repair links (#2413)

docs/CHANGLOG.md

@@ -53,7 +53,7 @@ PaddleX 3.0beta 集成了飞桨生态的优势能力，覆盖 7 大场景任务
 
 ### PaddleX v2.0.0rc0(5.19/2021)
 * 全面支持飞桨2.0动态图，更易用的开发模式
-* 目标检测任务新增[PP-YOLOv2](https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/tutorials/train/object_detection/ppyolov2.py), COCO test数据集精度达到49.5%、V100预测速度达到68.9 FPS 
+* 目标检测任务新增[PP-YOLOv2](https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/tutorials/train/object_detection/ppyolov2.py), COCO test数据集精度达到49.5%、V100预测速度达到68.9 FPS
 * 目标检测任务新增4.2MB的超轻量级模型[PP-YOLO tiny](https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/tutorials/train/object_detection/ppyolotiny.py)
 * 语义分割任务新增实时分割模型[BiSeNetV2](https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/tutorials/train/semantic_segmentation/bisenetv2.py)
 * C++部署模块全面升级
@@ -66,7 +66,7 @@ PaddleX 3.0beta 集成了飞桨生态的优势能力，覆盖 7 大场景任务
 ### PaddleX v1.3.0(12.19/2020)
 
 - 模型更新
-  > - 图像分类模型ResNet50_vd新增10万分类预训练模型 
+  > - 图像分类模型ResNet50_vd新增10万分类预训练模型
   > - 目标检测模型FasterRCNN新增模型裁剪支持
   > - 目标检测模型新增多通道图像训练支持
 
@@ -81,28 +81,26 @@ PaddleX 3.0beta 集成了飞桨生态的优势能力，覆盖 7 大场景任务
 新增RestFUL API模块，开发者可通过此模块快速开发基于PaddleX的训练平台
  > - 增加基于RestFUL API的HTML Demo
  > - 增加基于RestFUL API的Remote版可视化客户端
-新增模型通过OpenVINO的部署方案[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/deploy/openvino/index.html)
+新增模型通过OpenVINO的部署方案
 
 ### PaddleX v1.2.0(9.9/2020)
 - 模型更新
-  > - 新增目标检测模型PPYOLO[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#paddlex-det-ppyolo)
+  > - 新增目标检测模型PPYOLO
   > - FasterRCNN、MaskRCNN、YOLOv3、DeepLabv3p等模型新增内置COCO数据集预训练模型
-  > - 目标检测模型FasterRCNN和MaskRCNN新增backbone HRNet_W18[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#paddlex-det-fasterrcnn)
-  > - 语义分割模型DeepLabv3p新增backbone MobileNetV3_large_ssld[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#paddlex-seg-deeplabv3p)
+  > - 目标检测模型FasterRCNN和MaskRCNN新增backbone HRNet_W18
+  > - 语义分割模型DeepLabv3p新增backbone MobileNetV3_large_ssld
 
 - 模型部署更新
-  > - 新增模型通过OpenVINO的部署方案[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/deploy/openvino/index.html)
-  > - 新增模型在树莓派上的部署方案[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/deploy/raspberry/index.html)
+  > - 新增模型通过OpenVINO的部署方案
+  > - 新增模型在树莓派上的部署方案
   > - 优化PaddleLite Android部署的数据预处理和后处理代码性能
   > - 优化Paddle服务端C++代码部署代码，增加use_mkl等参数，通过mkldnn显著提升模型在CPU上的预测性能
 
