |
+| --- | --- |
+| 模型文件 | 上传需要转换的 `.pt` 或 `.onnx` 模型 |
+| 量化数据集 | 上传只包含量化图片的 `.zip` 文件 |
+| 模型名称 | 转换后文件的基础名称，例如 `yolo11n` |
+| 目标设备 | 根据实际设备选择 MaixCAM2 或 MaixCAM / Pro |
+| YOLO 版本 | 必须与上传模型的实际版本一致 |
+| 图片数量 | 参与量化的图片数量，不能超过压缩包中的实际图片数量 |
+| 宽度、高度 | 上传 `.pt` 时用于设置导出的模型输入分辨率；上传 `.onnx` 时以模型自身的静态输入尺寸为准。当前平台要求填写的宽和高在 32～4096 之间，并且都是 32 的倍数 |
+| 快速模式 | 跳过部分检查以缩短转换时间，适合先验证流程 |
+
+填写完成后点击“开始转换”。页面会显示文件上传进度、当前任务状态和实时转换日志。转换时间与模型大小、量化图片数量以及电脑性能有关，请耐心等待。
+
+> 快速模式适合调试环境和验证流程。准备正式部署的模型时，建议关闭快速模式并重新完整转换一次。
+
+## 下载转换结果
+
+转换成功后，页面中的“下载结果”按钮会变为可用状态。点击按钮可以下载打包好的 ZIP 文件。
+
+MaixCAM2 的结果通常包含：
+
+```text
+model_name.mud
+model_name_npu.axmodel
+model_name_vnpu.axmodel
+```
+
+MaixCAM 和 MaixCAM Pro 的结果通常包含：
+
+```text
+model_name.mud
+model_name.cvimodel
+```
+
+将 ZIP 解压后，把其中的所有模型文件复制到设备的同一个目录。MaixPy 程序只需要加载 `.mud` 文件，MUD 文件会自动引用同目录中的实际模型文件。
+
+下面以 YOLO11 为例：
+
+```python
+from maix import app, camera, display, image, nn
+
+detector = nn.YOLO11(model="/root/models/yolo11n.mud", dual_buff=True)
+cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
+disp = display.Display()
+
+while not app.need_exit():
+    img = cam.read()
+    objs = detector.detect(img, conf_th=0.5, iou_th=0.45)
+    for obj in objs:
+        img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=image.COLOR_RED)
+        msg = f"{detector.labels[obj.class_id]}: {obj.score:.2f}"
+        img.draw_string(obj.x, obj.y, msg, color=image.COLOR_RED)
+    disp.show(img)
+```
+
+如果转换的是 YOLO26 或 YOLOv8，请将示例中的 `nn.YOLO11` 换成对应的 MaixPy 模型接口。
+
+## 查看任务和日志
+
+每次转换都会在项目的 `jobs/` 目录中创建一个独立任务目录：
+
+```text
+jobs/<job_id>/
+```
+
+网页会显示最近的转换任务，并提供状态、日志、结果下载和任务删除功能。转换失败时，可以先查看网页中的实时日志，也可以打开任务目录中的以下文件排查：
+
+```text
+api.log
+convert.log
+job.json
+```
+
+其中 `convert.log` 记录了模型工具链的主要输出，通常最适合用来定位模型节点、量化数据集、Docker 镜像或转换参数问题。
+
+## 常见问题
+
+### Docker 没有权限
+
+如果 `docker ps` 提示权限不足，Linux 用户可以将当前用户加入 Docker 用户组：
+
+```shell
+sudo usermod -aG docker $USER
+```
+
+执行后需要重新登录系统，再运行 `docker ps` 检查。
+
+### 找不到 Docker 镜像
+
+如果日志中出现 `Unable to find image`，使用下面的命令检查镜像名称：
+
+```shell
+docker images
+```
+
+MaixCAM2 需要 `pulsar2:6.0`，MaixCAM / MaixCAM Pro 需要 `maixcam-tpumlir:v3.4`。镜像名称或标签不同也会导致平台找不到转换环境。
+
+### 量化图片数量不足
+
+页面中的“图片数量”不能大于 ZIP 文件中实际包含的有效图片数量。如果只准备了 50 张图片，就不要将图片数量设置为 100。
+
+### 自训练模型的类别不正确
+
+平台会尝试从 `.pt` 或 ONNX metadata 中读取类别名称。如果模型运行后类别数量或名称不正确，请检查生成的 `.mud` 文件中 `labels` 的内容是否与训练模型一致。
+
+### Windows 下转换时挂载目录失败
+
+Windows 用户需要确保 Docker Desktop 已经启动，并启用 WSL2 backend。建议把项目放在纯英文且层级较浅的目录中，例如 `C:\maix_converter_platform`，避免中文、特殊字符或过长路径影响 Docker 目录挂载。
+
+## 项目源代码
+
+如果遇到问题、发现错误或者希望支持更多模型，可以在项目仓库提交 Issue：
+
+[github.com/sipeed/maix_converter_platform](https://github.com/sipeed/maix_converter_platform)
diff --git a/docs/doc/zh/sidebar.yaml b/docs/doc/zh/sidebar.yaml
index 23bfb0ef..289c4f0a 100644
--- a/docs/doc/zh/sidebar.yaml
+++ b/docs/doc/zh/sidebar.yaml
@@ -145,6 +145,8 @@ items:
         label: ONNX 模型转给 MaixCAM 用
     -   file: ai_model_converter/onnx_export.md
         label: 裁剪 ONNX 模型节点教程
+    -   file: ai_model_converter/web_converter.md
+        label: 图形化模型转换平台
     -   file: pro/datasets.md
         label: 哪里找模型和数据集
     -   file: pro/customize_model.md
@@ -264,12 +266,3 @@ items:
         label: 使用 MaixCDK 开发
     -   file: pro/compile_os.md
         label: 编译系统
-
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