docs: fix invalid links

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>
 

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>
 

docs/en/PULC/PULC_person_attribute_en.md

@@ -429,7 +429,7 @@ 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>
 

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>
 

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>
 

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>
 

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>
 

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>
 

docs/en/PULC/PULC_vehicle_attribute_en.md

@@ -462,7 +462,7 @@ 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>
 

docs/en/algorithm_introduction/ISE_ReID_en.md

@@ -16,7 +16,7 @@ ISE (Implicit Sample Extension) is a simple, efficient, and effective learning a
 > Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang<br>
 > CVPR2022
 
-![image](../../images/ISE_ReID/ISE_pipeline.png)
+![image](../../images/ISE_pipeline.png)
 
 <a name='2'></a>
 ## 2. Performance on Market1501 and MSMT17

docs/en/algorithm_introduction/reid.md

-English | [简体中文](../../zh_CN/algorithm_introduction/reid.md)
+English | [简体中文](../../zh_CN/algorithm_introduction/ReID.md)
 
 # ReID pedestrian re-identification
 

docs/en/image_recognition_pipeline/feature_extraction_en.md

@@ -112,7 +112,7 @@ Based on the `GeneralRecognitionV2_PPLCNetV2_base.yaml` configuration file, the
 
 ### 5.1 Data Preparation
 
-First you need to customize your own dataset based on the task. Please refer to [Dataset Format Description](../data_preparation/recognition_dataset.md) for the dataset format and file structure.
+First you need to customize your own dataset based on the task. Please refer to [Dataset Format Description](../data_preparation/recognition_dataset_en.md) for the dataset format and file structure.
 
 After the preparation is complete, it is necessary to modify the content related to the data configuration in the configuration file, mainly including the path of the dataset and the number of categories. As is as shown below:
 
@@ -256,7 +256,7 @@ wangzai.jpg: [-7.82453567e-02 2.55877394e-02 -3.66694555e-02 1.34572461e-02
  -3.40284109e-02 8.35561901e-02 2.10910216e-02 -3.27066667e-02]
 ```
 
-In most cases, just getting the features may not meet the users' requirements. If you want to go further on the image recognition task, you can refer to the document [Vector Search](./vector_search.md).
+In most cases, just getting the features may not meet the users' requirements. If you want to go further on the image recognition task, you can refer to the document [Vector Search](./vector_search_en.md).
 
 <a name="6"></a>
 

docs/en/inference_deployment/classification_serving_deploy_en.md

-English | [简体中文](../../zh_CN/inference_deployment/classification_serving_deploy.md)
+English | [简体中文](../../zh_CN/deployment/image_classification/paddle_serving.md)
 
 # Classification model service deployment
 

docs/en/inference_deployment/export_model_en.md

@@ -99,5 +99,5 @@ The inference model exported is used to deployment by using prediction engine. Y
 * [C++ inference](./cpp_deploy_en.md)(Only support classification)
 * [Python Whl inference](./whl_deploy_en.md)(Only support classification)
 * [PaddleHub Serving inference](./paddle_hub_serving_deploy_en.md)(Only support classification)
-* [PaddleServing inference](./paddle_serving_deploy_en.md)
+* [PaddleServing inference](./classification_serving_deploy_en.md)
 * [PaddleLite inference](./paddle_lite_deploy_en.md)(Only support classification)

docs/en/inference_deployment/paddle_hub_serving_deploy_en.md

-English | [简体中文](../../zh_CN/inference_deployment/paddle_hub_serving_deploy.md)
+English | [简体中文](../../zh_CN/deployment/image_classification/paddle_hub.md)
 
 # Service deployment based on PaddleHub Serving
 
@@ -53,7 +53,7 @@ Before installing the service module, you need to prepare the inference model an
   "inference_model_dir": "../inference/"
   ```
 * Model files (including `.pdmodel` and `.pdiparams`) must be named `inference`.
-* We provide a large number of pre-trained models based on the ImageNet-1k dataset. For the model list and download address, see [Model Library Overview](../algorithm_introduction/ImageNet_models.md), or you can use your own trained and converted models.
+* We provide a large number of pre-trained models based on the ImageNet-1k dataset. For the model list and download address, see [Model Library Overview](../algorithm_introduction/ImageNet_models_en.md), or you can use your own trained and converted models.
 
