PaddlePaddle · TingquanGao · Nov 1, 2022 · Oct 24, 2022 · Oct 30, 2022 · Oct 31, 2022
diff --git a/README_ch.md b/README_ch.md
@@ -95,8 +95,8 @@ PaddleClas支持多种前沿图像分类、识别相关算法，发布产业级
     - [向量检索](docs/zh_CN/deployment/PP-ShiTu/vector_search.md)
     - [哈希编码](docs/zh_CN/training/PP-ShiTu/deep_hashing.md)
   - PipeLine 推理部署
-    - [基于python预测引擎推理](docs/zh_CN/deployment/image_classification/python.md#2)
-    - [基于C++预测引擎推理](deploy/cpp_shitu/readme.md)
+    - [基于python预测引擎推理](docs/zh_CN/deployment/PP-ShiTu/python.md)
+    - [基于C++预测引擎推理](docs/zh_CN/deployment/PP-ShiTu/cpp.md)
     - [服务化部署](docs/zh_CN/deployment/PP-ShiTu/paddle_serving.md)
     - [端侧部署](docs/zh_CN/deployment/PP-ShiTu/paddle_lite.md)
     - [库管理工具](docs/zh_CN/deployment/PP-ShiTu/gallery_manager.md)

diff --git a/docs/en/PPShiTu/PPShiTuV2_introduction.md b/docs/en/PPShiTu/PPShiTuV2_introduction.md
@@ -163,7 +163,7 @@ cd deploy/models
 wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
 
 # Download the feature extraction inference model and unzip it
-wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.
+wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.tar
 ```
 
 ### 4.2 Test data preparation

diff --git a/docs/en/PULC/PULC_car_exists_en.md b/docs/en/PULC/PULC_car_exists_en.md
@@ -438,7 +438,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_language_classification_en.md b/docs/en/PULC/PULC_language_classification_en.md
@@ -451,7 +451,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_person_attribute_en.md b/docs/en/PULC/PULC_person_attribute_en.md
@@ -450,7 +450,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_person_exists_en.md b/docs/en/PULC/PULC_person_exists_en.md
@@ -439,7 +439,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_safety_helmet_en.md b/docs/en/PULC/PULC_safety_helmet_en.md
@@ -413,7 +413,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_text_image_orientation_en.md b/docs/en/PULC/PULC_text_image_orientation_en.md
@@ -447,7 +447,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_textline_orientation_en.md b/docs/en/PULC/PULC_textline_orientation_en.md
@@ -56,7 +56,7 @@ It can be seen that high accuracy can be getted when backbone is SwinTranformer_
 
 **Note**:
 * Backbone name without \* means the resolution is 224x224, and with \* means the resolution is 48x192 (h\*w). The stride of the network is changed to `[2, [2, 1], [2, 1], [2, 1]`. Please refer to [PaddleOCR]（ https://github.com/PaddlePaddle/PaddleOCR）for more details.
-* Backbone name with \*\* means that the resolution is 80x160 (h\*w), and the stride of the network is changed to `[2, [2, 1], [2, 1], [2, 1]]`. This resolution is searched by [Hyperparameter Searching](pulc_train_en.md#4).
+* Backbone name with \*\* means that the resolution is 80x160 (h\*w), and the stride of the network is changed to `[2, [2, 1], [2, 1], [2, 1]]`. This resolution is searched by [Hyperparameter Searching](PULC_train_en.md#4).
 * The Latency is tested on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz. The MKLDNN is enabled and the number of threads is 10.
 * About PP-LCNet, please refer to [PP-LCNet Introduction](../models/PP-LCNet_en.md) and [PP-LCNet Paper](https://arxiv.org/abs/2109.15099).
 
@@ -431,7 +431,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_traffic_sign_en.md b/docs/en/PULC/PULC_traffic_sign_en.md
@@ -456,7 +456,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>
 

diff --git a/docs/en/PULC/PULC_vehicle_attribute_en.md b/docs/en/PULC/PULC_vehicle_attribute_en.md
@@ -464,7 +464,7 @@ PaddleClas provides an example about how to deploy with C++. Please refer to [De
 
 Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer [Paddle Serving](https://github.com/PaddlePaddle/Serving) for more information.
 
-PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/paddle_serving_deploy_en.md).
+PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to [Paddle Serving Deployment](../inference_deployment/classification_serving_deploy_en.md).
 
 <a name="6.5"></a>