diff --git a/docs/en/tutorials/quick_start_recognition_en.md b/docs/en/tutorials/quick_start_recognition_en.md
index 4d82cd2afba91fc57e632948e1697a039ecba107..32ac9d3a37a4bc4988a09085900ed2b9a5bd2e49 100644
--- a/docs/en/tutorials/quick_start_recognition_en.md
+++ b/docs/en/tutorials/quick_start_recognition_en.md
@@ -43,6 +43,11 @@ The detection model with the recognition inference model for the 4 directions (L
| Product Recignition Model | Product Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
+| Models Introduction | Recommended Scenarios | inference Model | Predict Config File | Config File to Build Index Database |
+| ------------ | ------------- | -------- | ------- | -------- |
+| Lightweight generic mainbody detection model | General Scenarios |[Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar) | - | - |
+| Lightweight generic recognition model | General Scenarios | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
+
Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.1.tar).
@@ -50,6 +55,7 @@ Demo data in this tutorial can be downloaded here: [download link](https://paddl
**Attention**
1. If you do not have wget installed on Windows, you can download the model by copying the link into your browser and unzipping it in the appropriate folder; for Linux or macOS users, you can right-click and copy the download link to download it via the `wget` command.
2. If you want to install `wget` on macOS, you can run the following command.
+3. The predict config file of the lightweight generic recognition model and the config file to build index database are used for the config of product recognition model of server-side. You can modify the path of the model to complete the index building and prediction.
```shell
# install homebrew
@@ -123,6 +129,13 @@ The `models` folder should have the following file structure.
│ └── inference.pdmodel
```
+**Attention**
+If you want to use the lightweight generic recognition model, you need to re-extract the features of the demo data and re-build the index. The way is as follows:
+
+```shell
+python3.7 python/build_gallery.py -c configs/build_product.yaml -o Global.rec_inference_model_dir=./models/general_PPLCNet_x2_5_lite_v1.0_infer
+```
+
### 2.2 Product Recognition and Retrieval
diff --git a/docs/zh_CN/tutorials/quick_start_recognition.md b/docs/zh_CN/tutorials/quick_start_recognition.md
index 19b8fed91925935a21f279ef7833867a70b8ebf2..4a7bf5f96c5afbfc7a8738984e7bb696a606ccf5 100644
--- a/docs/zh_CN/tutorials/quick_start_recognition.md
+++ b/docs/zh_CN/tutorials/quick_start_recognition.md
@@ -34,6 +34,8 @@
检测模型与4个方向(Logo、动漫人物、车辆、商品)的识别inference模型、测试数据下载地址以及对应的配置文件地址如下。
+服务器端通用主体检测模型与各方向识别模型：
+
| 模型简介 | 推荐场景 | inference模型 | 预测配置文件 | 构建索引库的配置文件 |
| ------------ | ------------- | -------- | ------- | -------- |
| 通用主体检测模型 | 通用场景 |[模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/ppyolov2_r50vd_dcn_mainbody_v1.0_infer.tar) | - | - |
@@ -43,6 +45,12 @@
| 商品识别模型 | 商品场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
+轻量级通用主体检测模型与轻量级通用识别模型：
+
+| 模型简介 | 推荐场景 | inference模型 | 预测配置文件 | 构建索引库的配置文件 |
+| ------------ | ------------- | -------- | ------- | -------- |
+| 轻量级通用主体检测模型 | 通用场景 |[模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar) | - | - |
+| 轻量级通用识别模型 | 通用场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
本章节demo数据下载地址如下: [数据下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.1.tar)。
@@ -50,6 +58,7 @@
1. windows 环境下如果没有安装wget,可以按照下面的步骤安装wget与tar命令，也可以在，下载模型时将链接复制到浏览器中下载，并解压放置在相应目录下；linux或者macOS用户可以右键点击，然后复制下载链接，即可通过`wget`命令下载。
2. 如果macOS环境下没有安装`wget`命令，可以运行下面的命令进行安装。
+3. 轻量级通用识别模型的预测配置文件和构建索引的配置文件目前使用的是服务器端商品识别模型的配置，您可以自行修改模型的路径完成相应的索引构建和识别预测。
```shell
# 安装 homebrew
@@ -124,6 +133,13 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognit
│ └── inference.pdmodel
```
+**注意**
+如果使用轻量级通用识别模型，Demo数据需要重新提取特征、够建索引，方式如下：
+
+```shell
+python3.7 python/build_gallery.py -c configs/build_product.yaml -o Global.rec_inference_model_dir=./models/general_PPLCNet_x2_5_lite_v1.0_infer
+```
+
### 2.2 商品识别与检索