update docs (#2438)

README.md

@@ -595,7 +595,7 @@ for res in output:
   <summary> <b> 🖼️ 图像分类 </b></summary>
 
   * [📂 图像分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/image_classification.html)
-  * [🏷️ 图像多标签分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/ml_classification.html)
+  * [🏷️ 图像多标签分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/image_multilabel_classification.html)
   * [👤 行人属性识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.html)
   * [🚗 车辆属性识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.html)
 

README_en.md

@@ -586,7 +586,7 @@ For other pipelines in Python scripts, just adjust the `pipeline` parameter of t
   <summary> <b> 🖼️ Image Classification </b></summary>
 
   * [📂 Image Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/image_classification.html)
-  * [🏷️ Multi-label Image Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/ml_classification.html)
+  * [🏷️ Multi-label Image Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/image_multilabel_classification.html)
 
   * [👤 Pedestrian Attribute Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.html)
   * [🚗 Vehicle Attribute Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.html)

docs/index.en.md

@@ -11,7 +11,7 @@ hide:
 </p>
 
 <p align="center">
-    <a href="./LICENSE"><img src="https://img.shields.io/badge/License-Apache%202-red.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/License-Apache%202-red.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/Python-3.8%2C%203.9%2C%203.10-blue.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Windows%2C%20Mac-orange.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/Hardware-CPU%2C%20GPU%2C%20XPU%2C%20NPU%2C%20MLU%2C%20DCU-yellow.svg"></a>
@@ -60,10 +60,10 @@ PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePadd
 
 <table class="img-table">
         <tr>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_classification.html"><strong>Image Classification</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html"><strong>Multi-label Image Classification</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/object_detection.html"><strong>Object Detection</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html"><strong>Instance Segmentation</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_classification.html"><strong>Image Classification</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html"><strong>Multi-label Image Classification</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/object_detection.html"><strong>Object Detection</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html"><strong>Instance Segmentation</strong></a></th>
         </tr>
         <tr>
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/b302cd7e-e027-4ea6-86d0-8a4dd6d61f39"></td>
@@ -72,10 +72,10 @@ PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePadd
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/09f683b4-27df-4c24-b8a7-84da20fdd182"></td>
         </tr>
         <tr>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html"><strong>Semantic Segmentation</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html"><strong>Image Anomaly Detection</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html"><strong>OCR</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html"><strong>Table Recognition</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html"><strong>Semantic Segmentation</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html"><strong>Image Anomaly Detection</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html"><strong>OCR</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html"><strong>Table Recognition</strong></a></th>
         </tr>
         <tr>
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/02637f8c-f248-415b-89ab-1276505f198c"></td>
@@ -84,10 +84,10 @@ PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePadd
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/1e798e05-dee7-4b41-9cc4-6708b6014efa"></td>
         </tr>
         <tr>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html"><strong>PP-ChatOCRv3-doc</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html"><strong>Time Series Forecasting</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html"><strong>Time Series Anomaly Detection</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html"><strong>Time Series Classification</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html"><strong>PP-ChatOCRv3-doc</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html"><strong>Time Series Forecasting</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html"><strong>Time Series Anomaly Detection</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html"><strong>Time Series Classification</strong></a></th>
         </tr>
         <tr>
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/e3d97f4e-ab46-411c-8155-494c61492b0a"></td>
@@ -124,7 +124,7 @@ PaddleX is dedicated to achieving pipeline-level model training, inference, and
 ## 📊 What can PaddleX do？
 
 
-All pipelines of PaddleX support <b>online experience</b> on [AI Studio]((https://aistudio.baidu.com/overview)) and local <b>fast inference</b>. You can quickly experience the effects of each pre-trained pipeline. If you are satisfied with the effects of the pre-trained pipeline, you can directly perform [high-performance inference](./docs/pipeline_deploy/high_performance_inference_en.html) / [serving deployment](./docs/pipeline_deploy/service_deploy_en.html) / [edge deployment](./docs/pipeline_deploy/edge_deploy_en.html) on the pipeline. If not satisfied, you can also <b>Custom Development</b> to improve the pipeline effect. For the complete pipeline development process, please refer to the [PaddleX pipeline Development Tool Local Use Tutorial](./docs/pipeline_usage/pipeline_develop_guide_en.html).
+All pipelines of PaddleX support <b>online experience</b> on [AI Studio](https://aistudio.baidu.com/overview) and local <b>fast inference</b>. You can quickly experience the effects of each pre-trained pipeline. If you are satisfied with the effects of the pre-trained pipeline, you can directly perform [high-performance inference](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/high_performance_inference.html) / [serving deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/service_deploy.html) / [edge deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/edge_deploy.html) on the pipeline. If not satisfied, you can also <b>Custom Development</b> to improve the pipeline effect. For the complete pipeline development process, please refer to the [PaddleX pipeline Development Tool Local Use Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/pipeline_develop_guide.html).
 
 In addition, PaddleX provides developers with a full-process efficient model training and deployment tool based on a [cloud-based GUI](https://aistudio.baidu.com/pipeline/mine). Developers <b>do not need code development</b>, just need to prepare a dataset that meets the pipeline requirements to <b>quickly start model training</b>. For details, please refer to the tutorial ["Developing Industrial-level AI Models with Zero Barrier"](https://aistudio.baidu.com/practical/introduce/546656605663301).
 
