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
+```
+
 <a name="Product_recognition_and_retrival"></a>
 ### 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 f11bef49cdf14cd0fb43fbef9a21559fdfee2f10..4a7bf5f96c5afbfc7a8738984e7bb696a606ccf5 100644
--- a/docs/zh_CN/tutorials/quick_start_recognition.md
+++ b/docs/zh_CN/tutorials/quick_start_recognition.md
@@ -260,12 +260,12 @@ cp recognition_demo_data_v1.1/gallery_product/data_file.txt recognition_demo_dat
 然后在文件`recognition_demo_data_v1.1/gallery_product/data_file_update.txt`中添加以下的信息，
 
 ```
-gallery/anmuxi/001.jpg    安慕希酸奶
-gallery/anmuxi/002.jpg    安慕希酸奶
-gallery/anmuxi/003.jpg    安慕希酸奶
-gallery/anmuxi/004.jpg    安慕希酸奶
-gallery/anmuxi/005.jpg    安慕希酸奶
-gallery/anmuxi/006.jpg    安慕希酸奶
+gallery/anmuxi/001.jpg	安慕希酸奶
+gallery/anmuxi/002.jpg	安慕希酸奶
+gallery/anmuxi/003.jpg	安慕希酸奶
+gallery/anmuxi/004.jpg	安慕希酸奶
+gallery/anmuxi/005.jpg	安慕希酸奶
+gallery/anmuxi/006.jpg	安慕希酸奶
 ```
 
 每一行的文本中，第一个字段表示图像的相对路径，第二个字段表示图像对应的标签信息，中间用`tab`键分隔开（注意：有些编辑器会将`tab`自动转换为`空格`，这种情况下会导致文件解析报错）。