 - 产业案例更新
-  > - 新增RGB图像遥感分割案例[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/examples/remote_sensing.html)
-  > - 新增多通道遥感分割案例[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/examples/multi-channel_remote_sensing/README.html)
-
+  > - 新增RGB图像遥感分割案例
+  > - 新增多通道遥感分割案例
 - 其它
-  > - 新增数据集切分功能，支持通过命令行切分ImageNet、PascalVOC、MSCOCO和语义分割数据集[详情链接](https://paddlex.readthedocs.io/zh_CN/develop/data/format/classification.html#id2)
-
+  > - 新增数据集切分功能，支持通过命令行切分ImageNet、PascalVOC、MSCOCO和语义分割数据集
 ### PaddleX v1.1.0(7.13/2020)
 - 模型更新
 > - 新增语义分割模型HRNet、FastSCNN
@@ -134,4 +132,3 @@ PaddleX 3.0beta 集成了飞桨生态的优势能力，覆盖 7 大场景任务
 - **易用易集成**
   - 统一易用的全流程API，5步完成模型训练，10行代码实现Python/C++高性能部署。
   - 提供以PaddleX为核心集成的跨平台可视化工具PaddleX-GUI，快速体验飞桨深度学习全流程。
-

docs/data_annotations/ocr_modules/table_recognition_en.md

@@ -3,7 +3,7 @@
 # PaddleX Table Structure Recognition Task Data Annotation Tutorial
 
 ## 1. Data Annotation
-For annotating table data, use the [PPOCRLabelv2](https://github.com/PFCCLab/PPOCRLabel/blob/main/README_en.md) tool. Detailed steps can be found in: [【Video Demonstration】](https://www.bilibili.com/video/BV1wR4y1v7JE/?share_source=copy_web&vd_source=cf1f9d24648d49636e3d109c9f9a377d&t=1998)
+For annotating table data, use the [PPOCRLabelv2](https://github.com/PFCCLab/PPOCRLabel/blob/main/README.md) tool. Detailed steps can be found in: [【Video Demonstration】](https://www.bilibili.com/video/BV1wR4y1v7JE/?share_source=copy_web&vd_source=cf1f9d24648d49636e3d109c9f9a377d&t=1998)
 
 Table annotation focuses on structured extraction of table data, converting tables in images into Excel format. Therefore, annotation requires the use of an external software to open Excel simultaneously. In PPOCRLabel, complete the annotation of text information within the table (text and position), and in the Excel file, complete the annotation of table structure information. The recommended steps are:
 
@@ -20,4 +20,4 @@ The dataset structure and annotation format defined by PaddleX for table recogni
 dataset_dir    # Root directory of the dataset, the directory name can be changed
 ├── images     # Directory for saving images, the directory name can be changed, but note the correspondence with the content of train.txt and val.txt
 ├── train.txt  # Training set annotation file, the file name cannot be changed. Example content: {"filename": "images/border.jpg", "html": {"structure": {"tokens": ["<tr>", "<td", " colspan=\"3\"", ">", "</td>", "</tr>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>"]}, "cells": [{"tokens": ["、", "自", "我"], "bbox": [[[5, 2], [231, 2], [231, 35], [5, 35]]]}, {"tokens": ["9"], "bbox": [[[168, 68], [231, 68], [231, 98], [168, 98]]]}]}, "gt": "<html><body><table><tr><td colspan=\"3\">、自我</td></tr><tr><td>Aghas</td><td>失吴</td><td>月，</td></tr><tr><td>lonwyCau</td><td></td><td>9</td></tr></table></body></html>"}
-└── val.txt    # Validation set annotation file, the file name cannot be changed. Example content: {"filename": "images/no_border.jpg", "html": {"structure": {"tokens": ["<tr>", "<td", " colspan=\"2\"", ">", "</td>", "<td", " rowspan=\"2\"", ">", "</td>", "<td", " rowspan=\"2\"", ">", "</td>", "</tr>", "<tr>", "<td>", "</td>", "<td>", "</td>", "</tr>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>"]}, "cells": [{"tokens": ["a
+└── val.txt    # Validation set annotation file, the file name cannot be changed. Example content: {"filename": "images/no_border.jpg", "html": {"structure": {"tokens": ["<tr>", "<td", " colspan=\"2\"", ">", "</td>", "<td", " rowspan=\"2\"", ">", "</td>", "<td", " rowspan=\"2\"", ">", "</td>", "</tr>", "<tr>", "<td>", "</td>", "<td>", "</td>", "</tr>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>"]}, "cells": [{"tokens": ["a