@@ -140,7 +140,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <th><a href="https://aistudio.baidu.com/pipeline/mine">Zero-Code Development On AI Studio</a></td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/ocr_pipelines/OCR_en.html">OCR</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html">OCR</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -150,7 +150,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_en.html">PP-ChatOCRv3</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">PP-ChatOCRv3</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/182491/webUI?source=appCenter">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -160,7 +160,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_en.html">Table Recognition</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">Table Recognition</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/91661?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -170,7 +170,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/object_detection_en.html">Object Detection</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/object_detection.html">Object Detection</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/70230/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -180,7 +180,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation_en.html">Instance Segmentation</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">Instance Segmentation</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/100063/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -190,7 +190,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/image_classification_en.html">Image Classification</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_classification.html">Image Classification</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/100061/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -200,7 +200,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation_en.html">Semantic Segmentation</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">Semantic Segmentation</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/100062/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -210,7 +210,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting_en.html">Time Series Forecasting</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">Time Series Forecasting</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/105706/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>🚧</td>
@@ -220,7 +220,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection_en.html">Time Series Anomaly Detection</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">Time Series Anomaly Detection</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/105708/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>🚧</td>
@@ -230,7 +230,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/time_series_pipelines/time_series_classification_en.html">Time Series Classification</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">Time Series Classification</a></td>
         <td><a href="https://aistudio.baidu.com/community/app/105707/webUI?source=appMineRecent">Link</a></td>
         <td>✅</td>
         <td>🚧</td>
@@ -240,7 +240,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
     </tr>
         <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/small_object_detection_en.html">Small Object Detection</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">Small Object Detection</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -250,7 +250,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
         <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification_en.html">Multi-label Image Classification</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">Multi-label Image Classification</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -260,7 +260,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection_en.html">Image Anomaly Detection</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html">Image Anomaly Detection</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -270,7 +270,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_en.html">Layout Parsing</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">Layout Parsing</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -280,7 +280,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition_en.html">Formula Recognition</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -290,7 +290,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="./docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition_en.html">Seal Recognition</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">Seal Recognition</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -299,8 +299,8 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>✅</td>
         <td>🚧</td>
     </tr>
-<tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html>Image Recognition</a></td>
+    <tr>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html>Image Recognition</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -310,7 +310,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html">Pedestrian Attribute Recognition</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html">Pedestrian Attribute Recognition</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -320,7 +320,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html">Vehicle Attribute Recognition</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html">Vehicle Attribute Recognition</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -330,7 +330,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">Face Recognition</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">Face Recognition</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -342,7 +342,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
 </table>
 
 
-> ❗Note: The above capabilities are implemented based on GPU/CPU. PaddleX can also perform local inference and custom development on mainstream hardware such as Kunlunxin, Ascend, Cambricon, and Haiguang. The table below details the support status of the pipelines. For specific supported model lists, please refer to the [Model List (Kunlunxin XPU)](./docs/support_list/model_list_xpu_en.html)/[Model List (Ascend NPU)](./docs/support_list/model_list_npu_en.html)/[Model List (Cambricon MLU)](./docs/support_list/model_list_mlu_en.html)/[Model List (Haiguang DCU)](./docs/support_list/model_list_dcu_en.html). We are continuously adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.
+> ❗Note: The above capabilities are implemented based on GPU/CPU. PaddleX can also perform local inference and custom development on mainstream hardware such as Kunlunxin, Ascend, Cambricon, and Haiguang. The table below details the support status of the pipelines. For specific supported model lists, please refer to the [Model List (Kunlunxin XPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_xpu.html)/[Model List (Ascend NPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_npu.html)/[Model List (Cambricon MLU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_mlu.html)/[Model List (Haiguang DCU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_dcu.html). We are continuously adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.
 
 🔥🔥 <b>Support for Domestic Hardware Capabilities</b>
 

docs/index.md

@@ -11,7 +11,7 @@ hide:
 </p>
 
 <p align="center">
-    <a href="./LICENSE"><img src="https://img.shields.io/badge/License-Apache%202-red.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/License-Apache%202-red.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/Python-3.8%2C%203.9%2C%203.10-blue.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Windows%2C%20Mac-orange.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/Hardware-CPU%2C%20GPU%2C%20XPU%2C%20NPU%2C%20MLU%2C%20DCU-yellow.svg"></a>
@@ -59,10 +59,10 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
 
 <table class="img-table">
         <tr>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html"><strong>通用图像分类</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html"><strong>图像多标签分类</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html"><strong>通用目标检测</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html"><strong>通用实例分割</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html"><strong>通用图像分类</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html"><strong>图像多标签分类</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html"><strong>通用目标检测</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html"><strong>通用实例分割</strong></a></th>
         </tr>
         <tr>
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/b302cd7e-e027-4ea6-86d0-8a4dd6d61f39"></td>
@@ -71,10 +71,10 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/09f683b4-27df-4c24-b8a7-84da20fdd182"></td>
         </tr>
         <tr>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html"><strong>通用语义分割</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html"><strong>图像异常检测</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html"><strong>通用OCR</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html"><strong>通用表格识别</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html"><strong>通用语义分割</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html"><strong>图像异常检测</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html"><strong>通用OCR</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html"><strong>通用表格识别</strong></a></th>
         </tr>
         <tr>
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/02637f8c-f248-415b-89ab-1276505f198c"></td>
@@ -83,10 +83,10 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/1e798e05-dee7-4b41-9cc4-6708b6014efa"></td>
         </tr>
         <tr>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html"><strong>文本图像智能分析</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html"><strong>时序预测</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html"><strong>时序异常检测</strong></a></th>
-            <th><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html"><strong>时序分类</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html"><strong>文本图像智能分析</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html"><strong>时序预测</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html"><strong>时序异常检测</strong></a></th>
+            <th><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html"><strong>时序分类</strong></a></th>
         </tr>
         <tr>
             <td><img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/e3d97f4e-ab46-411c-8155-494c61492b0a"></td>
@@ -119,10 +119,10 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
  <b>PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。</b>
 
 
-## 📊 能力支持
+ ## 📊 能力支持
 
 
-PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在[AI Studio星河社区](https://aistudio.baidu.com/overview)上进行<b>在线体验</b>，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能推理](https://amberc0209.github.io/PaddleX/latest/pipeline_deploy/high_performance_inference.html)/[服务化部署](https://amberc0209.github.io/PaddleX/latest/pipeline_deploy/service_deploy.html)/[端侧部署](https://amberc0209.github.io/PaddleX/latest/pipeline_deploy/edge_deploy.html)，如果不满意，您也可以使用产线的<b>二次开发</b>能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](https://amberc0209.github.io/PaddleX/latest/pipeline_usage/pipeline_develop_guide.html)或各产线使用[教程](#-文档)。
+PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在[AI Studio星河社区](https://aistudio.baidu.com/overview)上进行<b>在线体验</b>，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能推理](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/high_performance_inference.html)/[服务化部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/service_deploy.html)/[端侧部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/edge_deploy.html)，如果不满意，您也可以使用产线的<b>二次开发</b>能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/pipeline_develop_guide.html)或各产线使用[教程](#-文档)。
 
