diff --git a/example/vision/detetcion/README.md b/example/vision/detetcion/README.md
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--- a/example/vision/detetcion/README.md
+++ b/example/vision/detetcion/README.md
@@ -31,7 +31,112 @@
 
 ## 2 图像数据标注
 
-## 3 使用星河社区 AI Studio 训练检测模型
+### 2.1 Labelme 标注工具安装
+
+Labelme 是一个 python 语言编写，带有图形界面的图像标注软件。可用于图像分类，目标检测，图像分割等任务，在图像分类的标注任务中，标签存储为 JSON 文件。在本章节中，我们使用这个软件来标注数据。
+
+首先，点击 [Labelme 下载地址](https://sourceforge.net/projects/labelme-ima-polygonal.mirror/files/v5.5.0/Labelme.exe/download)，耐心等待几秒后，浏览器将自动开始下载 Labelme，如下图：
+
+![](images/sourceForge.png)
+
+一般来说，下载完成后，系统的下载文件夹内将多出 **Labelme.exe**
+
+![](images/download.png)
+
+> 注:
+> 
+> 如果网速太慢，我们也提供了百度网盘的下载链接，点击链接 [Labelme 标注工具(提取码:cyqc)](https://pan.baidu.com/s/1ssdBXxhoE1tME3nh-xqNzQ) 即可下载
+
+### 2.2 标注前的准备
+
+首先，请参考 [摄像头使用指南](./example/periphery/capture) 利用 **Lockzhiner Vision Module 图片获取助手** 拍摄你需要进行标注的图片，如下图所示：
+
+![](images/capture_images.png)
+
+选择一个你想存放数据集的位置，将 **Labelme.exe** 移动到文件夹下同时创建一个空的文件夹并命名为 **Dataset**，如下图：
+
+![](images/move.png)
+
+进入 **Dataset** 文件夹，创建 **images** 文件夹、**annotations** 文件夹、**flags.txt** 文件，如下图:
+
+![](images/creatre.png)
+
+进入 **Dataset** 文件夹，打开 **images**目录，将 **Lockzhiner Vision Module 图片获取助手** 保存的图片数据（在程序运行目录的 **LockzhinerVisionModuleImages** 文件夹内）移动到该文件夹内，如下图：
+
+![](images/move_images.png)
+
+进入 **Dataset** 文件夹，打开 **flags.txt** 按行写入待标注数据集的类别，写入完成后如下图所示：
+
+![](images/flags.png)
+
+进入 **Dataset** 文件夹所在的目录，按住键盘`Shift`键后，单击鼠标右键，点击 **在此处打开 Powershell 窗口**。
+
+![](images/open_powershll.png)
+
+弹出 **Powershell 窗口**后，输入以下命令来打开 **Labelme**，如下图：
+
+```bash
+.\Labelme.exe .\Dataset\images --nodata --autosave --output .\Dataset\annotations --labels .\Dataset\flags.txt
+```
+
+![](images/open_labelme.png)
+
+参数意义如下：
+
+* --labels 类别标签路径。
+* --nodata 停止将图像数据存储到JSON文件。
+* --autosave 自动存储
+* --ouput 标签文件存储路径
+
+命令执行后将打开 **Labelme** 程序，如下图
+
+![](images/Labelme.png)
+
+### 2.3 标注并产出数据集
+
+点击"编辑"选择标注类型
+
+![](images/labelme_compile.png)
+
+选择创建矩形框
+
+![](images/labelme_Create_rectangle.png)
+
+在图片上拖动十字框选目标区域
+
+![](images/labelme_Select_target.png)
+
+再次点击选择目标框类别
+
+![](images/labelme_category.png)
+
+标注好后点击存储。（若在启动 **labelme** 时未指定 --output 字段，会在第一次存储时提示选择存储路径，若指定 --autosave 字段使用自动保存，则无需点击存储按钮）
+
+![](images/labelme_save.png)
+
+然后点击"下一幅"进行下一张图片的标注
+
+![](images/labelme_next.png)
+
+最终标注好的标签文件如图所示
+
+![](images/Annotate_images.png)
+
+进入 **Dataset** 文件夹所在的目录，将 **Dataset** 目录压缩为文件夹。注意，压缩包的最外层需要有 **Dataset** 目录。这里我们建议你使用 **Bandzip** 压缩软件进行压缩，具体的操作步骤如下：
+
+![](images/compress.png)
+
+![](images/compress_1.png)
+
+![](images/compress_2.png)
+
+压缩完成后，打开压缩文件检查下最外层是否有 Dataset 目录
+
+![](images/compress_3.png)
+
+
+
+## 3 使用 AIStudio 训练检测模型
 
 ## 4 在凌智视觉模块上部署模型
 
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