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting_en.md

@@ -577,7 +577,7 @@ Choose the appropriate deployment method for your model pipeline based on your n
 If the default model weights provided by the General Time Series Forecasting Pipeline do not meet your requirements in terms of accuracy or speed in your specific scenario, you can try to further fine-tune the existing model using **your own domain-specific or application-specific data** to improve the recognition performance of the pipeline in your scenario.
 
 #### 4.1 Model Fine-tuning
-Since the General Time Series Forecasting Pipeline includes a time series forecasting module, if the performance of the pipeline does not meet expectations, you need to refer to the [Customization](../../../module_usage/tutorials/time_series_modules/time_series_forecast_en.md#iv-custom-development) section in the [Time Series Forecasting Module Development Tutorial](../../../module_usage/tutorials/time_series_modules/time_series_forecasting_en.md) and use your private dataset to fine-tune the time series forecasting model.
+Since the General Time Series Forecasting Pipeline includes a time series forecasting module, if the performance of the pipeline does not meet expectations, you need to refer to the [Customization](../../../module_usage/tutorials/time_series_modules/time_series_forecasting_en.md#iv-custom-development) section in the [Time Series Forecasting Module Development Tutorial](../../../module_usage/tutorials/time_series_modules/time_series_forecasting_en.md) and use your private dataset to fine-tune the time series forecasting model.
 
 #### 4.2 Model Application
 After fine-tuning with your private dataset, you will obtain local model weight files.

docs/practical_tutorials/deployment_tutorial.md

@@ -221,7 +221,7 @@ INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
 | `--host`           | 服务器绑定的主机名或 IP 地址。默认为0.0.0.0。                                                                                                               |
 | `--port`           | 服务器监听的端口号。默认为8080。                                                                                                                            |
 | `--use_hpip`       | 如果指定，则启用高性能推理插件。                                                                                                                            |
-| `--serial_number`  | 高性能推理插件使用的序列号。只在启用高性能推理插件时生效。 请注意，并非所有产线、模型都支持使用高性能推理插件，详细的支持情况请参考[PaddleX 高性能推理指南](./high_performance_inference.md)。 |
+| `--serial_number`  | 高性能推理插件使用的序列号。只在启用高性能推理插件时生效。 请注意，并非所有产线、模型都支持使用高性能推理插件，详细的支持情况请参考[PaddleX 高性能推理指南](../pipeline_deploy/high_performance_inference.md)。 |
 | `--update_license` | 如果指定，则进行联网激活。只在启用高性能推理插件时生效。                                                                                                    |
 
 ### 2.3 调用服务

docs/practical_tutorials/deployment_tutorial_en.md

@@ -0,0 +1,3 @@
+简体中文 (deployment_tutorial.md)| English
+
+# PaddleX 3.0 Pipeline Deploy Tutorial

docs/practical_tutorials/ts_anomaly_detection.md

@@ -2,7 +2,7 @@
 
 # PaddleX 3.0 时序异常检测模型产线———设备异常检测应用教程
 
-PaddleX 提供了丰富的模型产线，模型产线由一个或多个模型组合实现，每个模型产线都能够解决特定的场景任务问题。PaddleX 所提供的模型产线均支持快速体验，如果效果不及预期，也同样支持使用私有数据微调模型，并且 PaddleX 提供了 Python API，方便将产线集成到个人项目中。在使用之前，您首先需要安装 PaddleX， 安装方式请参考[ ](../INSTALL.md)[PaddleX本地安装教程](../installation/installation.md)。此处以一个设备节点的异常检测的任务为例子，介绍模型产线工具的使用流程。
+PaddleX 提供了丰富的模型产线，模型产线由一个或多个模型组合实现，每个模型产线都能够解决特定的场景任务问题。PaddleX 所提供的模型产线均支持快速体验，如果效果不及预期，也同样支持使用私有数据微调模型，并且 PaddleX 提供了 Python API，方便将产线集成到个人项目中。在使用之前，您首先需要安装 PaddleX， 安装方式请参考[PaddleX本地安装教程](../installation/installation.md)。此处以一个设备节点的异常检测的任务为例子，介绍模型产线工具的使用流程。
 