 
 此外，PaddleX在[AI Studio星河社区](https://aistudio.baidu.com/overview)为开发者提供了基于[云端图形化开发界面](https://aistudio.baidu.com/pipeline/mine)的全流程开发工具, 点击【创建产线】，选择对应的任务场景和模型产线，就可以开启全流程开发。详细请参考[教程《零门槛开发产业级AI模型》](https://aistudio.baidu.com/practical/introduce/546656605663301)
@@ -139,7 +139,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <th><a href = "https://aistudio.baidu.com/pipeline/mine">星河零代码产线</a></td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -149,7 +149,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/182491/webUI?source=appCenter">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -159,7 +159,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">通用表格识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">通用表格识别</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/91661?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -169,7 +169,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html">通用目标检测</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html">通用目标检测</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/70230/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -179,7 +179,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">通用实例分割</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">通用实例分割</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/100063/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -189,7 +189,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html">通用图像分类</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html">通用图像分类</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/100061/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -199,7 +199,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">通用语义分割</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">通用语义分割</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/100062/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>✅</td>
@@ -209,7 +209,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">时序预测</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">时序预测</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/105706/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>🚧</td>
@@ -219,7 +219,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">时序异常检测</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">时序异常检测</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/105708/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>🚧</td>
@@ -229,7 +229,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">时序分类</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">时序分类</a></td>
         <td><a href = "https://aistudio.baidu.com/community/app/105707/webUI?source=appMineRecent">链接</a></td>
         <td>✅</td>
         <td>🚧</td>
@@ -239,7 +239,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>✅</td>
     </tr>
         <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">小目标检测</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">小目标检测</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -249,7 +249,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
         <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">图像多标签分类</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">图像多标签分类</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -259,7 +259,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html">图像异常检测</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html">图像异常检测</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -269,7 +269,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">通用版面解析</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">通用版面解析</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -279,7 +279,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">公式识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">公式识别</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -289,7 +289,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">印章文本识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">印章文本识别</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>✅</td>
@@ -299,7 +299,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html">通用图像识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html">通用图像识别</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -309,7 +309,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html">行人属性识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html">行人属性识别</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -319,7 +319,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.html">车辆属性识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.html">车辆属性识别</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -329,7 +329,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
         <td>🚧</td>
     </tr>
     <tr>
-        <td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">人脸识别</a></td>
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">人脸识别</a></td>
         <td>🚧</td>
         <td>✅</td>
         <td>🚧</td>
@@ -343,7 +343,7 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
 </table>
 
 
-> ❗注：以上功能均基于 GPU/CPU 实现。PaddleX 还可在昆仑芯、昇腾、寒武纪和海光等主流硬件上进行快速推理和二次开发。下表详细列出了模型产线的支持情况，具体支持的模型列表请参阅[模型列表(昆仑芯XPU)](support_list/model_list_xpu.html)/[模型列表(昇腾NPU)](support_list/model_list_npu.html)/[模型列表(寒武纪MLU)](support_list/model_list_mlu.html)/[模型列表(海光DCU)](support_list/model_list_dcu.html)。我们正在适配更多的模型，并在主流硬件上推动高性能和服务化部署的实施。
+> ❗注：以上功能均基于 GPU/CPU 实现。PaddleX 还可在昆仑芯、昇腾、寒武纪和海光等主流硬件上进行快速推理和二次开发。下表详细列出了模型产线的支持情况，具体支持的模型列表请参阅[模型列表(昆仑芯XPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_xpu.html)/[模型列表(昇腾NPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_npu.html)/[模型列表(寒武纪MLU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_mlu.html)/[模型列表(海光DCU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_dcu.html)。我们正在适配更多的模型，并在主流硬件上推动高性能和服务化部署的实施。
 
 🔥🔥 <b>国产化硬件能力支持</b>
 
@@ -424,8 +424,3 @@ PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在
 ## 💬 Discussion
 
 我们非常欢迎并鼓励社区成员在 [Discussions](https://github.com/PaddlePaddle/PaddleX/discussions) 板块中提出问题、分享想法和反馈。无论您是想要报告一个 bug、讨论一个功能请求、寻求帮助还是仅仅想要了解项目的最新动态，这里都是一个绝佳的平台。
-
-
-## 📄 许可证书
-
-本项目的发布受 [Apache 2.0 license](./LICENSE) 许可认证。