 
 <a name="4"></a>

docs/en/inference_deployment/recognition_serving_deploy_en.md

-English | [简体中文](../../zh_CN/inference_deployment/recognition_serving_deploy.md)
+English | [简体中文](../../zh_CN/deployment/PP-ShiTu/paddle_serving.md)
 
 # Recognition model service deployment
 
@@ -219,7 +219,7 @@ Different from Python Serving, the C++ Serving client calls C++ OP to predict, s
   # One-click compile and install Serving server, set SERVING_BIN
   source ./build_server.sh python3.7
   ```
-  **Note:** The path set by [build_server.sh](../build_server.sh#L55-L62) may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled; If you encounter a non-network error during the execution of `build_server.sh`, you can manually copy the commands in the script to the terminal for execution.
+  **Note:** The path set by [build_server.sh](../../../deploy/paddleserving/build_server.sh#L55-L62) may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled; If you encounter a non-network error during the execution of `build_server.sh`, you can manually copy the commands in the script to the terminal for execution.
 
 - The input and output format used by C++ Serving is different from that of Python, so you need to execute the following command to overwrite the files below [3.1] (#31-model conversion) by copying the 4 files to get the corresponding 4 prototxt files in the folder.
   ```shell

docs/en/quick_start/quick_start_recognition_en.md

@@ -71,7 +71,7 @@ Click the "save index" button above <img src="../../images/quick_start/android_d
 Click the "initialize index" button above <img src="../../images/quick_start/android_demo/reset_100.png" width="25" height="25"/> to initialize the current library to `original`.
 
 #### 1.2.5 Preview Index
-Click the "class preview" button <img src="../../images/quick_start/android_demo/leibichaxun_100.png" width="25" height="25"/> to view it in the pop-up window.
+Click the "class preview" button <img src="../../images/quick_start/android_demo/leibiechaxun_100.png" width="25" height="25"/> to view it in the pop-up window.
 
 <a name="Feature introduction"></a>
 
@@ -99,7 +99,7 @@ One can preview it according to the instructions in [Function Experience - Previ
 
 ### 2.1 Environment configuration
 
-* Installation: Please refer to the document [Environment Preparation](../installation/install_paddleclas.md) to configure the PaddleClas operating environment.
+* Installation: Please refer to the document [Environment Preparation](../installation/install_paddleclas_en.md) to configure the PaddleClas operating environment.
 
 * Go to the `deploy` run directory. All the content and scripts in this section need to be run in the `deploy` directory, you can enter the `deploy` directory with the following scripts.
 
@@ -315,7 +315,7 @@ Build a new index database `index_all` with the following scripts.
 python3.7 python/build_gallery.py -c configs/inference_general.yaml -o IndexProcess.data_file="./drink_dataset_v2.0/gallery/drink_label_all.txt" -o IndexProcess.index_dir="./drink_dataset_v2.0/index_all"
 ```
 
-The final constructed new index database is saved in the folder `./drink_dataset_v2.0/index_all`. For specific instructions on yaml `yaml`, please refer to [Vector Search Documentation](../image_recognition_pipeline/vector_search.md).
+The final constructed new index database is saved in the folder `./drink_dataset_v2.0/index_all`. For specific instructions on yaml `yaml`, please refer to [Vector Search Documentation](../image_recognition_pipeline/vector_search_en.md).
 
 <a name="Image recognition based on the new index database"></a>
 
@@ -392,4 +392,4 @@ After decompression, the `recognition_demo_data_v1.1` folder should have the fol
 
 After downloading the model and test data according to the above steps, you can re-build the index database and test the relevant recognition model.
 
-* For more introduction to object detection, please refer to: [Object Detection Tutorial Document](../image_recognition_pipeline/mainbody_detection.md); for the introduction of feature extraction, please refer to: [Feature Extraction Tutorial Document](../image_recognition_pipeline/feature_extraction.md); for the introduction to vector search, please refer to: [vector search tutorial document](../image_recognition_pipeline/vector_search.md).
+* For more introduction to object detection, please refer to: [Object Detection Tutorial Document](../image_recognition_pipeline/mainbody_detection.md); for the introduction of feature extraction, please refer to: [Feature Extraction Tutorial Document](../image_recognition_pipeline/feature_extraction.md); for the introduction to vector search, please refer to: [vector search tutorial document](../image_recognition_pipeline/vector_search_en.md).