 ## 1. 选择产线
 首先，需要根据您的任务场景，选择对应的 PaddleX 产线，本任务旨在识别和标记出设备节点中的异常行为或异常状态，帮助企业和组织及时发现和解决应用服务器节点中的问题，提高系统的可靠性和可用性。了解到这个任务属于时序异常检测任务，对应 PaddleX 的时序异常检测产线。如果无法确定任务和产线的对应关系，您可以在 PaddleX 支持的[PaddleX产线列表(CPU/GPU)](../support_list/pipelines_list.md)中了解相关产线的能力介绍。
@@ -114,7 +114,7 @@ python main.py -c paddlex/configs/ts_anomaly_detection/PatchTST_ad.yaml \
     -o Train.feature_cols=0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54 \
     -o Train.freq=1 \
     -o Train.label_col=label \
-    -o Train.seq_len=96 
+    -o Train.seq_len=96
 ```
 在 PaddleX 中模型训练支持：修改训练超参数、单机单卡训练(时序模型仅支持单卡训练)等功能，只需修改配置文件或追加命令行参数。
 

docs/support_list/models_list_en.md

@@ -146,7 +146,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 |-|-|-|-|-|-|
 |PP-ShiTuV2_det|41.5|33.7426|537.003|27.6 M|[PP-ShiTuV2_det.yaml](../../paddlex/configs/mainbody_detection/PP-ShiTuV2_det.yaml)|
 
-**Note: The above accuracy metrics are mAP(0.5:0.95) on the [PaddleClas main body detection dataset](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/en/training/PP-ShiTu/mainbody_detection.md).**
+**Note: The above accuracy metrics are mAP(0.5:0.95) on the [PaddleClas main body detection dataset](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/training/PP-ShiTu/mainbody_detection.md).**
 
 ## [Object Detection Module](../module_usage/tutorials/cv_modules/object_detection_en.md)
 | Model Name | mAP (%) | GPU Inference Time (ms) | CPU Inference Time (ms)  | Model Size |YAML File|
@@ -222,7 +222,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 |-|:-:|-|-|-|-|
 | BlazeFace                | 77.7/73.4/49.5  |              |         | 0.447      | [BlazeFace.yaml](../../paddlex/configs/face_detection/BlazeFace.yaml)|
 | BlazeFace-FPN-SSH        | 83.2/80.5/60.5  |              |         | 0.606      | [BlazeFace-FPN-SSH.yaml](../../paddlex/configs/face_detection/BlazeFace-FPN-SSH.yaml) |
-| PicoDet_LCNet_x2_5_face	 | 93.7/90.7/68.1  |              |         | 28.9       | [PicoDet_LCNet_x2_5_face.yaml](../../paddlex/configs/face_detection/PicoDet_LCNet_x2_5_face.yaml) |
+| PicoDet_LCNet_x2_5_face    | 93.7/90.7/68.1  |              |         | 28.9       | [PicoDet_LCNet_x2_5_face.yaml](../../paddlex/configs/face_detection/PicoDet_LCNet_x2_5_face.yaml) |
 | PP-YOLOE_plus-S_face     | 93.9/91.8/79.8  |              |         | 26.5       |[PP-YOLOE_plus-S_face](../../paddlex/configs/face_detection/PP-YOLOE_plus-S_face.yaml) |
 
 **Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.**