docs/pipeline_deploy/high_performance_inference.en.md

@@ -158,25 +158,22 @@ PaddleX provides default high-performance inference configurations for each mode
   <tr>
     <td>General Image Classification</td>
     <td>Image Classification</td>
-    <td>ResNet18<br/>ResNet34<details><summary><b>more</b></summary>
-<p>ResNet50ResNet101ResNet152ResNet18_vdResNet34_vdResNet50_vdResNet101_vdResNet152_vdResNet200_vdPP-LCNet_x0_25PP-LCNet_x0_35PP-LCNet_x0_5PP-LCNet_x0_75PP-LCNet_x1_0PP-LCNet_x1_5PP-LCNet_x2_0PP-LCNet_x2_5PP-LCNetV2_smallPP-LCNetV2_basePP-LCNetV2_largeMobileNetV3_large_x0_35MobileNetV3_large_x0_5MobileNetV3_large_x0_75MobileNetV3_large_x1_0MobileNetV3_large_x1_25MobileNetV3_small_x0_35MobileNetV3_small_x0_5MobileNetV3_small_x0_75MobileNetV3_small_x1_0MobileNetV3_small_x1_25ConvNeXt_tinyConvNeXt_smallConvNeXt_base_224ConvNeXt_base_384ConvNeXt_large_224ConvNeXt_large_384MobileNetV1_x0_25MobileNetV1_x0_5MobileNetV1_x0_75MobileNetV1_x1_0MobileNetV2_x0_25MobileNetV2_x0_5MobileNetV2_x1_0MobileNetV2_x1_5MobileNetV2_x2_0SwinTransformer_tiny_patch4_window7_224SwinTransformer_small_patch4_window7_224SwinTransformer_base_patch4_window7_224SwinTransformer_base_patch4_window12_384SwinTransformer_large_patch4_window7_224SwinTransformer_large_patch4_window12_384PP-HGNet_smallPP-HGNet_tinyPP-HGNet_basePP-HGNetV2-B0PP-HGNetV2-B1PP-HGNetV2-B2PP-HGNetV2-B3PP-HGNetV2-B4PP-HGNetV2-B5PP-HGNetV2-B6CLIP_vit_base_patch16_224CLIP_vit_large_patch14_224</p>
-<p><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/></p></details></td>
+    <td>ResNet18<br/>ResNet34<details>
+    <summary><b>more</b></summary>ResNet50<br/>ResNet101<br/>ResNet152<br/>ResNet18_vd<br/>ResNet34_vd<br/>ResNet50_vd<br/>ResNet101_vd<br/>ResNet152_vd<br/>ResNet200_vd<br/>PP-LCNet_x0_25<br/>PP-LCNet_x0_35<br/>PP-LCNet_x0_5<br/>PP-LCNet_x0_75<br/>PP-LCNet_x1_0<br/>PP-LCNet_x1_5<br/>PP-LCNet_x2_0<br/>PP-LCNet_x2_5<br/>PP-LCNetV2_small<br/>PP-LCNetV2_base<br/>PP-LCNetV2_large<br/>MobileNetV3_large_x0_35<br/>MobileNetV3_large_x0_5<br/>MobileNetV3_large_x0_75<br/>MobileNetV3_large_x1_0<br/>MobileNetV3_large_x1_25<br/>MobileNetV3_small_x0_35<br/>MobileNetV3_small_x0_5<br/>MobileNetV3_small_x0_75<br/>MobileNetV3_small_x1_0<br/>MobileNetV3_small_x1_25<br/>ConvNeXt_tiny<br/>ConvNeXt_small<br/>ConvNeXt_base_224<br/>ConvNeXt_base_384<br/>ConvNeXt_large_224<br/>ConvNeXt_large_384<br/>MobileNetV1_x0_25<br/>MobileNetV1_x0_5<br/>MobileNetV1_x0_75<br/>MobileNetV1_x1_0<br/>MobileNetV2_x0_25<br/>MobileNetV2_x0_5<br/>MobileNetV2_x1_0<br/>MobileNetV2_x1_5<br/>MobileNetV2_x2_0<br/>SwinTransformer_tiny_patch4_window7_224<br/>SwinTransformer_small_patch4_window7_224<br/>SwinTransformer_base_patch4_window7_224<br/>SwinTransformer_base_patch4_window12_384<br/>SwinTransformer_large_patch4_window7_224<br/>SwinTransformer_large_patch4_window12_384<br/>PP-HGNet_small<br/>PP-HGNet_tiny<br/>PP-HGNet_base<br/>PP-HGNetV2-B0<br/>PP-HGNetV2-B1<br/>PP-HGNetV2-B2<br/>PP-HGNetV2-B3<br/>PP-HGNetV2-B4<br/>PP-HGNetV2-B5<br/>PP-HGNetV2-B6<br/>CLIP_vit_base_patch16_224<br/>CLIP_vit_large_patch14_224</details></td>
   </tr>
 
   <tr>
     <td>General Object Detection</td>
     <td>Object Detection</td>
-    <td>PP-YOLOE_plus-S<br/>PP-YOLOE_plus-M<details><summary><b>more</b></summary>
-<p>PP-YOLOE_plus-LPP-YOLOE_plus-XYOLOX-NYOLOX-TYOLOX-SYOLOX-MYOLOX-LYOLOX-XYOLOv3-DarkNet53YOLOv3-ResNet50_vd_DCNYOLOv3-MobileNetV3RT-DETR-R18RT-DETR-R50RT-DETR-LRT-DETR-HRT-DETR-XPicoDet-SPicoDet-L</p>
-<p><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/></p></details></td>
+    <td>PP-YOLOE_plus-S<br/>PP-YOLOE_plus-M<details>
+        <summary><b>more</b></summary>PP-YOLOE_plus-L<br/>PP-YOLOE_plus-X<br/>YOLOX-N<br/>YOLOX-T<br/>YOLOX-S<br/>YOLOX-M<br/>YOLOX-L<br/>YOLOX-X<br/>YOLOv3-DarkNet53<br/>YOLOv3-ResNet50_vd_DCN<br/>YOLOv3-MobileNetV3<br/>RT-DETR-R18<br/>RT-DETR-R50<br/>RT-DETR-L<br/>RT-DETR-H<br/>RT-DETR-X<br/>PicoDet-S<br/>PicoDet-L</details></td>
   </tr>
 
   <tr>
     <td>General Semantic Segmentation</td>
     <td>Semantic Segmentation</td>
-    <td>Deeplabv3-R50<br/>Deeplabv3-R101<details><summary><b>more</b></summary>
-<p>Deeplabv3_Plus-R50Deeplabv3_Plus-R101PP-LiteSeg-TOCRNet_HRNet-W48OCRNet_HRNet-W18SeaFormer_tinySeaFormer_smallSeaFormer_baseSeaFormer_largeSegFormer-B0SegFormer-B1SegFormer-B2SegFormer-B3SegFormer-B4SegFormer-B5</p>
-<p><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/></p></details></td>
+    <td>Deeplabv3-R50<br/>Deeplabv3-R101<details>
+    <summary><b>more</b></summary>Deeplabv3_Plus-R50<br/>Deeplabv3_Plus-R101<br/>PP-LiteSeg-T<br/>OCRNet_HRNet-W48<br/>OCRNet_HRNet-W18<br/>SeaFormer_tiny<br/>SeaFormer_small<br/>SeaFormer_base<br/>SeaFormer_large<br/>SegFormer-B0<br/>SegFormer-B1<br/>SegFormer-B2<br/>SegFormer-B3<br/>SegFormer-B4<br/>SegFormer-B5</details></td>
   </tr>
 
   <tr>
@@ -188,9 +185,8 @@ PaddleX provides default high-performance inference configurations for each mode
   <tr>
     <td rowspan="3">Seal Text Recognition</td>
     <td>Layout Analysis</td>
-    <td>PicoDet-S_layout_3cls<br/>PicoDet-S_layout_17cls<details><summary><b>more</b></summary>
-<p>PicoDet-L_layout_3clsPicoDet-L_layout_17clsRT-DETR-H_layout_3clsRT-DETR-H_layout_17cls</p>
-<p><br/><br/><br/></p></details></td>
+    <td>PicoDet-S_layout_3cls<br/>PicoDet-S_layout_17cls<details>
+    <summary><b>more</b></summary>PicoDet-L_layout_3cls<br/>PicoDet-L_layout_17cls<br/>RT-DETR-H_layout_3cls<br/>RT-DETR-H_layout_17cls</details></td>
   </tr>
 
   <tr>

docs/pipeline_deploy/high_performance_inference.md

@@ -155,26 +155,23 @@ PaddleX 为每个模型提供默认的高性能推理配置，并将其存储在
   <tr>
     <td>通用图像分类</td>
     <td>图像分类</td>
-    <td>ResNet18<br/>ResNet34<details><summary><b>more</b></summary>
-<p>ResNet50ResNet101ResNet152ResNet18_vdResNet34_vdResNet50_vdResNet101_vdResNet152_vdResNet200_vdPP-LCNet_x0_25PP-LCNet_x0_35PP-LCNet_x0_5PP-LCNet_x0_75PP-LCNet_x1_0PP-LCNet_x1_5PP-LCNet_x2_0PP-LCNet_x2_5PP-LCNetV2_smallPP-LCNetV2_basePP-LCNetV2_largeMobileNetV3_large_x0_35MobileNetV3_large_x0_5MobileNetV3_large_x0_75MobileNetV3_large_x1_0MobileNetV3_large_x1_25MobileNetV3_small_x0_35MobileNetV3_small_x0_5MobileNetV3_small_x0_75MobileNetV3_small_x1_0MobileNetV3_small_x1_25ConvNeXt_tinyConvNeXt_smallConvNeXt_base_224ConvNeXt_base_384ConvNeXt_large_224ConvNeXt_large_384MobileNetV1_x0_25MobileNetV1_x0_5MobileNetV1_x0_75MobileNetV1_x1_0MobileNetV2_x0_25MobileNetV2_x0_5MobileNetV2_x1_0MobileNetV2_x1_5MobileNetV2_x2_0SwinTransformer_tiny_patch4_window7_224SwinTransformer_small_patch4_window7_224SwinTransformer_base_patch4_window7_224SwinTransformer_base_patch4_window12_384SwinTransformer_large_patch4_window7_224SwinTransformer_large_patch4_window12_384PP-HGNet_smallPP-HGNet_tinyPP-HGNet_basePP-HGNetV2-B0PP-HGNetV2-B1PP-HGNetV2-B2PP-HGNetV2-B3PP-HGNetV2-B4PP-HGNetV2-B5PP-HGNetV2-B6CLIP_vit_base_patch16_224CLIP_vit_large_patch14_224</p>
-<p><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/></p></details></td>
+    <td>ResNet18<br/>ResNet34<details>
+    <summary><b>more</b></summary>ResNet50<br/>ResNet101<br/>ResNet152<br/>ResNet18_vd<br/>ResNet34_vd<br/>ResNet50_vd<br/>ResNet101_vd<br/>ResNet152_vd<br/>ResNet200_vd<br/>PP-LCNet_x0_25<br/>PP-LCNet_x0_35<br/>PP-LCNet_x0_5<br/>PP-LCNet_x0_75<br/>PP-LCNet_x1_0<br/>PP-LCNet_x1_5<br/>PP-LCNet_x2_0<br/>PP-LCNet_x2_5<br/>PP-LCNetV2_small<br/>PP-LCNetV2_base<br/>PP-LCNetV2_large<br/>MobileNetV3_large_x0_35<br/>MobileNetV3_large_x0_5<br/>MobileNetV3_large_x0_75<br/>MobileNetV3_large_x1_0<br/>MobileNetV3_large_x1_25<br/>MobileNetV3_small_x0_35<br/>MobileNetV3_small_x0_5<br/>MobileNetV3_small_x0_75<br/>MobileNetV3_small_x1_0<br/>MobileNetV3_small_x1_25<br/>ConvNeXt_tiny<br/>ConvNeXt_small<br/>ConvNeXt_base_224<br/>ConvNeXt_base_384<br/>ConvNeXt_large_224<br/>ConvNeXt_large_384<br/>MobileNetV1_x0_25<br/>MobileNetV1_x0_5<br/>MobileNetV1_x0_75<br/>MobileNetV1_x1_0<br/>MobileNetV2_x0_25<br/>MobileNetV2_x0_5<br/>MobileNetV2_x1_0<br/>MobileNetV2_x1_5<br/>MobileNetV2_x2_0<br/>SwinTransformer_tiny_patch4_window7_224<br/>SwinTransformer_small_patch4_window7_224<br/>SwinTransformer_base_patch4_window7_224<br/>SwinTransformer_base_patch4_window12_384<br/>SwinTransformer_large_patch4_window7_224<br/>SwinTransformer_large_patch4_window12_384<br/>PP-HGNet_small<br/>PP-HGNet_tiny<br/>PP-HGNet_base<br/>PP-HGNetV2-B0<br/>PP-HGNetV2-B1<br/>PP-HGNetV2-B2<br/>PP-HGNetV2-B3<br/>PP-HGNetV2-B4<br/>PP-HGNetV2-B5<br/>PP-HGNetV2-B6<br/>CLIP_vit_base_patch16_224<br/>CLIP_vit_large_patch14_224</details></td>
   </tr>
 
 
   <tr>
     <td>通用目标检测</td>
     <td>目标检测</td>
-    <td>PP-YOLOE_plus-S<br/>PP-YOLOE_plus-M<details><summary><b>more</b></summary>
-<p>PP-YOLOE_plus-LPP-YOLOE_plus-XYOLOX-NYOLOX-TYOLOX-SYOLOX-MYOLOX-LYOLOX-XYOLOv3-DarkNet53YOLOv3-ResNet50_vd_DCNYOLOv3-MobileNetV3RT-DETR-R18RT-DETR-R50RT-DETR-LRT-DETR-HRT-DETR-XPicoDet-SPicoDet-L</p>
-<p><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/></p></details></td>
+    <td>PP-YOLOE_plus-S<br/>PP-YOLOE_plus-M<details>
+        <summary><b>more</b></summary>PP-YOLOE_plus-L<br/>PP-YOLOE_plus-X<br/>YOLOX-N<br/>YOLOX-T<br/>YOLOX-S<br/>YOLOX-M<br/>YOLOX-L<br/>YOLOX-X<br/>YOLOv3-DarkNet53<br/>YOLOv3-ResNet50_vd_DCN<br/>YOLOv3-MobileNetV3<br/>RT-DETR-R18<br/>RT-DETR-R50<br/>RT-DETR-L<br/>RT-DETR-H<br/>RT-DETR-X<br/>PicoDet-S<br/>PicoDet-L</details></td>
   </tr>
 
   <tr>
     <td>通用语义分割</td>
     <td>语义分割</td>
-    <td>Deeplabv3-R50<br/>Deeplabv3-R101<details><summary><b>more</b></summary>
-<p>Deeplabv3_Plus-R50Deeplabv3_Plus-R101PP-LiteSeg-TOCRNet_HRNet-W48OCRNet_HRNet-W18SeaFormer_tinySeaFormer_smallSeaFormer_baseSeaFormer_largeSegFormer-B0SegFormer-B1SegFormer-B2SegFormer-B3SegFormer-B4SegFormer-B5</p>
-<p><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/></p></details></td>
+    <td>Deeplabv3-R50<br/>Deeplabv3-R101<details>
+    <summary><b>more</b></summary>Deeplabv3_Plus-R50<br/>Deeplabv3_Plus-R101<br/>PP-LiteSeg-T<br/>OCRNet_HRNet-W48<br/>OCRNet_HRNet-W18<br/>SeaFormer_tiny<br/>SeaFormer_small<br/>SeaFormer_base<br/>SeaFormer_large<br/>SegFormer-B0<br/>SegFormer-B1<br/>SegFormer-B2<br/>SegFormer-B3<br/>SegFormer-B4<br/>SegFormer-B5</details></td>
   </tr>
 
   <tr>
@@ -197,9 +194,8 @@ PaddleX 为每个模型提供默认的高性能推理配置，并将其存储在
   <tr>
     <td rowspan="3">印章文本识别</td>
     <td>版面区域分析</td>
-    <td>PicoDet-S_layout_3cls<br/>PicoDet-S_layout_17cls<details><summary><b>more</b></summary>
-<p>PicoDet-L_layout_3clsPicoDet-L_layout_17clsRT-DETR-H_layout_3clsRT-DETR-H_layout_17cls</p>
-<p><br/><br/><br/></p></details></td>
+    <td>PicoDet-S_layout_3cls<br/>PicoDet-S_layout_17cls<details>
+    <summary><b>more</b></summary>PicoDet-L_layout_3cls<br/>PicoDet-L_layout_17cls<br/>RT-DETR-H_layout_3cls<br/>RT-DETR-H_layout_17cls</details></td>
   </tr>
 
   <tr>

docs/pipeline_usage/pipeline_develop_guide.en.md

@@ -133,7 +133,7 @@ python main.py -c paddlex/configs/text_recognition/PP-OCRv4_mobile_rec.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=your/dataset_dir
 ```
-In addition, PaddleX provides detailed tutorials for preparing private datasets for model fine-tuning, single-model inference, and more. For details, please refer to the [PaddleX Modules Tutorials](../../README.en.md#-documentation)
+In addition, PaddleX provides detailed tutorials for preparing private datasets for model fine-tuning, single-model inference, and more. For details, please refer to the [PaddleX Modules Tutorials](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_detection.html)
 
 ## 5. Pipeline Testing (Optional)
 

docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.en.md

@@ -757,7 +757,7 @@ You can choose the appropriate deployment method for your model pipeline based o
 If the default model weights provided by the general image multi-label classification 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 <b>your own domain-specific or application-specific data</b> to improve the recognition performance of the general image multi-label classification pipeline in your scenario.
 
 ### 4.1 Model Fine-tuning
-Since the general image multi-label classification pipeline includes an image multi-label classification module, if the performance of the pipeline does not meet expectations, you need to refer to the [Customization](../../../module_usage/tutorials/cv_modules/ml_classification.en.md#Customization) section in the [Image Multi-Label Classification Module Development Tutorial](../../../module_usage/tutorials/cv_modules/ml_classification.en.md) to fine-tune the image multi-label classification model using your private dataset.
+Since the general image multi-label classification pipeline includes an image multi-label classification module, if the performance of the pipeline does not meet expectations, you need to refer to the [Customization](../../../module_usage/tutorials/cv_modules/image_multilabel_classification.en.md#Customization) section in the [Image Multi-Label Classification Module Development Tutorial](../../../module_usage/tutorials/cv_modules/image_multilabel_classification.en.md) to fine-tune the image multi-label classification model using your private dataset.
 
 ### 4.2 Model Application
 After you have completed fine-tuning training using your private dataset, you will obtain local model weights files.

docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.md

@@ -780,7 +780,7 @@ print_r($result["categories"]);
 如果通用图像多标签分类产线提供的默认模型权重在您的场景中，精度或速度不满意，您可以尝试利用<b>您自己拥有的特定领域或应用场景的数据</b>对现有模型进行进一步的<b>微调</b>，以提升通用图像多标签分类产线的在您的场景中的识别效果。
 
 ### 4.1 模型微调
-由于通用图像多标签分类产线包含图像多标签分类模块，如果模型产线的效果不及预期，那么您需要参考[图像多标签分类模块开发教程](../../../module_usage/tutorials/cv_modules/ml_classification.md)中的[二次开发](../../../module_usage/tutorials/cv_modules/ml_classification.md#四二次开发)章节，使用您的私有数据集对图像多标签分类模型进行微调。
+由于通用图像多标签分类产线包含图像多标签分类模块，如果模型产线的效果不及预期，那么您需要参考[图像多标签分类模块开发教程](../../../module_usage/tutorials/cv_modules/image_multilabel_classification.md)中的[二次开发](../../../module_usage/tutorials/cv_modules/image_multilabel_classification.md#四二次开发)章节，使用您的私有数据集对图像多标签分类模型进行微调。
 
 ### 4.2 模型应用
 当您使用私有数据集完成微调训练后，可获得本地模型权重文件。

docs/quick_start.en.md

@@ -30,7 +30,7 @@ python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.
 pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b1-py3-none-any.whl
 ```
 
-> ❗For more installation methods, refer to the [PaddleX Installation Guide](./docs/installation/installation_en.html).
+> ❗For more installation methods, refer to the [PaddleX Installation Guide](https://paddlepaddle.github.io/PaddleX/latest/en/installation/installation.html).
 
 
 ### 💻 CLI Usage
@@ -50,7 +50,6 @@ For example, using the  OCR pipeline:
 ```bash
 paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png  --device gpu:0
 ```
-<details><summary><b>👉 Click to view the running result</b></summary>
 
 <pre><code class="language-bash">{
 'input_path': '/root/.paddlex/predict_input/general_ocr_002.png',
@@ -73,11 +72,10 @@ paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddle
 'rec_score': [0.9985831379890442, 0.999696917533874512, 0.9985735416412354, 0.9842517971992493, 0.9383274912834167, 0.9943678975105286, 0.9419361352920532, 0.9221674799919128, 0.9555020928382874, 0.9870321154594421, 0.9664073586463928, 0.9988052248954773, 0.9979352355003357, 0.9985110759735107, 0.9943482875823975, 0.9991195797920227, 0.9936401844024658, 0.9974591135978699, 0.9743705987930298, 0.9980487823486328, 0.9874696135520935, 0.9900962710380554, 0.9952947497367859, 0.9950481653213501, 0.989926815032959, 0.9915552139282227, 0.9938777685165405, 0.997239887714386, 0.9963340759277344, 0.9936134815216064, 0.97223961353302]}
 </code></pre>
 <p>The visualization result is as follows:</p>
-<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png"></p></details>
+<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png"></p>
 
 To use the command line for other pipelines, simply adjust the `pipeline` parameter to the name of the corresponding pipeline. Below are the commands for each pipeline:
 
-<details><summary><b>👉 More CLI usage for pipelines</b></summary>
 
 <table>
 <thead>
@@ -148,7 +146,7 @@ To use the command line for other pipelines, simply adjust the `pipeline` parame
 <td><code>paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0</code></td>
 </tr>
 </tbody>
-</table></details>
+</table>
 
 ### 📝 Python Script Usage
 
@@ -170,7 +168,6 @@ The following steps are executed:
 * The prediction results are processed.
 
 For other production lines using the Python script, you only need to adjust the `pipeline` parameter of the `create_pipeline()` method to the corresponding production line name. Below is a list of each production line's corresponding parameter name and detailed usage explanation:
-<details><summary><b>👉 More Python Script Usage for Production Lines</b></summary>
 
 <table>
 <thead>
@@ -184,82 +181,82 @@ For other production lines using the Python script, you only need to adjust the
 <tr>
 <td>Document Scene Information Extraction v3</td>
 <td><code>PP-ChatOCRv3-doc</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/information_extraction_pipelines/document_scene_information_extraction.html#22-本地体验">Document Scene Information Extraction v3 Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">Document Scene Information Extraction v3 Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General Image Classification</td>
 <td><code>image_classification</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_classification.html">General Image Classification Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_classification.html">General Image Classification Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General Object Detection</td>
 <td><code>object_detection</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/object_detection.html">General Object Detection Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/object_detection.html">General Object Detection Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General Instance Segmentation</td>
 <td><code>instance_segmentation</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/instance_segmentation.html">General Instance Segmentation Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/instance_segmentation.html">General Instance Segmentation Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General Semantic Segmentation</td>
 <td><code>semantic_segmentation</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/semantic_segmentation.html">General Semantic Segmentation Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/semantic_segmentation.html">General Semantic Segmentation Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Image Multi-label Classification</td>
 <td><code>multi_label_image_classification</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_multi_label_classification.html#22-python脚本方式集成">Image Multi-label Classification Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_multi_label_classification.html">Image Multi-label Classification Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Small Object Detection</td>
 <td><code>small_object_detection</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/small_object_detection.html#22-python脚本方式集成">Small Object Detection Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/small_object_detection.html">Small Object Detection Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Image Anomaly Detection</td>
 <td><code>anomaly_detection</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_anomaly_detection.html#22-python脚本方式集成">Image Anomaly Detection Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_anomaly_detection.html">Image Anomaly Detection Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General OCR</td>
 <td><code>OCR</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/OCR.html">General OCR Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/OCR.html">General OCR Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General Table Recognition</td>
 <td><code>table_recognition</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/table_recognition.html#22-python脚本方式集成">General Table Recognition Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/table_recognition.html">General Table Recognition Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>General Layout Parsing</td>
 <td><code>layout_parsing</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/layout_parsing.html#22-python脚本方式集成">General Layout Parsing Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/layout_parsing.html">General Layout Parsing Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Formula Recognition</td>
 <td><code>formula_recognition</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/formula_recognition.html#22-python脚本方式集成">Formula Recognition Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Seal Text Recognition</td>
 <td><code>seal_recognition</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/seal_recognition.html#22-python脚本方式集成">Seal Text Recognition Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/seal_recognition.html">Seal Text Recognition Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Time Series Forecasting</td>
 <td><code>ts_fc</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_forecasting.html">Time Series Forecasting Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_forecasting.html">Time Series Forecasting Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Time Series Anomaly Detection</td>
 <td><code>ts_ad</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_anomaly_detection.html">Time Series Anomaly Detection Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_anomaly_detection.html">Time Series Anomaly Detection Python Script Instructions</a></td>
 </tr>
 <tr>
 <td>Time Series Classification</td>
 <td><code>ts_cls</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_classification.html">Time Series Classification Python Script Instructions</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_classification.html">Time Series Classification Python Script Instructions</a></td>
 </tr>
 </tbody>
-</table></details>
+</table>

docs/quick_start.md

@@ -30,7 +30,7 @@ python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.
 pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b1-py3-none-any.whl
 ```
 
-> ❗ 更多安装方式参考 [PaddleX 安装教程](installation/installation.html)
+> ❗ 更多安装方式参考 [PaddleX 安装教程](https://paddlepaddle.github.io/PaddleX/latest/installation/installation.html)
 
 ### 💻 命令行使用
 
@@ -51,7 +51,7 @@ paddlex --pipeline [产线名称] --input [输入图片] --device [运行设备]
 ```bash
 paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0
 ```
-<details><summary><b>👉 点击查看运行结果 </b></summary>
+运行结果如下：
 
 <pre><code class="language-bash">{
 'input_path': '/root/.paddlex/predict_input/general_ocr_002.png',
@@ -73,13 +73,12 @@ paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddle
 'rec_text': ['登机牌', 'BOARDING', 'PASS', '舱位', 'CLASS', '序号 SERIALNO.', '座位号', '日期 DATE', 'SEAT NO', '航班 FLIGHW', '035', 'MU2379', '始发地', 'FROM', '登机口', 'GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO', '姓名NAME', 'ZHANGQIWEI', 票号TKTNO', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前10分钟关闭GATESCLOSE10MINUTESBEFOREDEPARTURETIME'],
 'rec_score': [0.9985831379890442, 0.999696917533874512, 0.9985735416412354, 0.9842517971992493, 0.9383274912834167, 0.9943678975105286, 0.9419361352920532, 0.9221674799919128, 0.9555020928382874, 0.9870321154594421, 0.9664073586463928, 0.9988052248954773, 0.9979352355003357, 0.9985110759735107, 0.9943482875823975, 0.9991195797920227, 0.9936401844024658, 0.9974591135978699, 0.9743705987930298, 0.9980487823486328, 0.9874696135520935, 0.9900962710380554, 0.9952947497367859, 0.9950481653213501, 0.989926815032959, 0.9915552139282227, 0.9938777685165405, 0.997239887714386, 0.9963340759277344, 0.9936134815216064, 0.97223961353302]}
 </code></pre>
+
 <p>可视化结果如下：</p>
-<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png"></p></details>
+<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png"></p></
 
 其他产线的命令行使用，只需将 `pipeline` 参数调整为相应产线的名称。下面列出了每个产线对应的命令：
 
-<details><summary><b>👉 更多产线的命令行使用</b></summary>
-
 <table>
 <thead>
 <tr>
@@ -149,7 +148,7 @@ paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddle
 <td><code>paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0</code></td>
 </tr>
 </tbody>
-</table></details>
+</table>
 
 ### 📝 Python 脚本使用
 
@@ -171,7 +170,6 @@ for res in output:
 * 对预测结果进行处理
 
 其他产线的 Python 脚本使用，只需将 `create_pipeline()` 方法的 `pipeline` 参数调整为相应产线的名称。下面列出了每个产线对应的参数名称及详细的使用解释：
-<details><summary><b>👉 更多产线的Python脚本使用</b></summary>
 
 <table>
 <thead>
@@ -185,82 +183,82 @@ for res in output:
 <tr>
 <td>文档场景信息抽取v3</td>
 <td><code>PP-ChatOCRv3-doc</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用图像分类</td>
 <td><code>image_classification</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html">通用图像分类产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html">通用图像分类产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用目标检测</td>
 <td><code>object_detection</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html">通用目标检测产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html">通用目标检测产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用实例分割</td>
 <td><code>instance_segmentation</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">通用实例分割产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">通用实例分割产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用语义分割</td>
 <td><code>semantic_segmentation</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">通用语义分割产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">通用语义分割产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>图像多标签分类</td>
 <td><code>multi_label_image_classification</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">图像多标签分类产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">图像多标签分类产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>小目标检测</td>
 <td><code>small_object_detection</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">小目标检测产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">小目标检测产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>图像异常检测</td>
 <td><code>anomaly_detection</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html#22-python脚本方式集成">图像异常检测产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html#22-python脚本方式集成">图像异常检测产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用OCR</td>
 <td><code>OCR</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用表格识别</td>
 <td><code>table_recognition</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python脚本方式集成">通用表格识别产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python脚本方式集成">通用表格识别产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>通用版面解析</td>
 <td><code>layout_parsing</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python脚本方式集成">通用版面解析产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python脚本方式集成">通用版面解析产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>公式识别</td>
 <td><code>formula_recognition</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python脚本方式集成">公式识别产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python脚本方式集成">公式识别产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>印章文本识别</td>
 <td><code>seal_recognition</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python脚本方式集成">印章文本识别产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python脚本方式集成">印章文本识别产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>时序预测</td>
 <td><code>ts_fc</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">时序预测产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">时序预测产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>时序异常检测</td>
 <td><code>ts_ad</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">时序异常检测产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">时序异常检测产线Python脚本使用说明</a></td>
 </tr>
 <tr>
 <td>时序分类</td>
 <td><code>ts_cls</code></td>
-<td><a href="https://amberc0209.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">时序分类产线Python脚本使用说明</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">时序分类产线Python脚本使用说明</a></td>
 </tr>
 </tbody>
-</table></details>
+</table>

docs/support_list/models_list.en.md

@@ -663,7 +663,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are Top-1 Acc on the [ImageNet-1k](https://www.image-net.org/index.php) validation set.</b>
 
-## [Image Multi-Label Classification Module](../module_usage/tutorials/cv_modules/ml_classification.en.md)
+## [Image Multi-Label Classification Module](../module_usage/tutorials/cv_modules/image_multilabel_classification.en.md)
 <table>
 <thead>
 <tr>

docs/support_list/models_list.md

@@ -663,7 +663,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 </table>
 <b>注：以上精度指标为 </b>[ImageNet-1k](https://www.image-net.org/index.php)<b> 验证集 Top1 Acc。</b>
 
-## [图像多标签分类模块](../module_usage/tutorials/cv_modules/ml_classification.md)
+## [图像多标签分类模块](../module_usage/tutorials/cv_modules/image_multilabel_classification.md)
 <table>
 <thead>
 <tr>

mkdocs.yml

@@ -336,7 +336,7 @@ nav:
          - 公式识别模块: module_usage/tutorials/ocr_modules/formula_recognition.md
        - 图像分类:
          - 图像分类模块: module_usage/tutorials/cv_modules/image_classification.md
-         - 图像多标签分类模块: module_usage/tutorials/cv_modules/ml_classification.md
+         - 图像多标签分类模块: module_usage/tutorials/cv_modules/image_multilabel_classification.md
          - 行人属性识别模块: module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md
          - 车辆属性识别模块: module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md
        - 图像特征: