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update docs of benchmark (#3334)

zhangyubo0722 9 maanden geleden
bovenliggende
00979eb624
commit
ee7e4d2e96
100 gewijzigde bestanden met toevoegingen van 1853 en 306 verwijderingen
Zij-aan-zij weergave Toon Diff Stats
  1. 81 59
      README.md
  2. 140 106
      README_en.md
  3. 16 3
      docs/module_usage/tutorials/cv_modules/3d_bev_detection.en.md
  4. 18 2
      docs/module_usage/tutorials/cv_modules/3d_bev_detection.md
  5. 16 1
      docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md
  6. 16 1
      docs/module_usage/tutorials/cv_modules/anomaly_detection.md
  7. 16 1
      docs/module_usage/tutorials/cv_modules/face_detection.en.md
  8. 16 2
      docs/module_usage/tutorials/cv_modules/face_detection.md
  9. 17 2
      docs/module_usage/tutorials/cv_modules/face_feature.en.md
  10. 16 1
      docs/module_usage/tutorials/cv_modules/face_feature.md
  11. 16 1
      docs/module_usage/tutorials/cv_modules/human_detection.en.md
  12. 16 1
      docs/module_usage/tutorials/cv_modules/human_detection.md
  13. 15 1
      docs/module_usage/tutorials/cv_modules/human_keypoint_detection.en.md
  14. 15 1
      docs/module_usage/tutorials/cv_modules/human_keypoint_detection.md
  15. 18 1
      docs/module_usage/tutorials/cv_modules/image_classification.en.md
  16. 18 1
      docs/module_usage/tutorials/cv_modules/image_classification.md
  17. 16 1
      docs/module_usage/tutorials/cv_modules/image_feature.en.md
  18. 16 1
      docs/module_usage/tutorials/cv_modules/image_feature.md
  19. 16 1
      docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md
  20. 16 1
      docs/module_usage/tutorials/cv_modules/image_multilabel_classification.md
  21. 18 1
      docs/module_usage/tutorials/cv_modules/instance_segmentation.en.md
  22. 18 1
      docs/module_usage/tutorials/cv_modules/instance_segmentation.md
  23. 16 1
      docs/module_usage/tutorials/cv_modules/mainbody_detection.en.md
  24. 15 1
      docs/module_usage/tutorials/cv_modules/mainbody_detection.md
  25. 18 1
      docs/module_usage/tutorials/cv_modules/object_detection.en.md
  26. 18 1
      docs/module_usage/tutorials/cv_modules/object_detection.md
  27. 15 1
      docs/module_usage/tutorials/cv_modules/open_vocabulary_detection.en.md
  28. 15 1
      docs/module_usage/tutorials/cv_modules/open_vocabulary_detection.md
  29. 14 1
      docs/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.en.md
  30. 14 1
      docs/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.md
  31. 16 1
      docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md
  32. 16 1
      docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md
  33. 16 1
      docs/module_usage/tutorials/cv_modules/rotated_object_detection.en.md
  34. 13 1
      docs/module_usage/tutorials/cv_modules/rotated_object_detection.md
  35. 18 1
      docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md
  36. 18 1
      docs/module_usage/tutorials/cv_modules/semantic_segmentation.md
  37. 16 1
      docs/module_usage/tutorials/cv_modules/small_object_detection.en.md
  38. 16 1
      docs/module_usage/tutorials/cv_modules/small_object_detection.md
  39. 16 1
      docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md
  40. 16 1
      docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md
  41. 16 1
      docs/module_usage/tutorials/cv_modules/vehicle_detection.en.md
  42. 16 1
      docs/module_usage/tutorials/cv_modules/vehicle_detection.md
  43. 16 1
      docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md
  44. 16 0
      docs/module_usage/tutorials/ocr_modules/formula_recognition.en.md
  45. 15 1
      docs/module_usage/tutorials/ocr_modules/formula_recognition.md
  46. 22 5
      docs/module_usage/tutorials/ocr_modules/layout_detection.en.md
  47. 21 5
      docs/module_usage/tutorials/ocr_modules/layout_detection.md
  48. 16 1
      docs/module_usage/tutorials/ocr_modules/seal_text_detection.en.md
  49. 16 1
      docs/module_usage/tutorials/ocr_modules/seal_text_detection.md
  50. 20 3
      docs/module_usage/tutorials/ocr_modules/table_cells_detection.en.md
  51. 15 1
      docs/module_usage/tutorials/ocr_modules/table_cells_detection.md
  52. 17 3
      docs/module_usage/tutorials/ocr_modules/table_classification.en.md
  53. 15 1
      docs/module_usage/tutorials/ocr_modules/table_classification.md
  54. 15 1
      docs/module_usage/tutorials/ocr_modules/table_structure_recognition.en.md
  55. 16 1
      docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md
  56. 16 0
      docs/module_usage/tutorials/ocr_modules/text_detection.en.md
  57. 16 1
      docs/module_usage/tutorials/ocr_modules/text_detection.md
  58. 16 1
      docs/module_usage/tutorials/ocr_modules/text_image_unwarping.en.md
  59. 16 1
      docs/module_usage/tutorials/ocr_modules/text_image_unwarping.md
  60. 24 4
      docs/module_usage/tutorials/ocr_modules/text_recognition.en.md
  61. 25 6
      docs/module_usage/tutorials/ocr_modules/text_recognition.md
  62. 16 1
      docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.en.md
  63. 16 1
      docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.md
  64. 15 1
      docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.en.md
  65. 16 1
      docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.md
  66. 16 1
      docs/module_usage/tutorials/time_series_modules/time_series_classification.en.md
  67. 16 1
      docs/module_usage/tutorials/time_series_modules/time_series_classification.md
  68. 15 1
      docs/module_usage/tutorials/time_series_modules/time_series_forecasting.en.md
  69. 15 1
      docs/module_usage/tutorials/time_series_modules/time_series_forecasting.md
  70. 16 1
      docs/module_usage/tutorials/video_modules/video_classification.en.md
  71. 15 1
      docs/module_usage/tutorials/video_modules/video_classification.md
  72. 15 1
      docs/module_usage/tutorials/video_modules/video_detection.en.md
  73. 16 1
      docs/module_usage/tutorials/video_modules/video_detection.md
  74. 18 2
      docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.en.md
  75. 16 2
      docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.md
  76. 19 2
      docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.en.md
  77. 19 3
      docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md
  78. 17 2
      docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.en.md
  79. 17 3
      docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.md
  80. 20 2
      docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.en.md
  81. 20 2
      docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.md
  82. 16 1
      docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.en.md
  83. 16 1
      docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.md
  84. 18 1
      docs/pipeline_usage/tutorials/cv_pipelines/image_classification.en.md
  85. 18 1
      docs/pipeline_usage/tutorials/cv_pipelines/image_classification.md
  86. 16 1
      docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.en.md
  87. 16 1
      docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.md
  88. 18 1
      docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.en.md
  89. 18 2
      docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.md
  90. 18 1
      docs/pipeline_usage/tutorials/cv_pipelines/object_detection.en.md
  91. 18 1
      docs/pipeline_usage/tutorials/cv_pipelines/object_detection.md
  92. 18 6
      docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.en.md
  93. 15 1
      docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.md
  94. 15 3
      docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.en.md
  95. 14 1
      docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.md
  96. 18 2
      docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.en.md
  97. 19 2
      docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.md
  98. 15 1
      docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.en.md
  99. 15 1
      docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.md
  100. 18 1
      docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md

+ 81 - 59
README.md
Bestand weergeven 

@@ -211,48 +211,58 @@ PaddleX的各个产线均支持本地**快速推理**，部分模型支持在[AI
 
         <td>✅</td>
 
         <td>✅</td>
 
     </tr>
 
-    <tr>
 
-        <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>
 
+        <tr>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">公式识别</a></td>
 
+        <td><a href = "https://aistudio.baidu.com/community/app/387976/webUI?source=appCenter">链接</a></td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html">人体关键点检测</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><a href = "https://aistudio.baidu.com/community/app/387977/webUI?source=appCenter">链接</a></td>
 
+        <td>✅</td>
 
+        <td>✅</td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
+    </tr>
 
+        <tr>
 
+        <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><a href = "https://aistudio.baidu.com/community/app/387978/webUI?source=appCenter">链接</a></td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
+        <td>✅</td>
 
+        <td>✅</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html">开放词汇检测</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><a href = "https://aistudio.baidu.com/community/app/387979/webUI?source=appCenter">链接</a></td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
+        <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
-        <td>🚧</td>
 
-        <td>🚧</td>
 
-        <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html">开放词汇分割</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>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html">旋转目标检测</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html">人体关键点检测</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
@@ -262,27 +272,27 @@ PaddleX的各个产线均支持本地**快速推理**，部分模型支持在[AI
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html">3D多模态融合检测</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html">开放词汇检测</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html">通用表格识别v2</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html">开放词汇分割</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <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><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html">旋转目标检测</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
@@ -292,47 +302,47 @@ PaddleX的各个产线均支持本地**快速推理**，部分模型支持在[AI
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html">通用版面解析v2</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html">3D多模态融合检测</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">公式识别</a></td>
 
-        <td><a href = "https://aistudio.baidu.com/community/app/387976/webUI?source=appCenter">链接</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html">通用表格识别v2</a></td>
 
+        <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <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><a href = "https://aistudio.baidu.com/community/app/387977/webUI?source=appCenter">链接</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>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html">文档图像预处理</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html">通用版面解析v2</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <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><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html">文档图像预处理</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
@@ -342,24 +352,14 @@ PaddleX的各个产线均支持本地**快速推理**，部分模型支持在[AI
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <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><a href = "https://aistudio.baidu.com/community/app/387978/webUI?source=appCenter">链接</a></td>
 
-        <td>✅</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>
 
         <td>✅</td>
 
         <td>✅</td>
 
-    </tr>
 
-    <tr>
 
-        <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><a href = "https://aistudio.baidu.com/community/app/387979/webUI?source=appCenter">链接</a></td>
 
-        <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
-        <td>✅</td>
 
     </tr>
 
     <tr>
 
         <td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">人脸识别</a></td>
 
@@ -608,23 +608,34 @@ paddlex --pipeline OCR \
 
 
 | 产线名称           | 使用命令                                                                                                                                                                                    |
 
 |--------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 
-| 通用图像分类       | `paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0`                    |
 
-| 通用目标检测       | `paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --device gpu:0`                            |
 
-| 通用实例分割       | `paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0`                  |
 
-| 通用语义分割       | `paddlex --pipeline semantic_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png --device gpu:0` |
 
-| 图像多标签分类 | `paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0`        |
 
-| 小目标检测         | `paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0`                            |
 
-| 图像异常检测       | `paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0`                                              |
 
-| 行人属性识别       | `paddlex --pipeline pedestrian_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --device gpu:0`                                              |
 
-| 车辆属性识别       | `paddlex --pipeline vehicle_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --device gpu:0`                                              |
 
-| 通用OCR            | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0`                                                      |
 
-| 通用表格识别       | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0`                                      |
 
-| 通用版面解析       | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0`                                      |
 
-| 公式识别       | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0`                                      |
 
-| 印章文本识别       | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0`                                      |
 
-| 时序预测       | `paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0`                                                                   |
 
-| 时序异常检测   | `paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0`                                                                    |
 
-| 时序分类       | `paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0`                                                                 |
 
+| 通用图像分类       | `paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0 --save_path ./output/`                    |
 
+| 通用目标检测       | `paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --threshold 0.5 --save_path ./output/ --device gpu:0`                            |
 
+| 通用实例分割       | `paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --threshold 0.5 --save_path ./output --device gpu:0`                  |
 
+| 通用语义分割       | `paddlex --pipeline semantic_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png --target_size -1 --save_path ./output --device gpu:0` |
 
+| 图像多标签分类 | `paddlex --pipeline image_multilabel_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --save_path ./output --device gpu:0`        |
 
+| 小目标检测         | `paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --threshold 0.5 --save_path ./output --device gpu:0`                            |
 
+| 图像异常检测       | `paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --save_path ./output --device gpu:0`                                              |
 
+| 行人属性识别       | `paddlex --pipeline pedestrian_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --save_path ./output/ --device gpu:0`                                              |
 
+| 车辆属性识别       | `paddlex --pipeline vehicle_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --save_path ./output/ --device gpu:0`                                              |
 
+| 3D多模态融合检测       | `paddlex --pipeline 3d_bev_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/det_3d/demo_det_3d/nuscenes_demo_infer.tar --device gpu:0 --save_path ./output/`                    |
 
+| 人体关键点检测      | `paddlex --pipeline human_keypoint_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/keypoint_detection_001.jpg --det_threshold 0.5 --save_path ./output/ --device gpu:0`                    |
 
+| 开放词汇检测       | `paddlex --pipeline open_vocabulary_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_detection.jpg --prompt "bus . walking man . rearview mirror ." --thresholds "{'text_threshold': 0.25, 'box_threshold': 0.3}" --save_path ./output --device gpu:0`                    |
 
+| 开放词汇分割       | `paddlex --pipeline open_vocabulary_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_segmentation.jpg --prompt_type box --prompt "[[112.9,118.4,513.8,382.1],[4.6,263.6,92.2,336.6],[592.4,260.9,607.2,294.2]]" --save_path ./output --device gpu:0`                    |
 
+| 旋转目标检测       | `paddlex --pipeline rotated_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png --threshold 0.5 --save_path ./output --device gpu:0`                    |
 
+| 通用OCR            | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0`                                                      |
 
+| 文档图像预处理            | `paddlex --pipeline doc_preprocessor --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/doc_test_rotated.jpg --use_doc_orientation_classify True --use_doc_unwarping True --save_path ./output --device gpu:0`                                                      |
 
+| 通用表格识别       | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0`                                      |
 
+| 通用表格识别v2       | `paddlex --pipeline table_recognition_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0`                                      |
 
+| 通用版面解析       | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0`                                      |
 
+| 通用版面解析v2       | `paddlex --pipeline layout_parsing_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_v2_demo.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0`                                      |
 
+| 公式识别       | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --use_layout_detection True --use_doc_orientation_classify False --use_doc_unwarping False --layout_threshold 0.5 --layout_nms True --layout_unclip_ratio  1.0 --layout_merge_bboxes_mode large --save_path ./output --device gpu:0`                                      |
 
+| 印章文本识别       | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --use_doc_orientation_classify False --use_doc_unwarping False --device gpu:0 --save_path ./output`                                      |
 
+| 时序预测       | `paddlex --pipeline ts_forecast --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0 --save_path ./output`                                                                   |
 
+| 时序异常检测   | `paddlex --pipeline ts_anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0 --save_path ./output`                                                                    |
 
+| 时序分类       | `paddlex --pipeline ts_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0 --save_path ./output`                                                                 |
 
+| 多语种语音识别       | `paddlex --pipeline multilingual_speech_recognition --input https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav --save_path ./output --device gpu:0`                                      |
 
+| 通用视频分类       | `paddlex --pipeline video_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/general_video_classification_001.mp4 --topk 5 --save_path ./output --device gpu:0`                     |
 
+| 通用视频检测       | `paddlex --pipeline video_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/HorseRiding.avi --device gpu:0 --save_path ./output`                     |
 
 
 
 </details>
 
@@ -666,14 +677,25 @@ for res in output:
 
 | 人脸识别       | `face_recognition`                | [人脸识别Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html#22-python脚本方式集成)                              |
 
 | 车辆属性识别       | `vehicle_attribute_recognition`                | [车辆属性识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.html#22-python脚本方式集成)                              |
 
 | 行人属性识别       | `pedestrian_attribute_recognition`                | [行人属性识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html#22-python脚本方式集成)                              |
 
+| 3D多模态融合检测       | `3d_bev_detection`             | [3D多模态融合检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html#222-python脚本方式集成)                                |
 
+| 人体关键点检测       | `human_keypoint_detection`             | [人体关键点检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html#222-python脚本方式集成)                       |
 
+| 开放词汇检测       | `open_vocabulary_detection`             | [开放词汇检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html#212-python脚本方式集成)                                |
 
+| 开放词汇分割       | `open_vocabulary_segmentation`             | [开放词汇分割产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html#212-python脚本方式集成)                     |
 
+| 旋转目标检测       | `rotated_object_detection`             | [旋转目标检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html#212-python脚本方式集成)                                |
 
 | 通用OCR            | `OCR`                              | [通用OCR产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html#222-python脚本方式集成)                                                     |
 
+| 文档图像预处理            | `doc_preprocessor`                              | [文档图像预处理产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html#212-python脚本方式集成)                       |
 
 | 通用表格识别       | `table_recognition`                | [通用表格识别产线Python脚本使用说明](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v2      | `table_recognition_v2`                | [通用表格识别v2产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html#22-python脚本方式集成)                                   |
 
 | 通用版面解析       | `layout_parsing`                | [通用版面解析产线Python脚本使用说明](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v2      | `layout_parsing_v2`                | [通用版面解析v2产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html#22-python脚本方式集成)                                   |
 
 | 公式识别       | `formula_recognition`                | [公式识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python脚本方式集成)                                   |
 
 | 印章文本识别       | `seal_recognition`                | [印章文本识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python脚本方式集成)                                   |
 
-| 时序预测       | `ts_fc`                            | [时序预测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html#222-python脚本方式集成)                    |
 
-| 时序异常检测   | `ts_ad`                            | [时序异常检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html#222-python脚本方式集成)          |
 
-| 时序分类       | `ts_cls`                           | [时序分类产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html#222-python脚本方式集成)                 |
 
+| 时序预测       | `ts_forecast`                            | [时序预测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html#222-python脚本方式集成)                    |
 
+| 时序异常检测   | `ts_anomaly_detection`                            | [时序异常检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html#222-python脚本方式集成)          |
 
+| 时序分类       | `ts_classification`                           | [时序分类产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html#222-python脚本方式集成)                 |
 
+| 多语种语音识别       | `multilingual_speech_recognition`                           | [多语种语音识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/multilingual_speech_recognition.html#212-python脚本方式集成)                 |
 
+| 通用视频分类       | `video_classification`                           | [通用视频分类产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/video_classification.html#22-python脚本方式集成)                 |
 
+| 通用视频检测       | `video_detection`                           | [通用视频检测产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/video_detection.html#212-python脚本方式集成)                 |
 
 
 </details>

+ 140 - 106
README_en.md
Bestand weergeven 

@@ -4,13 +4,13 @@
 
 
 <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/Python-3.8%2C%203.9%2C%203.10-blue.svg"></a>
 
+    <a href=""><img src="https://img.shields.io/badge/Python-3.8~3.12-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>
 
 </p>
 
 
 <h4 align="center">
 
-  <a href=#-why-paddlex->🌟 Features</a> | <a href=https://aistudio.baidu.com/pipeline/mine>🌐  Online Experience</a>｜<a href=#️-quick-start>🚀  Quick Start</a> | <a href=https://addlepaddle.github.io/PaddleX/latest/en/index.html> 📖 Documentation</a> | <a href=#-what-can-paddlex-do> 🔥Capabilities</a> | <a href=https://paddlepaddle.github.io/PaddleX/latest/en/support_list/models_list.html> 📋 Models</a>
 
+  <a href=#-why-paddlex->🌟 Features</a> | <a href=https://aistudio.baidu.com/application/center/app?tag=%E5%85%A8%E9%83%A8&flod=86503>>🌐  Online Experience</a>｜<a href=#️-quick-start>🚀  Quick Start</a> | <a href=https://addlepaddle.github.io/PaddleX/latest/en/index.html> 📖 Documentation</a> | <a href=#-what-can-paddlex-do> 🔥Capabilities</a> | <a href=https://paddlepaddle.github.io/PaddleX/latest/en/support_list/models_list.html> 📋 Models</a>
 
 </h4>
 
 
 <h5 align="center">
 
@@ -210,47 +210,57 @@ In addition, PaddleX provides developers with a full-process efficient model tra
 
         <td>✅</td>
 
     </tr>
 
     <tr>
 
-        <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><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html">Pedestrian Attribute Recognition</a></td>
 
+        <td><a href="https://aistudio.baidu.com/community/app/387978/webUI?source=appCenter">Link</a></td>
 
+        <td>✅</td>
 
+        <td>🚧</td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
+    </tr>
 
+    <tr>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html">Vehicle Attribute Recognition</a></td>
 
+        <td><a href="https://aistudio.baidu.com/community/app/387979/webUI?source=appCenter">Link</a></td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
+        <td>✅</td>
 
+        <td>✅</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html">Human Keypoint Detection</a></td>
 
-        <td>🚧</td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition</a></td>
 
+        <td><a href="https://aistudio.baidu.com/community/app/387976/webUI?source=appCenter">Link</a></td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html">Open Vocabulary Detection</a></td>
 
-        <td>🚧</td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">Seal Recognition</a></td>
 
+        <td><a href="https://aistudio.baidu.com/community/app/387977/webUI?source=appCenter">Link</a></td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>🚧</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
+        <td>✅</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html">Open Vocabulary Segmentation</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>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html">Rotated Object Detection</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html">Human Keypoint Detection</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
@@ -260,27 +270,27 @@ In addition, PaddleX provides developers with a full-process efficient model tra
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html">3D Bev Detection</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html">Open Vocabulary Detection</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html">Table Recognition v2</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html">Open Vocabulary Segmentation</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">Layout Parsing</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html">Rotated Object Detection</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
@@ -290,74 +300,64 @@ In addition, PaddleX provides developers with a full-process efficient model tra
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html">Layout Parsing v2</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html">3D Bev Detection</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition</a></td>
 
-        <td><a href="https://aistudio.baidu.com/community/app/387976/webUI?source=appCenter">Link</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html">Table Recognition v2</a></td>
 
+        <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">Seal Recognition</a></td>
 
-        <td><a href="https://aistudio.baidu.com/community/app/387977/webUI?source=appCenter">Link</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>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html">Document Image Preprocessing</a></td>
 
+        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html">Layout Parsing v2</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
+        <td>🚧</td>
 
         <td>🚧</td>
 
     </tr>
 
     <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><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html">Document Image Preprocessing</a></td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
-        <td>🚧</td>
 
+        <td>✅</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
     </tr>
 
     <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html">Pedestrian Attribute Recognition</a></td>
 
-        <td><a href="https://aistudio.baidu.com/community/app/387978/webUI?source=appCenter">Link</a></td>
 
-        <td>✅</td>
 
+        <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>
 
         <td>✅</td>
 
-        <td>✅</td>
 
-    </tr>
 
-    <tr>
 
-        <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html">Vehicle Attribute Recognition</a></td>
 
-        <td><a href="https://aistudio.baidu.com/community/app/387979/webUI?source=appCenter">Link</a></td>
 
-        <td>✅</td>
 
         <td>🚧</td>
 
         <td>✅</td>
 
         <td>🚧</td>
 
-        <td>✅</td>
 
-        <td>✅</td>
 
     </tr>
 
     <tr>
 
         <td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">Face Recognition</a></td>
 
@@ -533,21 +533,21 @@ In addition, PaddleX provides developers with a full-process efficient model tra
 
 
 ### 🛠️ Installation
 
 
-> ❗Before installing PaddleX, please ensure you have a basic **Python environment** (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted). The PaddleX 3.0-beta2 version depends on PaddlePaddle version 3.0.0b2.
 
+> ❗Before installing PaddleX, please ensure you have a basic **Python runtime environment** (Note: Currently supports running under Python 3.8 to Python 3.10, with more Python versions under adaptation). The PaddlePaddle version required by PaddleX
 
 
 * **Installing PaddlePaddle**
 
 
 ```bash
 
-# cpu
 
+# CPU
 
 python -m pip install paddlepaddle==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
 
 
-# gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
 
+# gpu，requires GPU driver version ≥450.80.02 (Linux) or ≥452.39 (Windows)
 
 python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
 
 
-# gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
 
+# gpu，requires GPU driver version ≥545.23.06 (Linux) or ≥545.84 (Windows)
 
 python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
 
 ```
 
-> ❗For more PaddlePaddle versions, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html).
 
+> ❗No need to focus on the CUDA version on the physical machine, only the GPU driver version needs attention. For more information on PaddlePaddle Wheel versions, please refer to the [PaddlePaddle Official Website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/en/install/pip/linux-pip.html).
 
 
 * **Installing PaddleX**
 
 
@@ -566,38 +566,32 @@ One command can quickly experience the pipeline effect, the unified CLI format i
 
 paddlex --pipeline [Pipeline Name] --input [Input Image] --device [Running Device]
 
 ```
 
 
-You only need to specify three parameters:
 
-* `pipeline`: The name of the pipeline
 
-* `input`: The local path or URL of the input image to be processed
 
-* `device`: The GPU number used (for example, `gpu:0` means using the 0th GPU), you can also choose to use the CPU (`cpu`)
 
+Each Pipeline in PaddleX corresponds to specific parameters, which you can view in the respective Pipeline documentation for detailed explanations. Each Pipeline requires specifying three necessary parameters:
 
+* `pipeline`: The name of the Pipeline or the configuration file of the Pipeline
 
+* `input`: The local path, directory, or URL of the input file (e.g., an image) to be processed
 
+* `device`: The hardware device and its index to use (e.g., `gpu:0` indicates using the 0th GPU), or you can choose to use NPU (`npu:0`), XPU (`xpu:0`), CPU (`cpu`), etc.
 
 
 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
 
+paddlex --pipeline OCR \
 
+        --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png \
 
+        --use_doc_orientation_classify False \
 
+        --use_doc_unwarping False \
 
+        --use_textline_orientation False \
 
+        --save_path ./output \
 
+        --device gpu:0
 
 ```
 
 <details>
 
   <summary><b>👉 Click to view the running result</b></summary>
 
 
 ```bash
 
-{
 
-'input_path': '/root/.paddlex/predict_input/general_ocr_002.png',
 
-'dt_polys': [array([[161,  27],
 
-       [353,  22],
 
-       [354,  69],
 
-       [162,  74]], dtype=int16), array([[426,  26],
 
-       [657,  21],
 
-       [657,  58],
 
-       [426,  62]], dtype=int16), array([[702,  18],
 
-       [822,  13],
 
-       [824,  57],
 
-       [704,  62]], dtype=int16), array([[341, 106],
 
-       [405, 106],
 
-       [405, 128],
 
-       [341, 128]], dtype=int16)
 
-       ...],
 
-'dt_scores': [0.758478200014338, 0.7021546472698513, 0.8536622648391111, 0.8619181462164781, 0.8321051217096188, 0.8868756173427551, 0.7982964727675609, 0.8289939036796322, 0.8289428877522524, 0.8587063317632897, 0.7786755892491615, 0.8502032769081344, 0.8703346500042997, 0.834490931790065, 0.908291103353393, 0.7614978661708064, 0.8325774055997542, 0.7843421347676149, 0.8680889482955594, 0.8788859304537682, 0.8963341277518075, 0.9364654810069546, 0.8092413027028257, 0.8503743089091863, 0.7920740420391101, 0.7592224394793805, 0.7920547400069311, 0.6641757962457888, 0.8650289477605955, 0.8079483304467047, 0.8532207681055275, 0.8913377034754717],
 
-'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]}
 
+{'res': {'input_path': 'general_ocr_002.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': False}, 'angle': 0},'dt_polys': [array([[ 3, 10],
 
+       [82, 10],
 
+       [82, 33],
 
+       [ 3, 33]], dtype=int16), ...], 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': [-1, ...], 'text_rec_score_thresh': 0.0, 'rec_texts': ['www.99*', ...], 'rec_scores': [0.8980069160461426,  ...], 'rec_polys': [array([[ 3, 10],
 
+       [82, 10],
 
+       [82, 33],
 
+       [ 3, 33]], dtype=int16), ...], 'rec_boxes': array([[  3,  10,  82,  33], ...], dtype=int16)}}
 
 ```
 
 
 The visualization result is as follows:
 
@@ -606,30 +600,41 @@ The visualization result is as follows:
 
 
 </details>
 
 
-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:
 
+To use the command line for other pipelines, simply adjust the `pipeline` parameter to the name of the corresponding pipeline and modify the parameters accordingly. Below are the commands for each pipeline:
 
 
 <details>
 
   <summary><b>👉 More CLI usage for pipelines</b></summary>
 
 
 | Pipeline Name                | Command                                                                                                                                                                                    |
 
 |------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 
-| Image Classification | `paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0`                    |
 
-| Object Detection     | `paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --device gpu:0`                            |
 
-| Instance Segmentation| `paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0`                  |
 
-| Semantic Segmentation| `paddlex --pipeline semantic_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png --device gpu:0` |
 
-| Image Multi-label Classification | `paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0`        |
 
-| Small Object Detection       | `paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0`                            |
 
-| Image Anomaly Detection       | `paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0`                                              |
 
-| Pedestrian Attribute Recognition       | `paddlex --pipeline pedestrian_attribute --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --device gpu:0`                                              |
 
-| Vehicle Attribute Recognition       | `paddlex --pipeline vehicle_attribute --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --device gpu:0`                                              |
 
-| OCR                  | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0`                                                      |
 
-| Table Recognition    | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0`                                      |
 
-| Layout Parsing       | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0`                                      |
 
-| Formula Recognition       | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0`                                      |
 
-| Seal Recognition       | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0`                                      |
 
-| Time Series Forecasting | `paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0`                                                                   |
 
-| Time Series Anomaly Detection | `paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0`                                                                    |
 
-| Time Series Classification | `paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0`                                                                 |
 
+| General Image Classification       | `paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0 --save_path ./output/`                    |
 
+| General Object Detection         | `paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --threshold 0.5 --save_path ./output/ --device gpu:0`                            |
 
+| General Instance Segmentation    | `paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --threshold 0.5 --save_path ./output --device gpu:0`                  |
 
+| General Semantic Segmentation    | `paddlex --pipeline semantic_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png --target_size -1 --save_path ./output --device gpu:0` |
 
+| Image Multi-label Classification | `paddlex --pipeline image_multilabel_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --save_path ./output --device gpu:0`        |
 
+| Small Object Detection           | `paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --threshold 0.5 --save_path ./output --device gpu:0`                            |
 
+| Image Anomaly Detection          | `paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --save_path ./output --device gpu:0`                                              |
 
+| Pedestrian Attribute Recognition | `paddlex --pipeline pedestrian_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --save_path ./output/ --device gpu:0`                                              |
 
+| Vehicle Attribute Recognition    | `paddlex --pipeline vehicle_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --save_path ./output/ --device gpu:0`                                              |
 
+| 3D Multi-modal Fusion Detection  | `paddlex --pipeline 3d_bev_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/det_3d/demo_det_3d/nuscenes_demo_infer.tar --device gpu:0 --save_path ./output/`                    |
 
+| Human Keypoint Detection         | `paddlex --pipeline human_keypoint_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/keypoint_detection_001.jpg --det_threshold 0.5 --save_path ./output/ --device gpu:0`                    |
 
+| Open Vocabulary Detection        | `paddlex --pipeline open_vocabulary_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_detection.jpg --prompt "bus . walking man . rearview mirror ." --thresholds "{'text_threshold': 0.25, 'box_threshold': 0.3}" --save_path ./output --device gpu:0`                    |
 
+| Open Vocabulary Segmentation     | `paddlex --pipeline open_vocabulary_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_segmentation.jpg --prompt_type box --prompt "[[112.9,118.4,513.8,382.1],[4.6,263.6,92.2,336.6],[592.4,260.9,607.2,294.2]]" --save_path ./output --device gpu:0`                    |
 
+| Rotated Object Detection         | `paddlex --pipeline rotated_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png --threshold 0.5 --save_path ./output --device gpu:0`                    |
 
+| General OCR                      | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0`                                      |
 
+| Document Image Preprocessor      | `paddlex --pipeline doc_preprocessor --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/doc_test_rotated.jpg --use_doc_orientation_classify True --use_doc_unwarping True --save_path ./output --device gpu:0`                                                      |
 
+| General Table Recognition        | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0`                                      |
 
+| General Table Recognition v2     | `paddlex --pipeline table_recognition_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0`                                      |
 
+| General Layout Parsing           | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0`                      |
 
+| General Layout Parsing v2        | `paddlex --pipeline layout_parsing_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_v2_demo.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0`                      |
 
+| Formula Recognition              | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --use_layout_detection True --use_doc_orientation_classify False --use_doc_unwarping False --layout_threshold 0.5 --layout_nms True --layout_unclip_ratio  1.0 --layout_merge_bboxes_mode large --save_path ./output --device gpu:0`                                      |
 
+| Seal Text Recognition            | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --use_doc_orientation_classify False --use_doc_unwarping False --device gpu:0 --save_path ./output`                                      |
 
+| Time Series Forecasting       | `paddlex --pipeline ts_forecast --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0 --save_path ./output`                                                                   |
 
+| Time Series Anomaly Detection | `paddlex --pipeline ts_anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0 --save_path ./output`                                                                    |
 
+| Time Series Classification    | `paddlex --pipeline ts_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0 --save_path ./output`                                                                 |
 
+| Multilingual Speech Recognition   | `paddlex --pipeline multilingual_speech_recognition --input https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav --save_path ./output --device gpu:0`                                      |
 
+| General Video Classification   | `paddlex --pipeline video_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/general_video_classification_001.mp4 --topk 5 --save_path ./output --device gpu:0`                     |
 
+| General Video Detection       | `paddlex --pipeline video_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/HorseRiding.avi --device gpu:0 --save_path ./output`                     |
 
 
 </details>
 
 
@@ -649,10 +654,10 @@ for res in output:
 
 The following steps are executed:
 
 
 * `create_pipeline()` instantiates the pipeline object
 
-* Passes the image and calls the `predict` method of the pipeline object for inference prediction
 
+* Passes the image and calls the `predict()` method of the pipeline object for inference prediction
 
 * Processes the prediction results
 
 
-For other pipelines in Python scripts, just adjust the `pipeline` parameter of the `create_pipeline()` method to the corresponding name of the pipeline. Below is a list of each pipeline's corresponding parameter name and detailed usage explanation:
 
+To use the Python script for other pipelines, simply adjust the `pipeline` parameter in the `create_pipeline()` method to the name of the corresponding pipeline and modify the parameters accordingly. Below are the parameter names and detailed usage explanations for each pipeline:
 
 
 <details>
 
   <summary>👉 More Python script usage for pipelines</summary>
 
@@ -671,14 +676,25 @@ For other pipelines in Python scripts, just adjust the `pipeline` parameter of t
 
 | Face Recognition       | `face_recognition`                | [Face Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html)                              |
 
 | Pedestrian Attribute Recognition       | `pedestrian_attribute`                | [Pedestrian Attribute Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html)                              |
 
 |Vehicle Attribute Recognition       | `vehicle_attribute`                | [Vehicle Attribute Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html)                              |
 
-|  OCR            | `OCR` | [ OCR Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html) |
 
-|  Table Recognition       | `table_recognition` | [Table Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html) |
 
-| Layout Parsing       | `layout_parsing`                | [Layout Parsing Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html)                                   |
 
-| Formula Recognition       | `formula_recognition`                | [Formula Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html)                                   |
 
-| Seal Recognition       | `seal_recognition`                | [Seal Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)                 |
 
-|  Time Series Forecast       | `ts_forecast` | [ Time Series Forecast Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html) |
 
-|  Time Series Anomaly Detection   | `ts_anomaly_detection` | [ Time Series Anomaly Detection Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html) |
 
-|  Time Series Classification       | `ts_cls` | [ Time Series Classification Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html) |
 
+| 3D Multi-modal Fusion Detection | `3d_bev_detection` | [Instructions for Using the 3D Multi-modal Fusion Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html#222-python-script-integration) |
 
+| Human Keypoint Detection | `human_keypoint_detection` | [Instructions for Using the Human Keypoint Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html#222-python-script-integration) |
 
+| Open Vocabulary Detection | `open_vocabulary_detection` | [Instructions for Using the Open Vocabulary Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html#212-python-script-integration) |
 
+| Open Vocabulary Segmentation | `open_vocabulary_segmentation` | [Instructions for Using the Open Vocabulary Segmentation Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html#212-python-script-integration) |
 
+| Rotated Object Detection | `rotated_object_detection` | [Instructions for Using the Rotated Object Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html#212-python-script-integration) |
 
+| OCR | `OCR` | [Instructions for Using the General OCR Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html#222-python-script-integration) |
 
+| Document Image Preprocessing | `doc_preprocessor` | [Instructions for Using the Document Image Preprocessing Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html#212-python-script-integration) |
 
+| General Table Recognition | `table_recognition` | [Instructions for Using the General Table Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python-script-integration) |
 
+| General Table Recognition v2 | `table_recognition_v2` | [Instructions for Using the General Table Recognition v2 Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html#22-python-script-integration) |
 
+| General Layout Parsing | `layout_parsing` | [Instructions for Using the General Layout Parsing Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python-script-integration) |
 
+| General Layout Parsing v2 | `layout_parsing_v2` | [Instructions for Using the General Layout Parsing v2 Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html#22-python-script-integration) |
 
+| Formula Recognition | `formula_recognition` | [Instructions for Using the Formula Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python-script-integration) |
 
+| Seal Text Recognition | `seal_recognition` | [Instructions for Using the Seal Text Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python-script-integration) |
 
+| Time Series Forecasting | `ts_forecast` | [Instructions for Using the Time Series Forecasting Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html#222-python-script-integration) |
 
+| Time Series Anomaly Detection | `ts_anomaly_detection` | [Instructions for Using the Time Series Anomaly Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html#222-python-script-integration) |
 
+| Time Series Classification | `ts_classification` | [Instructions for Using the Time Series Classification Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html#222-python-script-integration) |
 
+| Multilingual Speech Recognition | `multilingual_speech_recognition` | [Instructions for Using the Multilingual Speech Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/multilingual_speech_recognition.html#212-python-script-integration) |
 
+| General Video Classification | `video_classification` | [Instructions for Using the General Video Classification Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/video_classification.html#22-python-script-integration) |
 
+| General Video Detection | `video_detection` | [Instructions for Using the General Video Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/video_detection.html#212-python-script-integration) |
 
 </details>
 
 
 ## 📖 Documentation
 
@@ -744,12 +760,12 @@ For other pipelines in Python scripts, just adjust the `pipeline` parameter of t
 
   </details>
 
 
 * <details open>
 
-    <summary> <b> 🎤 Speech Analysis</b> </summary>
 
+    <summary> <b> 🎤 Speech Recognition</b> </summary>
 
 
     * [🌐 Multilingual Speech Recognition Pipeline Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/speech_pipelines/multilingual_speech_recognition.html)
 
 
 * <details open>
 
-    <summary> <b> 🎥 Video Processing</b> </summary>
 
+    <summary> <b> 🎥 Video Recognition</b> </summary>
 
 
     * [📈 General Video Classification Pipeline Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/video_pipelines/video_classification.html)
 
     * [🔍 General Video Detection Pipeline Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/video_pipelines/video_detection.html)
 
@@ -809,18 +825,36 @@ For other pipelines in Python scripts, just adjust the `pipeline` parameter of t
 
   * [🔍 Mainbody Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/mainbody_detection.html)
 
   * [🚶 Pedestrian Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/human_detection.html)
 
   * [🚗 Vehicle Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/vehicle_detection.html)
 
-  * [🚶‍♂️ Human Keypoint Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/human_keypoint_detection.html)
 
-  * [🌐 Open-Vocabulary Object Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/open_vocabulary_detection.html)
 
   * [🔄 Rotated Object Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/rotated_object_detection.html)
 
 
   </details>
 
 
 * <details open>
 
+  <summary> <b> 🌐 Open-Vocabulary Object Detection </b></summary>
 
+
 
+  * [🌐 Open-Vocabulary Object Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/open_vocabulary_detection.html)
 
+</details>
 
+
 
+* <details open>
 
+  <summary> <b> 🎯 Keypoint Detection </b></summary>
 
+
 
+  * [🚶‍♂️ Human Keypoint Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/human_keypoint_detection.html)
 
+   </details>
 
+
 
+
 
+
 
+
 
+* <details open>
 
   <summary> <b> 🖼️ Image Segmentation </b></summary>
 
 
   * [🗺️ Semantic Segmentation Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/semantic_segmentation.html)
 
   * [🔍 Instance Segmentation Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/instance_segmentation.html)
 
   * [🚨 Image Anomaly Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/anomaly_detection.html)
 
+  </details>
 
+
 
+* <details open>
 
+  <summary> <b> 🌐 Open-Vocabulary Segmentation </b></summary>
 
+
 
   * [🌐 Open-Vocabulary Segmentation Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.html)
 
   </details>
 
 
@@ -833,17 +867,17 @@ For other pipelines in Python scripts, just adjust the `pipeline` parameter of t
 
   </details>
 
 
 * <details open>
 
-  <summary> <b> 🌐 3D </b></summary>
 
+  <summary> <b> 📦 3D  </b></summary>
 
 
-  * [🚗 3D Multimodal Fusion Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/3d_bev_detection.html)
 
+  * [📦 3D Multimodal Fusion Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/3d_bev_detection.html)
 
 
 * <details open>
 
-  <summary> <b> 🎤 Speech </b></summary>
 
+  <summary> <b> 🎤 Speech Recognition </b></summary>
 
 
   * [🌐 Multilingual Speech Recognition Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/speech_modules/multilingual_speech_recognition.html)
 
 
 * <details open>
 
-  <summary> <b> 🎥 Video </b></summary>
 
+  <summary> <b> 🎥 Video Recognition </b></summary>
 
 
   * [📈 Video Classification Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/video_modules/video_classification.html)
 
   * [🔍 Video Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/video_modules/video_detection.html)

+ 16 - 3
docs/module_usage/tutorials/cv_modules/3d_bev_detection.en.md
Bestand weergeven 

@@ -26,7 +26,21 @@ The 3D multimodal fusion detection module is a key component in the fields of co
 
 <tr>
 
 </table>
 
 
-<p><b>Note: The above accuracy metrics are based on the <a href="https://www.nuscenes.org/nuscenes">nuscenes</a> validation set with mAP(0.5:0.95) and NDS 60.9, and the precision type is FP32.</b></p>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The above accuracy metrics are based on the <a href="https://www.nuscenes.org/nuscenes">nuscenes</a> validation set with mAP(0.5:0.95) and NDS 60.9, and the precision type is FP32.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package first. For details, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
 
@@ -46,7 +60,7 @@ for res in output:
 
     res.visualize(save_path="./output/", show=True) ## 3D result visualization. If the runtime environment has a graphical interface, set `show=True`; otherwise, set it to `False`.
 
 ```
 
 
-<b>Note: </b>  
+<b>Note: </b>
 
 1、To visualize 3D detection results, you need to install the open3d package first. The installation command is as follows:
 
 ```bash
 
 pip install open3d
 
@@ -408,4 +422,3 @@ The 3D multimodal fusion detection module can be integrated into the 3D detectio
 
 2.<b>Module Integration</b>
 
 
 The weights you generate can be directly integrated into the 3D multimodal fusion detection module. You can refer to the Python example code in [Quick Integration](). Just replace the model with the path of the model you have trained.
 
-

+ 18 - 2
docs/module_usage/tutorials/cv_modules/3d_bev_detection.md
Bestand weergeven 

@@ -30,8 +30,24 @@ comments: true
 
 
 </table>
 
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://www.nuscenes.org/nuscenes">nuscenes</a>验证集 mAP(0.5:0.95), NDS 60.9, 精度类型为 FP32。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
-<p><b>注：以上精度指标为<a href="https://www.nuscenes.org/nuscenes">nuscenes</a>验证集 mAP(0.5:0.95), NDS 60.9, 精度类型为 FP32。</b></p></details>
 
 
 
 ## 三、快速集成
 
@@ -51,7 +67,7 @@ for res in output:
 
     res.visualize(save_path="./output/", show=True) ## 3d结果可视化，如果运行环境有图形界面设置show=True，否则设置为False
 
 ```
 
 
-<b>注：</b>   
+<b>注：</b>
 
 1、3d检测结果可视化需要先安装open3d包，安装命令如下：
 
 ```bash
 
 pip install open3d

+ 16 - 1
docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md
Bestand weergeven 

@@ -28,7 +28,22 @@ Unsupervised anomaly detection is a technology that automatically identifies and
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>The above model accuracy indicators are measured from the MVTec_AD dataset.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The above model accuracy indicators are measured from the MVTec_AD dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration  <a id="quick"> </a>
 
 Before quick integration, you need to install the PaddleX wheel package. For the installation method of the wheel package, please refer to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md). After installing the wheel package, a few lines of code can complete the inference of the unsupervised anomaly detection module. You can switch models under this module freely, and you can also integrate the model inference of the unsupervised anomaly detection module into your project. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png) to your local machine.

+ 16 - 1
docs/module_usage/tutorials/cv_modules/anomaly_detection.md
Bestand weergeven 

@@ -28,7 +28,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>以上模型精度指标测量自 MVTec_AD 数据集中的grid类别。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：MVTec_AD 数据集中的grid类别。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/cv_modules/face_detection.en.md
Bestand weergeven 

@@ -56,7 +56,22 @@ Face detection is a fundamental task in object detection, aiming to automaticall
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640. GPU inference time is based on an NVIDIA V100 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## III. Quick Integration  <a id="quick"> </a>

+ 16 - 2
docs/module_usage/tutorials/cv_modules/face_detection.md
Bestand weergeven 

@@ -55,8 +55,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p>注：以上精度指标是在 COCO 格式的 WIDER-FACE 验证集上，以640
 
-*640作为输入尺寸评估得到的。所有模型 GPU 推理耗时基于 NVIDIA V100 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz，精度类型为 FP32。</p>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：COCO 格式的 WIDER-FACE 验证集上，以640*640作为输入尺寸评估得到。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 17 - 2
docs/module_usage/tutorials/cv_modules/face_feature.en.md
Bestand weergeven 

@@ -42,7 +42,22 @@ Face feature models typically take standardized face images processed through de
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p>Note: The above accuracy metrics are Accuracy scores measured on the AgeDB-30, CFP-FP, and LFW datasets, respectively. All model GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</p>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The above accuracy metrics are Accuracy scores measured on the AgeDB-30, CFP-FP, and LFW datasets.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For details, refer to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md)
 
@@ -417,4 +432,4 @@ The face feature module can be integrated into the PaddleX pipeline for [<b>Face
 
 
 The weights you produced can be directly integrated into the face feature module. You can refer to the Python example code in [Quick Integration](#III.-Quick-Integration) and only need to replace the model with the path to the model you trained.
 
 
-You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).
 
+You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).

+ 16 - 1
docs/module_usage/tutorials/cv_modules/face_feature.md
Bestand weergeven 

@@ -43,7 +43,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p>注：以上精度指标是分别在AgeDB-30、CFP-FP和LFW数据集上测得的Accuracy。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</p>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：分别在AgeDB-30、CFP-FP和LFW数据集上测得。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/human_detection.en.md
Bestand weergeven 

@@ -38,7 +38,22 @@ Human detection is a subtask of object detection, which utilizes computer vision
 
 <td>28.79</td>
 
 </tr>
 
 </table>
 
-<b>Note: The evaluation set for the above accuracy metrics is CrowdHuman dataset mAP(0.5:0.95). GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The evaluation set for the above accuracy metrics is CrowdHuman dataset mAP(0.5:0.95).
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## III. Quick Integration

+ 16 - 1
docs/module_usage/tutorials/cv_modules/human_detection.md
Bestand weergeven 

@@ -37,7 +37,22 @@ comments: true
 
 <td>28.79</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为CrowdHuman数据集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：CrowdHuman数据集 mAP(0.5:0.95)。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 15 - 1
docs/module_usage/tutorials/cv_modules/human_keypoint_detection.en.md
Bestand weergeven 

@@ -43,7 +43,21 @@ Keypoint detection algorithms mainly include two approaches: Top-Down and Bottom
 
   </tr>
 
 </table>
 
 
-**Note: The above accuracy metrics are based on the COCO dataset AP(0.5:0.95) using ground truth annotations for bounding boxes. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision, while CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.**
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The above accuracy metrics are based on the COCO dataset AP(0.5:0.95) using ground truth annotations for bounding boxes.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package first. For details, please refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 15 - 1
docs/module_usage/tutorials/cv_modules/human_keypoint_detection.md
Bestand weergeven 

@@ -43,7 +43,21 @@ comments: true
 
   </tr>
 
 </table>
 
 
-**注：以上精度指标为COCO数据集 AP(0.5:0.95)，所依赖的检测框为ground truth标注得到。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。**
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：COCO数据集 AP(0.5:0.95)，所依赖的检测框为ground truth标注得到。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 18 - 1
docs/module_usage/tutorials/cv_modules/image_classification.en.md
Bestand weergeven 

@@ -677,7 +677,24 @@ The image classification module is a crucial component in computer vision system
 
 <td>100.1 M</td>
 
 </tr>
 
 </tr></tr></tr></tr></table>
 
-<p><b>Note: The above accuracy metrics refer to Top-1 Accuracy on the <a href="https://www.image-net.org/index.php">ImageNet-1k</a> validation set. </b><b>All model GPU inference times are based on NVIDIA Tesla T4 machines, with precision type FP32. CPU inference speeds are based on Intel® Xeon® Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://www.image-net.org/index.php">ImageNet-1k</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## <span id="lable">III. Quick Integration</span>
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).

+ 18 - 1
docs/module_usage/tutorials/cv_modules/image_classification.md
Bestand weergeven 

@@ -672,7 +672,24 @@ comments: true
 
 <td>100.1 M</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为 <a href="https://www.image-net.org/index.php">ImageNet-1k</a> 验证集 Top1 Acc。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://www.image-net.org/index.php">ImageNet-1k</a> 验证集 Top1 Acc。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)。

+ 16 - 1
docs/module_usage/tutorials/cv_modules/image_feature.en.md
Bestand weergeven 

@@ -42,7 +42,22 @@ The image feature module is one of the important tasks in computer vision, prima
 
 <td>1.05 G</td>
 
 </tr>
 
 </table>
 
-<b>Note: The above accuracy metrics are Recall@1 from AliProducts. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Custom Dataset Creation.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/image_feature.md
Bestand weergeven 

@@ -42,7 +42,22 @@ comments: true
 
 <td>1.05 G</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为 AliProducts recall@1。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX自建数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md
Bestand weergeven 

@@ -66,7 +66,22 @@ The image multi-label classification module is a crucial component in computer v
 
 <td>ResNet50_ML is an image multi-label classification model based on ResNet50, which significantly improves accuracy on multi-label classification tasks by incorporating an ML-Decoder.</td>
 
 </tr>
 
 </table>
 
-<b>Note: The above accuracy metrics are mAP for the multi-label classification task on [COCO2017](https://cocodataset.org/#home).</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:  multi-label classification task on [COCO2017](https://cocodataset.org/#home).
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/image_multilabel_classification.md
Bestand weergeven 

@@ -66,7 +66,22 @@ comments: true
 
 <td>ResNet50_ML是一种基于ResNet50的图像多标签分类模型，通过结合ML-Decoder，显著提升了模型在图像多标签分类任务上的准确性。</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为[COCO2017](https://cocodataset.org/#home)的多标签分类任务mAP。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：[COCO2017](https://cocodataset.org/#home)的多标签分类任务。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
  > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 18 - 1
docs/module_usage/tutorials/cv_modules/instance_segmentation.en.md
Bestand weergeven 

@@ -159,7 +159,24 @@ The instance segmentation module is a crucial component in computer vision syste
 
 <td> SOLOv2 is a real-time instance segmentation algorithm that segments objects by location. This model is an improved version of SOLO, achieving a good balance between accuracy and speed through the introduction of mask learning and mask NMS.</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The above accuracy metrics are based on the Mask AP of the <a href="https://cocodataset.org/#home">COCO2017</a> validation set. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://cocodataset.org/#home">COCO2017</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## <span id="lable">III. Quick Integration</span>
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md)

+ 18 - 1
docs/module_usage/tutorials/cv_modules/instance_segmentation.md
Bestand weergeven 

@@ -159,7 +159,24 @@ comments: true
 
 <td> SOLOv2 是一种按位置分割物体的实时实例分割算法。该模型是SOLO的改进版本，通过引入掩码学习和掩码NMS，实现了精度和速度上取得良好平衡。</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为<a href="https://cocodataset.org/#home">COCO2017</a>验证集 Mask AP。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://cocodataset.org/#home">COCO2017</a>验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/mainbody_detection.en.md
Bestand weergeven 

@@ -30,7 +30,22 @@ Mainbody detection is a fundamental task in object detection, aiming to identify
 
 <td>A mainbody detection model based on PicoDet_LCNet_x2_5, which may detect multiple common subjects simultaneously.</td>
 
 </tr>
 
 </table>
 
-<b>Note: The evaluation set for the above accuracy metrics is  PaddleClas mainbody detection dataset mAP(0.5:0.95). GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleClas mainbody detection dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration  <a id="quick"> </a>
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 15 - 1
docs/module_usage/tutorials/cv_modules/mainbody_detection.md
Bestand weergeven 

@@ -31,7 +31,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-注：以上精度指标为 PaddleClas主体检测数据集  mAP(0.5:0.95)。
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleClas主体检测数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 18 - 1
docs/module_usage/tutorials/cv_modules/object_detection.en.md
Bestand weergeven 

@@ -347,7 +347,24 @@ The object detection module is a crucial component in computer vision systems, r
 
 <td>351.5 M</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The precision metrics mentioned are based on the <a href="https://cocodataset.org/#home">COCO2017</a> validation set mAP(0.5:0.95). All model GPU inference times are measured on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:  <a href="https://cocodataset.org/#home">COCO2017</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## III. Quick Integration

+ 18 - 1
docs/module_usage/tutorials/cv_modules/object_detection.md
Bestand weergeven 

@@ -362,7 +362,24 @@ comments: true
 
 <td>187 M</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为<a href="https://cocodataset.org/#home">COCO2017</a>验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://cocodataset.org/#home">COCO2017</a>验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 15 - 1
docs/module_usage/tutorials/cv_modules/open_vocabulary_detection.en.md
Bestand weergeven 

@@ -30,7 +30,21 @@ Open-vocabulary object detection is an advanced object detection technology aime
 
 </tr>
 
 </table>
 
 
-**Note: The above accuracy metrics are based on the COCO val2017 validation set mAP(0.5:0.95). All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision, while CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.**
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: Based on the open vocabulary object detection model trained on the three datasets: O365, GoldG, and Cap4M.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package first. For details, please refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).

+ 15 - 1
docs/module_usage/tutorials/cv_modules/open_vocabulary_detection.md
Bestand weergeven 

@@ -31,7 +31,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<b>注：以上精度指标为 COCO val2017 验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32</b>。
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：基于O365,GoldG,Cap4M三个数据集训练的开放词汇目标检测模型。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 14 - 1
docs/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.en.md
Bestand weergeven 

@@ -32,7 +32,20 @@ Open-vocabulary segmentation is an image segmentation task that aims to segment
 
 </tr>
 
 </table>
 
 
-<b>Note: All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision, while CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For details, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).

+ 14 - 1
docs/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.md
Bestand weergeven 

@@ -33,7 +33,20 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<b>注：所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32</b>。
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md
Bestand weergeven 

@@ -32,7 +32,22 @@ Pedestrian attribute recognition is a crucial component in computer vision syste
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are mA on PaddleX's internal self-built dataset. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX's internal self-built dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## <span id="lable">III. Quick Integration</span>
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md
Bestand weergeven 

@@ -32,7 +32,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标为 PaddleX 内部自建数据集 mA。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX 内部自建数据集
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/rotated_object_detection.en.md
Bestand weergeven 

@@ -27,7 +27,22 @@ Rotated object detection is a derivative of the object detection module, specifi
 
 <td rowspan="1">PP-YOLOE-R is an efficient single-stage Anchor-free rotated box detection model. Based on PP-YOLOE, PP-YOLOE-R introduces a series of useful designs to improve detection accuracy with minimal parameters and computational cost.</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The above accuracy metrics are on the <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set mAP(0.5:0.95)。All model GPU inference times are based on an NVIDIA TRX2080 Ti machine, with precision type F16, and CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.</b></p>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## III. Quick Integration

+ 13 - 1
docs/module_usage/tutorials/cv_modules/rotated_object_detection.md
Bestand weergeven 

@@ -28,9 +28,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<p><b>注：以上精度指标为<a href="https://captain-whu.github.io/DOTA/">DOTA</a>验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA TRX2080 Ti 机器，精度类型为 F16， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+**测试环境说明：**
 
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://captain-whu.github.io/DOTA/">DOTA</a>验证集
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 18 - 1
docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md
Bestand weergeven 

@@ -206,7 +206,24 @@ Semantic segmentation is a technique in computer vision that classifies each pix
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>The accuracy metrics of the above models are measured on the <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset and <a href="https://www.cityscapes-dataset.com/">Cityscapes</a>dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 18 - 1
docs/module_usage/tutorials/cv_modules/semantic_segmentation.md
Bestand weergeven 

@@ -207,7 +207,24 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>以上模型的精度指标测量自<a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a>数据集。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a>数据集及 <a href="https://www.cityscapes-dataset.com/">Cityscapes</a>数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/small_object_detection.en.md
Bestand weergeven 

@@ -45,7 +45,22 @@ Small object detection typically refers to accurately detecting and locating sma
 
 <td>340.42</td>
 
 </tr>
 
 </table>
 
-<b>Note: The evaluation set for the above accuracy metrics is VisDrone-DET dataset mAP(0.5:0.95). GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: VisDrone-DET dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## III. Quick Integration  <a id="quick"> </a>

+ 16 - 1
docs/module_usage/tutorials/cv_modules/small_object_detection.md
Bestand weergeven 

@@ -46,7 +46,22 @@ comments: true
 
 <td>340.42</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为 VisDrone-DET 验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32</b>。
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：VisDrone-DET 验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md
Bestand weergeven 

@@ -32,7 +32,22 @@ Vehicle attribute recognition is a crucial component in computer vision systems.
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are mA on the VeRi dataset. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: VeRi dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## <span id="lable">III. Quick Integration</span>

+ 16 - 1
docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md
Bestand weergeven 

@@ -32,7 +32,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标为 VeRi 数据集mA。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：VeRi 数据集
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/cv_modules/vehicle_detection.en.md
Bestand weergeven 

@@ -34,8 +34,23 @@ Vehicle detection is a subtask of object detection, specifically referring to th
 
 <td>176.60 / 176.60</td>
 
 <td>196.02</td>
 
 </tr>
 
-<b>Note: The evaluation set for the above accuracy metrics is PPVehicle dataset mAP(0.5:0.95). GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+</table>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PPVehicle dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/cv_modules/vehicle_detection.md
Bestand weergeven 

@@ -35,7 +35,22 @@ comments: true
 
 <td>196.02</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为PPVehicle 验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PPVehicle 验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md
Bestand weergeven 

@@ -32,7 +32,22 @@ The document image orientation classification module is aim to distinguish the o
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are evaluated on a self-built dataset covering various scenarios such as IDs and documents, containing 1000 images. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: Self-built multi-scene dataset (1000 images, including ID cards/documents, etc.)
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration

+ 16 - 0
docs/module_usage/tutorials/ocr_modules/formula_recognition.en.md
Bestand weergeven 

@@ -47,6 +47,22 @@ The formula recognition module is a crucial component of OCR (Optical Character
 
 
 <b>Note: The above accuracy metrics are measured using an internally built formula recognition test set within PaddleX. The BLEU score of LaTeX_OCR_rec on the LaTeX-OCR formula recognition test set is 0.8821. All model GPU inference times are based on machines with Tesla V100 GPUs, with precision type FP32.</b>
 
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Internal Self-built Formula Recognition Test Set
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For details, please refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 15 - 1
docs/module_usage/tutorials/ocr_modules/formula_recognition.md
Bestand weergeven 

@@ -43,7 +43,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<b>注：以上精度指标测量自 PaddleX 内部自建公式识别测试集。LaTeX_OCR_rec在LaTeX-OCR公式识别测试集的BLEU score为 0.8821。所有模型 GPU 推理耗时基于 Tesla V100 GPUs 机器，精度类型为 FP32。</b>
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX 内部自建公式识别测试集
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 22 - 5
docs/module_usage/tutorials/ocr_modules/layout_detection.en.md
Bestand weergeven 

@@ -48,7 +48,6 @@ The core task of structure analysis is to parse and segment the content of input
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The evaluation dataset for the above precision metrics is a self-built layout area detection dataset by PaddleOCR, containing 500 common document-type images of Chinese and English papers, magazines, contracts, books, exams, and research reports. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
 
 > ❗ The above list includes the <b>3 core models</b> that are key supported by the text recognition module. The module actually supports a total of <b>11 full models</b>, including several predefined models with different categories. The complete model list is as follows:
 
 
@@ -76,7 +75,6 @@ The core task of structure analysis is to parse and segment the content of input
 
 <td>A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting table regions.</td>
 
 </tr>
 
 </tbody></table>
 
-<b>Note: The evaluation dataset for the above precision metrics is a self-built layout table area detection dataset by PaddleOCR, containing 7835 Chinese and English document images with tables. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
 
 * <b>3-Class Layout Detection Model, including Table, Image, and Stamp</b>
 
 <table>
 
@@ -116,7 +114,6 @@ The core task of structure analysis is to parse and segment the content of input
 
 <td>A high-precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H.</td>
 
 </tr>
 
 </tbody></table>
 
-<b>Note: The evaluation dataset for the above precision metrics is a self-built layout area detection dataset by PaddleOCR, containing 1154 common document images of Chinese and English papers, magazines, and research reports. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
 
 * <b>5-Class English Document Area Detection Model, including Text, Title, Table, Image, and List</b>
 
 <table>
 
@@ -140,7 +137,6 @@ The core task of structure analysis is to parse and segment the content of input
 
 <td>A high-efficiency English document layout area localization model trained on the PubLayNet dataset using PicoDet-1x.</td>
 
 </tr>
 
 </tbody></table>
 
-<b>Note: The evaluation dataset for the above precision metrics is the [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) dataset, containing 11245 English document images. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
 
 * <b>17-Class Area Detection Model, including 17 common layout categories: Paragraph Title, Image, Text, Number, Abstract, Content, Figure Caption, Formula, Table, Table Caption, References, Document Title, Footnote, Header, Algorithm, Footer, and Stamp</b>
 
 <table>
 
@@ -181,7 +177,28 @@ The core task of structure analysis is to parse and segment the content of input
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The evaluation dataset for the above precision metrics is a self-built layout area detection dataset by PaddleOCR, containing 892 common document images of Chinese and English papers, magazines, and research reports. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:
 
+    - Layout Detection Model: A self-built layout area detection dataset by PaddleOCR, containing 500 common document type images such as Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
 
+    - Table Layout Detection Model: A self-built table area detection dataset by PaddleOCR, including 7,835 Chinese and English paper document type images with tables.
 
+    - 3-Class Layout Detection Model: A self-built layout area detection dataset by PaddleOCR, comprising 1,154 common document type images such as Chinese and English papers, magazines, and research reports.
 
+    - 5-Class English Document Area Detection Model: The evaluation dataset of [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet), containing 11,245 images of English documents.
 
+    - 17-Class Area Detection Model: A self-built layout area detection dataset by PaddleOCR, including 892 common document type images such as Chinese and English papers, magazines, and research reports.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
 </details>

+ 21 - 5
docs/module_usage/tutorials/ocr_modules/layout_detection.md
Bestand weergeven 

@@ -48,7 +48,6 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标的评估集是 PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志、合同、书本、试卷和研报等常见的 500 张文档类型图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
 
 
 > ❗ 以上列出的是版面检测模块重点支持的<b>3个核心模型</b>，该模块总共支持<b>11个全量模型</b>，包含多个预定义了不同类别的模型，完整的模型列表如下：
 
@@ -77,7 +76,6 @@ comments: true
 
 <td>基于PicoDet-1x在自建数据集训练的高效率版面区域定位模型，可定位表格这1类区域</td>
 
 </tr>
 
 </tbody></table>
 
-<b>注：以上精度指标的评估集是 PaddleOCR 自建的版面表格区域检测数据集，包含中英文 7835 张带有表格的论文文档类型图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
 
 * <b>3类版面检测模型，包含表格、图像、印章</b>
 
 <table>
 
@@ -117,7 +115,6 @@ comments: true
 
 <td>基于RT-DETR-H在中英文论文、杂志和研报等场景上自建数据集训练的高精度版面区域定位模型</td>
 
 </tr>
 
 </tbody></table>
 
-<b>注：以上精度指标的评估集是 PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 1154 张文档类型图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
 
 * <b>5类英文文档区域检测模型，包含文字、标题、表格、图片以及列表</b>
 
 <table>
 
@@ -141,7 +138,6 @@ comments: true
 
 <td>基于PicoDet-1x在PubLayNet数据集训练的高效率英文文档版面区域定位模型</td>
 
 </tr>
 
 </tbody></table>
 
-<b>注：以上精度指标的评估集是 [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet/) 的评估数据集，包含英文文档的 11245 张文图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
 
 * <b>17类区域检测模型，包含17个版面常见类别，分别是：段落标题、图片、文本、数字、摘要、内容、图表标题、公式、表格、表格标题、参考文献、文档标题、脚注、页眉、算法、页脚、印章</b>
 
 <table>
 
@@ -182,7 +178,27 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标的评估集是 PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 892 张文档类型图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：
 
+    - 版面检测模型： PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志、合同、书本、试卷和研报等常见的 500 张文档类型图片。
 
+    - 表格版面检测模型：PaddleOCR 自建的版面表格区域检测数据集，包含中英文 7835 张带有表格的论文文档类型图片。
 
+    - 3类版面检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 1154 张文档类型图片。
 
+    - 5类英文文档区域检测模型：[PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet) 的评估数据集，包含英文文档的 11245 张文图片。
 
+    - 17类区域检测模型：PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 892 张文档类型图片。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 </details>
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/seal_text_detection.en.md
Bestand weergeven 

@@ -40,7 +40,22 @@ The seal text detection module typically outputs multi-point bounding boxes arou
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The evaluation set for the above accuracy metrics is a self-built dataset containing 500 circular seal images. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Custom Dataset, Containing 500 Images of Circular Stamps.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## III. Quick Integration

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/seal_text_detection.md
Bestand weergeven 

@@ -40,7 +40,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标的评估集是自建的数据集，包含500张圆形印章图像。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX自建数据集，包含500张圆形印章图像。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 20 - 3
docs/module_usage/tutorials/ocr_modules/table_cells_detection.en.md
Bestand weergeven 

@@ -34,7 +34,21 @@ The table cell detection module is a key component of table recognition tasks, r
 
 </tr>
 
 </table>
 
 
-<p><b>Note: The above accuracy metrics are measured from the internal table cell detection dataset of PaddleX. All model GPU inference times are based on an NVIDIA Tesla T4 machine, with precision type FP32. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32. Considering that the table cell detection module needs to be integrated into the table recognition pipeline v2 for practical applications, the table cell detection results output from the table recognition pipeline v2 are used to calculate the mAP accuracy.</b></p>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Internal Self-built Evaluation Dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package first. For details, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
 
@@ -241,7 +255,7 @@ The following is the explanation of the methods, parameters, etc.:
 
 </table>
 
 
 * Additionally, it also supports obtaining visualized images with results and prediction results through attributes, as follows:
 
-  
+
 
 <table>
 
 <thead>
 
 <tr>
 
@@ -491,5 +505,8 @@ The table cell detection module can be integrated into the PaddleX pipeline [Gen
 
 2.<b>Module Integration</b>
 
 
 The weights you generate can be directly integrated into the table cell detection module. You can refer to the Python example code in [Quick Integration](#3-Quick-Integration). Simply replace the model with the path of the model you have trained.
 
+<<<<<<< HEAD
 
 
-You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).
 
+You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).
 
+=======
 
+>>>>>>> update docs of benchmark

+ 15 - 1
docs/module_usage/tutorials/ocr_modules/table_cells_detection.md
Bestand weergeven 

@@ -34,7 +34,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<p><b>注：以上精度指标测量自 PaddleX 内部自建评测集。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。考虑到实际应用时，表格单元格检测模块需要被集成在表格识别产线v2中使用，因此采用表格识别产线v2输出的表格单元格检测结果计算 mAP 精度。</b></p>
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX 内部自建评测集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)

+ 17 - 3
docs/module_usage/tutorials/ocr_modules/table_classification.en.md
Bestand weergeven 

@@ -26,12 +26,26 @@ The table classification module is a key component of a computer vision system,
 
 </tr>
 
 </table>
 
 
-<p><b>Note: The above accuracy metrics are measured from the internal table classification dataset built by PaddleX. All models' GPU inference time is based on an NVIDIA Tesla T4 machine, with a precision type of FP32. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and a precision type of FP32.</b></p>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Internal Self-built Evaluation Dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package first. For details, please refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
 
 
-After installing the wheel package, you can complete the inference of the table classification module with just a few lines of code. You can switch between models under this module at will, and you can also integrate the model inference of the table classification module into your project. Before running the following code, please download the [example image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg) to your local machine. 
+After installing the wheel package, you can complete the inference of the table classification module with just a few lines of code. You can switch between models under this module at will, and you can also integrate the model inference of the table classification module into your project. Before running the following code, please download the [example image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg) to your local machine.
 
 
 ```python
 
 from paddlex import create_model
 
@@ -405,4 +419,4 @@ The table classification module can be integrated into the PaddleX pipeline such
 
 
 The weights you generate can be directly integrated into the table classification module. You can refer to the Python example code in [Quick Integration](#Three-Quick-Integration). Just replace the model with the path of the model you have trained.
 
 
-You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).
 
+You can also use the PaddleX high-performance inference plugin to optimize the inference process of your model and further improve efficiency. For detailed procedures, please refer to the [PaddleX High-Performance Inference Guide](../../../pipeline_deploy/high_performance_inference.en.md).

+ 15 - 1
docs/module_usage/tutorials/ocr_modules/table_classification.md
Bestand weergeven 

@@ -27,7 +27,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<p><b>注：以上精度指标测量自 PaddleX 内部自建评测数据集。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX 内部自建评测数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)。

+ 15 - 1
docs/module_usage/tutorials/ocr_modules/table_structure_recognition.en.md
Bestand weergeven 

@@ -52,7 +52,21 @@ SLANet_plus is an enhanced version of SLANet, a table structure recognition mode
 
 </tr>
 
 </table>
 
 
-<b>Note: The above accuracy metrics are evaluated on a self-built English table recognition dataset by PaddleX. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Internal Self-built High-difficulty Chinese Table Recognition Dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 
 ## III. Quick Integration

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md
Bestand weergeven 

@@ -49,7 +49,22 @@ comments: true
 
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/SLANeXt_wireless_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wireless_pretrained.pdparams">训练模型</a></td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标测量自 PaddleX 内部自建高难度中文表格识别数据集。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX 内部自建高难度中文表格识别数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 0
docs/module_usage/tutorials/ocr_modules/text_detection.en.md
Bestand weergeven 

@@ -39,6 +39,22 @@ The text detection module is a crucial component in OCR (Optical Character Recog
 
 </tbody>
 
 </table>
 
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: addleOCR Self-built Dataset for Chinese and English, Covering Various Scenarios
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/text_detection.md
Bestand weergeven 

@@ -56,7 +56,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标的评估集是 PaddleOCR 自建的中英文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 593 张图片。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleOCR 自建的中英文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 593 张图片。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)。

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/text_image_unwarping.en.md
Bestand weergeven 

@@ -27,7 +27,22 @@ The primary purpose of Text Image Unwarping is to perform geometric transformati
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>The accuracy metrics of the above models are measured on the [DocUNet benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) dataset.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: [DocUNet benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/text_image_unwarping.md
Bestand weergeven 

@@ -29,7 +29,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>模型的精度指标测量自 [DocUNet benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html)。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：[DocUNet benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html)数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 24 - 4
docs/module_usage/tutorials/ocr_modules/text_recognition.en.md
Bestand weergeven 

@@ -51,7 +51,6 @@ The text recognition module is the core component of an OCR (Optical Character R
 
 <td>The ultra-lightweight English text recognition model released by PaddleOCR in May 2023. It is small in size and fast in speed, and can achieve millisecond-level prediction on CPU. Compared with the PP-OCRv3 English model, the recognition accuracy is improved by 6%, and it is suitable for text recognition tasks in various scenarios.</td>
 
 </tr>
 
 </table>
 
-<b>Note: The evaluation set for the above accuracy indicators is the Chinese dataset built by PaddleOCR, covering multiple scenarios such as street view, web images, documents, and handwriting, with 11,000 images included in text recognition. All models' GPU inference time is based on NVIDIA Tesla T4 machine, with precision type of FP32. CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type of FP32.</b>
 
 
 > ❗ The above list features the <b>4 core models</b> that the text recognition module primarily supports. In total, this module supports <b>18 models</b>. The complete list of models is as follows:
 
 
@@ -100,7 +99,7 @@ The text recognition module is the core component of an OCR (Optical Character R
 
 <td>An ultra-lightweight OCR model suitable for mobile applications. It adopts an encoder-decoder structure based on Transformer and enhances recognition accuracy and efficiency through techniques such as data augmentation and mixed precision training. The model size is 10.6M, making it suitable for deployment on resource-constrained devices. It can be used in scenarios such as mobile photo translation and business card recognition.</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The evaluation set for the above accuracy indicators is the Chinese dataset built by PaddleOCR, covering multiple scenarios such as street view, web images, documents, and handwriting. The text recognition includes 11,000 images. The GPU inference time for all models is based on NVIDIA Tesla T4 machines with FP32 precision type. The CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b></p>
 
+
 
 <table>
 
 <tr>
 
 <th>Model</th><th>Model Download Link</th>
 
@@ -121,7 +120,7 @@ SVTRv2 is a server text recognition model developed by the OpenOCR team of Fudan
 
 </td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The evaluation set for the above accuracy indicators is the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge</a> - Task One: OCR End-to-End Recognition Task A list. The GPU inference time for all models is based on NVIDIA Tesla T4 machines with FP32 precision type. The CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b></p>
 
+
 
 <table>
 
 <tr>
 
 <th>Model</th><th>Model Download Link</th>
 
@@ -140,7 +139,6 @@ SVTRv2 is a server text recognition model developed by the OpenOCR team of Fudan
 
 <td rowspan="1">    The RepSVTR text recognition model is a mobile text recognition model based on SVTRv2. It won the first prize in the PaddleOCR Algorithm Model Challenge - Task One: OCR End-to-End Recognition Task. The end-to-end recognition accuracy on the B list is 2.5% higher than that of PP-OCRv4, with the same inference speed.</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The evaluation set for the above accuracy indicators is the <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge</a> - Task One: OCR End-to-End Recognition Task B list. The GPU inference time for all models is based on NVIDIA Tesla T4 machines with FP32 precision type. The CPU inference speed is based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision type.</b></p>
 
 
 * <b>English Recognition Model</b>
 
 <table>
 
@@ -261,6 +259,28 @@ SVTRv2 is a server text recognition model developed by the OpenOCR team of Fudan
 
 <td>Devanagari Script Recognition</td>
 
 </tr>
 
 </table>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:
 
+    - Chinese Recognition Model: A self-built Chinese dataset using PaddleOCR, covering various scenarios such as street scenes, web images, documents, and handwriting, with 11,000 images for text recognition.
 
+    - ch_SVTRv2_rec: <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge - Track 1: OCR End-to-End Recognition Task</a> A-Rank Evaluation Set.
 
+    - ch_RepSVTR_rec: <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR Algorithm Model Challenge - Track 1: OCR End-to-End Recognition Task</a> B-Rank Evaluation Set.
 
+    - English Recognition Model: A self-built English dataset using PaddleX.
 
+    - Multilingual Recognition Model: A self-built multilingual dataset using PaddleX.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
 </details>
 
 
 ## III. Quick Integration

+ 25 - 6
docs/module_usage/tutorials/ocr_modules/text_recognition.md
Bestand weergeven 

@@ -53,7 +53,6 @@ en_PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model
 
 <td>基于PP-OCRv4识别模型训练得到的超轻量英文识别模型，支持英文、数字识别</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标的评估集是 PaddleOCR 自建的中文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 1.1w 张图片。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
 
 > ❗ 以上列出的是文本识别模块重点支持的<b>4个核心模型</b>，该模块总共支持<b>18个全量模型</b>，包含多个多语言文本识别模型，完整的模型列表如下：
 
 
@@ -104,7 +103,7 @@ PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ec
 
 <td>PP-OCRv3的轻量级识别模型，推理效率高，可以部署在包含端侧设备的多种硬件设备中</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标的评估集是 PaddleOCR 自建的中文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 8367 张图片。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+
 
 <table>
 
 <tr>
 
 <th>模型</th><th>模型下载链接</th>
 
@@ -125,7 +124,7 @@ SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队
 
 </td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标的评估集是 <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>A榜。 所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+
 
 <table>
 
 <tr>
 
 <th>模型</th><th>模型下载链接</th>
 
@@ -144,7 +143,6 @@ SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队
 
 <td rowspan="1">    RepSVTR 文本识别模型是一种基于SVTRv2 的移动端文本识别模型，其在PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务中荣获一等奖，B榜端到端识别精度相比PP-OCRv4提升2.5%，推理速度持平。</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标的评估集是 <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>B榜。 所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
 
 * <b>英文识别模型</b>
 
 <table>
 
@@ -175,7 +173,7 @@ en_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model
 
 <td>基于PP-OCRv3识别模型训练得到的超轻量英文识别模型，支持英文、数字识别</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标的评估集是 PaddleX 自建的英文数据集。 所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+
 
 
 * <b>多语言识别模型</b>
 
 <table>
 
@@ -278,7 +276,28 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
 
 <td>基于PP-OCRv3识别模型训练得到的超轻量梵文字母识别模型，支持梵文字母、数字识别</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标的评估集是 PaddleX 自建的多语种数据集。 </b></p>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：
 
+    - 中文识别模型： PaddleOCR 自建的中文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 1.1w 张图片。
 
+    - ch_SVTRv2_rec：<a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>A榜评估集。
 
+    - ch_RepSVTR_rec：<a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>B榜评估集。
 
+    - 英文识别模型：PaddleX 自建的英文数据集。
 
+    - 多语言识别模型：PaddleX 自建的多语种数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
 </details>
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.en.md
Bestand weergeven 

@@ -31,7 +31,22 @@ The text line orientation classification module primarily distinguishes the orie
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are evaluated on a self-built dataset covering multiple scenarios such as documents and licenses, containing 1000 images. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: PaddleX Self-built Dataset, Covering Multiple Scenarios Such as Documents and Certificates, Containing 1000 Images.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration

+ 16 - 1
docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.md
Bestand weergeven 

@@ -33,7 +33,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标的评估集是自建的数据集，覆盖证件和文档等多个场景，包含 1000 张图片。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：PaddleX 自建的数据集，覆盖证件和文档等多个场景，包含 1000 张图片。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 三、快速集成

+ 15 - 1
docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.en.md
Bestand weergeven 

@@ -56,7 +56,21 @@ Time series anomaly detection focuses on identifying abnormal points or periods
 
 </tbody>
 
 </table>
 
 
-<b>Note: The above precision metrics are measured on the</b>PSM<b>dataset with a time-series input length of 100.</b>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: </b>PSM<b>dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For details, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.md
Bestand weergeven 

@@ -57,7 +57,22 @@ comments: true
 
 </tbody>
 
 </table>
 
 
-<b>注：以上精度指标测量自</b>PSM<b>数据集，时序输入长度为100。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：</b>PSM<b>数据集，时序输入长度为100。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/time_series_modules/time_series_classification.en.md
Bestand weergeven 

@@ -27,7 +27,22 @@ Time series classification involves identifying and categorizing different patte
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The evaluation set for the above accuracy metrics is UWaveGestureLibrary.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: UWaveGestureLibrary.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 16 - 1
docs/module_usage/tutorials/time_series_modules/time_series_classification.md
Bestand weergeven 

@@ -28,7 +28,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标的评估集是 UWaveGestureLibrary。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：UWaveGestureLibrary评估集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 15 - 1
docs/module_usage/tutorials/time_series_modules/time_series_forecasting.en.md
Bestand weergeven 

@@ -57,8 +57,22 @@ Time series forecasting aims to predict the possible values or states at a futur
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are measured on the [ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar) test dataset, with an input sequence length of 96, and a prediction sequence length of 96 for all models except TiDE, which has a prediction sequence length of 720.</b>
 
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: [ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar) test dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## III. Quick Integration
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

+ 15 - 1
docs/module_usage/tutorials/time_series_modules/time_series_forecasting.md
Bestand weergeven 

@@ -72,7 +72,21 @@ comments: true
 
 </tbody>
 
 </table>
 
 
-<b>注意：上述准确性指标是在</b>[ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar)<b>测试数据集上测量的，所有模型的输入序列长度为96，预测序列长度也为96，除了TiDE模型，其预测序列长度为720。</b>
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：[ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar)<b>测试数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 三、快速集成

+ 16 - 1
docs/module_usage/tutorials/video_modules/video_classification.en.md
Bestand weergeven 

@@ -40,7 +40,22 @@ PP-TSM is a video classification model developed by Baidu PaddlePaddle's Vision
 
 
 </table>
 
 
-<p><b>Note: The above accuracy metrics refer to Top-1 Accuracy on the <a href="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/dataset/k400.md">K400</a> validation set. </b></p></details>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/dataset/k400.md">K400</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+</details>
 
 
 ## III. Quick Integration

+ 15 - 1
docs/module_usage/tutorials/video_modules/video_classification.md
Bestand weergeven 

@@ -40,9 +40,23 @@ PP-TSM是一种百度飞桨视觉团队自研的视频分类模型。该模型
 
 
 </table>
 
 
+**测试环境说明：**
 
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/dataset/k400.md">K400</a> 验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
 
-<p><b>注：以上精度指标为 <a href="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/dataset/k400.md">K400</a> 验证集 Top1 Acc。</b></p></details>
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 
 ## 三、快速集成

+ 15 - 1
docs/module_usage/tutorials/video_modules/video_detection.en.md
Bestand weergeven 

@@ -30,7 +30,21 @@ YOWO is a single-stage network with two branches. One branch extracts spatial fe
 
 
 </table>
 
 
-<p><b>Note: The above accuracy metrics refer to Frame-mAP (@ IoU 0.5) Accuracy on the  <a href="http://www.thumos.info/download.html">UCF101-24</a> test set. </b><b>All model GPU inference times are based on NVIDIA Tesla T4 machines, with precision type FP32. CPU inference speeds are based on Intel® Xeon® Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.</b></p></details>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="http://www.thumos.info/download.html">UCF101-24</a> test set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## <span id="lable">III. Quick Integration</span>
 
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).

+ 16 - 1
docs/module_usage/tutorials/video_modules/video_detection.md
Bestand weergeven 

@@ -29,8 +29,23 @@ YOWO是具有两个分支的单阶段网络。一个分支通过2D-CNN提取关
 
 
 </table>
 
 
+**测试环境说明：**
 
 
-<p><b>注：以上精度指标为 <a href="http://www.thumos.info/download.html">UCF101-24</a> test数据集上的测试指标Frame-mAP (@ IoU 0.5)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="http://www.thumos.info/download.html">UCF101-24</a> test数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 三、快速集成
 
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)。

+ 18 - 2
docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.en.md
Bestand weergeven 

@@ -32,7 +32,23 @@ BEVFusion is a multi-modal 3D object detection model that fuses surround camera
 
 <tr>
 
 </table>
 
 
-<p>Note: The above accuracy metrics are for the <a href="https://www.nuscenes.org/nuscenes">nuscenes</a> validation set with mAP(0.5:0.95) and NDS 60.9, with an accuracy type of FP32.</p></details>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://www.nuscenes.org/nuscenes">nuscenes</a> validation set
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## 2. Quick Start
 
 
@@ -142,7 +158,7 @@ for res in output:
 
     res.visualize(save_path="./output/", show=True) ## 3D result visualization. If the runtime environment has a graphical interface, set `show=True`; otherwise, set it to `False`.
 
 ```
 
 
-<b>Note: </b>  
+<b>Note: </b>
 
 1、To visualize 3D detection results, you need to install the open3d package first. The installation command is as follows:
 
 ```bash
 
 pip install open3d

+ 16 - 2
docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.md
Bestand weergeven 

@@ -32,7 +32,21 @@ BEVFusion 是一种多模态 3D 目标检测模型，通过将环视摄像头图
 
 <tr>
 
 </table>
 
 
-<p>注：以上精度指标为<a href="https://www.nuscenes.org/nuscenes">nuscenes</a>验证集 mAP(0.5:0.95), NDS 60.9, 精度类型为 FP32。</p></details>
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://www.nuscenes.org/nuscenes">nuscenes</a>验证集
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始
 
 
@@ -134,7 +148,7 @@ for res in output:
 
     res.visualize(save_path="./output/", show=True) ## 3d结果可视化，如果运行环境有图形界面设置show=True，否则设置为False
 
 ```
 
 
-<b>注：</b>   
+<b>注：</b>
 
 1、3d检测结果可视化需要先安装open3d包，安装命令如下：
 
 ```bash
 
 pip install open3d

+ 19 - 2
docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.en.md
Bestand weergeven 

@@ -60,7 +60,7 @@ The face recognition pipeline is an end-to-end system dedicated to solving face
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p>Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640x640. All GPU inference times are based on an NVIDIA V100 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz and FP32 precision.</p>
 
+
 
 <p><b>Face Recognition Module</b>:</p>
 
 <table>
 
 <thead>
 
@@ -95,7 +95,24 @@ The face recognition pipeline is an end-to-end system dedicated to solving face
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p>Note: The above accuracy metrics are Accuracy scores measured on the AgeDB-30, CFP-FP, and LFW datasets, respectively. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</p>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:
 
+    - Face Detection Model: Evaluated on the WIDER-FACE validation set in COCO format with an input size of 640*640.
 
+    - Face Feature Model: Evaluated on the AgeDB-30, CFP-FP, and LFW datasets, respectively.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start
 
 The pre-trained model pipelines provided by PaddleX can be quickly experienced. You can experience the effects of the face recognition pipeline online or locally using command-line or Python.

+ 19 - 3
docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md
Bestand weergeven 

@@ -60,8 +60,7 @@ comments: true
 
 </tr>
 
 </tr></tbody>
 
 </table>
 
-<p>注：以上精度指标是在 COCO 格式的 WIDER-FACE 验证集上，以640
 
-*640作为输入尺寸评估得到的。所有模型 GPU 推理耗时基于 NVIDIA V100 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz，精度类型为 FP32。</p>
 
+
 
 <p><b>人脸特征模块：</b></p>
 
 <table>
 
 <thead>
 
@@ -96,7 +95,24 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p>注：以上精度指标是分别在 AgeDB-30、CFP-FP 和 LFW 数据集上测得的 Accuracy。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</p>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**
 
+    - 人脸检测模型：COCO 格式的 WIDER-FACE 验证集上，以640*640作为输入尺寸评估得到的。
 
+    - 人脸特征模型：分别在 AgeDB-30、CFP-FP 和 LFW 数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始
 
 PaddleX 所提供的模型产线均可以快速体验效果，你可以在线体验人脸识别产线的效果，也可以在本地使用命令行或 Python 体验人脸识别产线的效果。

+ 17 - 2
docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.en.md
Bestand weergeven 

@@ -36,7 +36,6 @@ PP-ShiTuV2 is a practical general image recognition system mainly composed of th
 
 </tr>
 
 </table>
 
 
-Note: The above accuracy metrics are based on the private mainbody detection dataset.
 
 
 <b>Image Feature Module:</b>
 
 <table>
 
@@ -72,7 +71,23 @@ Note: The above accuracy metrics are based on the private mainbody detection dat
 
 </tr>
 
 </table>
 
 
-Note: The above accuracy metrics are based on AliProducts Recall@1. All GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision. CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:
 
+    - Subject Detection Model: PaddleClas Subject Detection Dataset.
 
+    - Image Feature Model: AliProducts Dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start

+ 17 - 3
docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.md
Bestand weergeven 

@@ -36,8 +36,6 @@ PP-ShiTuV2 是一个实用的通用图像识别系统，主要由主体检测、
 
 </tr>
 
 </table>
 
 
-注：以上精度指标为 PaddleClas 主体检测数据集。
 
-
 
 <b>图像特征模块：</b>
 
 <table>
 
 <tr>
 
@@ -72,7 +70,23 @@ PP-ShiTuV2 是一个实用的通用图像识别系统，主要由主体检测、
 
 </tr>
 
 </table>
 
 
-注：以上精度指标为 AliProducts recall@1。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：
 
+    - 主体检测模型：PaddleClas 主体检测数据集。
 
+    - 图像特征模型：AliProducts数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始

+ 20 - 2
docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.en.md
Bestand weergeven 

@@ -42,7 +42,7 @@ PaddleX's Human Keypoint Detection Pipeline is a Top-Down solution consisting of
 
 <td>28.79</td>
 
 </tr>
 
 </table>
 
-<b>Note: The above accuracy metrics are based on the CrowdHuman dataset mAP(0.5:0.95). All model GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
 <b>Human Keypoint Detection Module:</b>
 
 <table>
 
 <tr>
 
@@ -75,7 +75,25 @@ PaddleX's Human Keypoint Detection Pipeline is a Top-Down solution consisting of
 
 <td>4.9</td>
 
 </tr>
 
 </table>
 
-<b>Note: The above accuracy metrics are based on the COCO dataset AP(0.5:0.95), with detection boxes obtained from ground truth annotations. All model GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:
 
+    - Pedestrian Detection Model: CrowdHuman Dataset.
 
+    - Human Keypoint Detection Model: COCO Dataset AP(0.5:0.95), with detection boxes obtained from ground truth annotations.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
 </details>
 
 
 ## 2. Quick Start

+ 20 - 2
docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.md
Bestand weergeven 

@@ -39,7 +39,7 @@ PaddleX 的人体关键点检测产线是一个 Top-Down 方案，由行人检
 
 <td>28.79</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为CrowdHuman数据集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
 <b>人体关键点检测模块：</b>
 
 <table>
 
 <tr>
 
@@ -72,7 +72,25 @@ PaddleX 的人体关键点检测产线是一个 Top-Down 方案，由行人检
 
 <td>4.9</td>
 
 </tr>
 
 </table>
 
-<b>注：以上精度指标为COCO数据集 AP(0.5:0.95)，所依赖的检测框为ground truth标注得到。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：
 
+    - 行人检测模型：CrowdHuman数据集。
 
+    - 人体关键点检测模型：COCO数据集 AP(0.5:0.95)，所依赖的检测框为ground truth标注得到。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
 </details>
 
 
 ## 2. 快速开始

+ 16 - 1
docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.en.md
Bestand weergeven 

@@ -28,7 +28,22 @@ This pipeline integrates the high-precision anomaly detection model STFPM, which
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>Note: The above accuracy metrics are the average anomaly scores on the </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> validation set. All model GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: The above accuracy metrics are the average anomaly scores on the </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start
 
 PaddleX provides pre-trained models for the anomaly detection pipeline, allowing for quick experience of its effects. You can use the command line or Python to experience the image anomaly detection pipeline locally.

+ 16 - 1
docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.md
Bestand weergeven 

@@ -29,7 +29,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<b>注：以上精度指标为 </b><a href="https://www.mvtec.com/company/research/datasets/mvtec-ad">MVTec AD</a><b> 验证集 grid 数据的mIoU结果。以上所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://www.mvtec.com/company/research/datasets/mvtec-ad">MVTec AD</a><b> 验证集 grid 数据。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始
 
 PaddleX 所提供的模型产线均可以快速体验效果，您可以在本地使用命令行或 Python 体验图像异常检测产线的效果。

+ 18 - 1
docs/pipeline_usage/tutorials/cv_pipelines/image_classification.en.md
Bestand weergeven 

@@ -684,7 +684,24 @@ Image classification is a technique that assigns images to predefined categories
 
 <td>100.1 M</td>
 
 </tr>
 
 </tr></tr></tr></tr></table>
 
-<p><b>Note: The above accuracy metrics refer to Top-1 Accuracy on the <a href="https://www.image-net.org/index.php">ImageNet-1k</a> validation set. </b><b>All model GPU inference times are based on NVIDIA Tesla T4 machines, with precision type FP32. CPU inference speeds are based on Intel® Xeon® Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:  <a href="https://www.image-net.org/index.php">ImageNet-1k</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## 2. Quick Start
 
 All model pipelines provided by PaddleX can be quickly experienced. You can experience the general image classification pipeline online in the Star River Community, or you can use the command line or Python locally to experience the effects of the general image classification pipeline.

+ 18 - 1
docs/pipeline_usage/tutorials/cv_pipelines/image_classification.md
Bestand weergeven 

@@ -680,7 +680,24 @@ comments: true
 
 <td>100.1 M</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为 <a href="https://www.image-net.org/index.php">ImageNet-1k</a> 验证集 Top1 Acc。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://www.image-net.org/index.php">ImageNet-1k</a> 验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 2. 快速开始
 
 PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河社区线体验通用图像分类产线的效果，也可以在本地使用命令行或 Python 体验通用图像分类产线的效果。

+ 16 - 1
docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.en.md
Bestand weergeven 

@@ -52,7 +52,22 @@ Image multi-label classification is a technique that assigns multiple relevant c
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>Note: The above accuracy metrics are mAP for the multi-label classification task on </b><a href="https://cocodataset.org/#home">COCO2017</a><b>. The GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: multi-label classification task on </b><a href="https://cocodataset.org/#home">COCO2017</a><b>.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start
 
 All model pipelines provided by PaddleX can be quickly experienced. You can experience the effect of the image multi-label classification pipeline on the community platform, or you can use the command line or Python locally to experience the effect of the image multi-label classification pipeline.

+ 16 - 1
docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.md
Bestand weergeven 

@@ -52,7 +52,22 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>注：以上精度指标为 </b><a href="https://cocodataset.org/#home">COCO2017</a><b> 的多标签分类任务mAP。以上所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://cocodataset.org/#home">COCO2017</a><b> 的多标签分类任务
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 
 ## 2. 快速开始

+ 18 - 1
docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.en.md
Bestand weergeven 

@@ -159,7 +159,24 @@ Instance segmentation is a computer vision task that not only identifies the obj
 
 <td> SOLOv2 is a real-time instance segmentation algorithm that segments objects by location. This model is an improved version of SOLO, achieving a good balance between accuracy and speed through the introduction of mask learning and mask NMS.</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The above accuracy metrics are based on the Mask AP of the <a href="https://cocodataset.org/#home">COCO2017</a> validation set. All GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://cocodataset.org/#home">COCO2017</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## 2. Quick Start
 
 The pre-trained model pipelines provided by PaddleX allow for quick experience of the effects. You can experience the effects of the General Instance Segmentation Pipeline online or locally using command line or Python.

+ 18 - 2
docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.md
Bestand weergeven 

@@ -37,7 +37,6 @@ comments: true
 
 <td>113.6 M</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为<a href="https://cocodataset.org/#home">COCO2017</a>验证集 Mask AP。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
 
 > ❗ 以上列出的是实例分割模块重点支持的<b>2个核心模型</b>，该模块总共支持<b>15个模型</b>，完整的模型列表如下：
 
 
@@ -163,7 +162,24 @@ comments: true
 
 <td> SOLOv2 是一种按位置分割物体的实时实例分割算法。该模型是SOLO的改进版本，通过引入掩码学习和掩码NMS，实现了精度和速度上取得良好平衡。</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为<a href="https://cocodataset.org/#home">COCO2017</a>验证集 Mask AP。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://cocodataset.org/#home">COCO2017</a>验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 2. 快速开始
 
 PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河社区线体验通用 实例分割 产线的效果，也可以在本地使用命令行或 Python 体验通用 实例分割 产线的效果。

+ 18 - 1
docs/pipeline_usage/tutorials/cv_pipelines/object_detection.en.md
Bestand weergeven 

@@ -346,7 +346,24 @@ Object detection aims to identify the categories and locations of multiple objec
 
 <td>351.5 M</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The precision metrics mentioned are based on the <a href="https://cocodataset.org/#home">COCO2017</a> validation set mAP(0.5:0.95). All model GPU inference times are measured on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://cocodataset.org/#home">COCO2017</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## 2. Quick Start
 
 PaddleX's pre-trained model pipelines allow for quick experience of their effects. You can experience the effects of the General Object Detection Pipeline online or locally using command line or Python.

+ 18 - 1
docs/pipeline_usage/tutorials/cv_pipelines/object_detection.md
Bestand weergeven 

@@ -363,7 +363,24 @@ comments: true
 
 <td>187 M</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为<a href="https://cocodataset.org/#home">COCO2017</a>验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p></details>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://cocodataset.org/#home">COCO2017</a>验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
+
 
+</details>
 
 
 ## 2. 快速开始
 
 PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可以在线体验通用目标检测产线的效果，也可以在本地使用命令行或 Python 体验通用目标检测产线的效果。

+ 18 - 6
docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.en.md
Bestand weergeven 

@@ -35,8 +35,21 @@ Open vocabulary object detection is an advanced object detection technology that
 
 <td rowspan="3">An open vocabulary object detection model trained on O365, GoldG, and Cap4M datasets. The text encoder uses Bert, and the visual model part adopts DINO overall, with additional cross-modal fusion modules designed, achieving good results in the field of open vocabulary object detection.</td>
 
 </tr>
 
 </table>
 
+**Test Environment Description**:
 
 
-<b>Note: The above accuracy metrics are based on the COCO val2017 validation set mAP(0.5:0.95). All model GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: COCO val2017 validation set
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start
 
 
@@ -69,7 +82,7 @@ The explanation of the running result parameters can refer to the result explana
 
 
 The visualization results are saved under `save_path`, and the visualization results of open vocabulary detection are as follows:
 
 
-<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/open_vocabulary_detection/open_vocabulary_detection_res.jpg"> 
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/open_vocabulary_detection/open_vocabulary_detection_res.jpg">
 
 
 #### 2.1.2 Python Script Integration
 
 * The above command line is for quickly experiencing the effect. Generally, in a project, it is often necessary to integrate through code. You can complete the rapid inference of the pipeline with just a few lines of code. The inference code is as follows:
 
@@ -553,9 +566,9 @@ print(result["detectedObjects"])
 
 <details><summary>C++</summary>
 
 
 <pre><code class="language-cpp">#include &lt;iostream&gt;
 
-#include &quot;cpp-httplib/httplib.h&quot; // <url id="cu9q6pqi5970ak6ek5e0" type="url" status="parsed" title="GitHub - Huiyicc/cpp-httplib: A C++ header-only HTTP/HTTPS server and client library" wc="15064">https://github.com/Huiyicc/cpp-httplib</url> 
-#include &quot;nlohmann/json.hpp&quot; // <url id="cu9q6pqi5970ak6ek5eg" type="url" status="parsed" title="GitHub - nlohmann/json: JSON for Modern C++" wc="80311">https://github.com/nlohmann/json</url> 
-#include &quot;base64.hpp&quot; // <url id="cu9q6pqi5970ak6ek5f0" type="url" status="parsed" title="GitHub - tobiaslocker/base64: A modern C++ base64 encoder / decoder" wc="2293">https://github.com/tobiaslocker/base64</url> 
+#include &quot;cpp-httplib/httplib.h&quot; // <url id="cu9q6pqi5970ak6ek5e0" type="url" status="parsed" title="GitHub - Huiyicc/cpp-httplib: A C++ header-only HTTP/HTTPS server and client library" wc="15064">https://github.com/Huiyicc/cpp-httplib</url>
 
+#include &quot;nlohmann/json.hpp&quot; // <url id="cu9q6pqi5970ak6ek5eg" type="url" status="parsed" title="GitHub - nlohmann/json: JSON for Modern C++" wc="80311">https://github.com/nlohmann/json</url>
 
+#include &quot;base64.hpp&quot; // <url id="cu9q6pqi5970ak6ek5f0" type="url" status="parsed" title="GitHub - tobiaslocker/base64: A modern C++ base64 encoder / decoder" wc="2293">https://github.com/tobiaslocker/base64</url>
 
 
 int main() {
 
     httplib::Client client(&quot;localhost:8080&quot;);
 
@@ -889,4 +902,3 @@ The current pipeline temporarily does not support fine-tuning training, only inf
 
 
 ## 5. Multi-Hardware Support
 
 The current pipeline temporarily only supports GPU and CPU inference. Adaptation to more hardware for this pipeline is planned to be supported in the future.
 
-

+ 15 - 1
docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.md
Bestand weergeven 

@@ -36,7 +36,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<b>注：以上精度指标为 COCO val2017 验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32</b>。
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：COCO val2017 验证集
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始

+ 15 - 3
docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.en.md
Bestand weergeven 

@@ -38,7 +38,20 @@ Open vocabulary segmentation is an image segmentation task that aims to segment
 
 </tr>
 
 </table>
 
 
-<b>Note: All model GPU inference times are based on NVIDIA Tesla T4 machines with FP32 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start
 
 
@@ -547,7 +560,6 @@ print("\nresult(with rle encoded binary mask):")
 
 print(result)
 
 </code></pre></details>
 
 
-
 
 </details>
 
 <br/>
 
 
@@ -558,4 +570,4 @@ You can choose the appropriate method to deploy the model pipeline according to
 
 The current pipeline temporarily does not support fine-tuning training, only inference integration is supported. Fine-tuning training for this pipeline is planned to be supported in the future.
 
 
 ## 5. Multi-Hardware Support
 
-The current pipeline temporarily only supports GPU and CPU inference. Adaptation to more hardware for this pipeline is planned to be supported in the future.
 
+The current pipeline temporarily only supports GPU and CPU inference. Adaptation to more hardware for this pipeline is planned to be supported in the future.

+ 14 - 1
docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.md
Bestand weergeven 

@@ -38,7 +38,20 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<b>注：所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32</b>。
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始

+ 18 - 2
docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.en.md
Bestand weergeven 

@@ -40,7 +40,6 @@ Pedestrian attribute recognition is a key function in computer vision systems, u
 
 <td>28.79</td>
 
 </tr>
 
 </table>
 
-<p><b>Note: The above accuracy metrics are mAP(0.5:0.95) on the CrowdHuman dataset. All model GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
 
 <p><b>Pedestrian Attribute Recognition Module</b>:</p>
 
 <table>
 
 <thead>
 
@@ -64,7 +63,24 @@ Pedestrian attribute recognition is a key function in computer vision systems, u
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>Note: The above accuracy metrics are mA on PaddleX's internally built dataset. GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**:
 
+    - Pedestrian Detection Model: CrowdHuman Dataset.
 
+    - Pedestrian Attribute Recognition Model: PaddleX Internal Self-built Dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start

+ 19 - 2
docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.md
Bestand weergeven 

@@ -40,7 +40,7 @@ comments: true
 
 <td>28.79</td>
 
 </tr>
 
 </table>
 
-<p><b>注：以上精度指标为CrowdHuman数据集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+
 
 <p><b>行人属性识别模块：</b></p>
 
 <table>
 
 <thead>
 
@@ -64,7 +64,24 @@ comments: true
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>注：以上精度指标为 PaddleX 内部自建数据集 mA。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b></p>
 
+
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**
 
+    - 行人检测模型：CrowdHuman数据集。
 
+    - 行人属性是别模型：PaddleX 内部自建数据集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始
 
 PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河社区线体验行人属性识别产线的效果，也可以在本地使用命令行或 Python 体验行人属性识别产线的效果。

+ 15 - 1
docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.en.md
Bestand weergeven 

@@ -34,7 +34,21 @@ Rotated object detection is a variant of the object detection module, specifical
 
 </tr>
 
 </table>
 
 
-<p><b>Note: The above accuracy metrics are based on the <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set mAP(0.5:0.95). All model GPU inference times are based on NVIDIA TRX2080 Ti machines with F16 precision, and CPU inference speeds are based on Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p>
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 
 ## 2. Quick Start

+ 15 - 1
docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.md
Bestand weergeven 

@@ -34,7 +34,21 @@ comments: true
 
 </tr>
 
 </table>
 
 
-<p><b>注：以上精度指标为<a href="https://captain-whu.github.io/DOTA/">DOTA</a>验证集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA TRX2080 Ti 机器，精度类型为 F16， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b></p>
 
+**测试环境说明：**
 
+
 
+- **性能测试环境**
 
+  - **测试数据集**：<a href="https://captain-whu.github.io/DOTA/">DOTA</a>验证集。
 
+  - **硬件配置**：
 
+    - GPU：NVIDIA Tesla T4
 
+    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
 
+    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **推理模式说明**
 
+
 
+| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
 
+|-------------|----------------------------------|------------------|---------------------------------------------|
 
+| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
 
+| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 
 ## 2. 快速开始

+ 18 - 1
docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md
Bestand weergeven 

@@ -192,7 +192,24 @@ Semantic segmentation is a computer vision technique that aims to assign each pi
 
 </tr>
 
 </tbody>
 
 </table>
 
-<p><b>The accuracy metrics of the SeaFormer series models are measured on the <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
+
 
+**Test Environment Description**:
 
+
 
+- **Performance Test Environment**
 
+  - **Test Dataset**: <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset and <a href="https://www.cityscapes-dataset.com/">Cityscapes</a>dataset.
 
+  - **Hardware Configuration**:
 
+    - GPU: NVIDIA Tesla T4
 
+    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
 
+    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
+
 
+- **Inference Mode Description**
 
+
 
+| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
 
+|-------------|----------------------------------------|-------------------|---------------------------------------------------|
 
+| Regular Mode| FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
 
+| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
+
 
+</details>
 
 
 ## 2. Quick Start
 
 PaddleX's pre-trained model pipelines can be quickly experienced. You can experience the effects of the General Semantic Segmentation Pipeline online or locally using command line or Python.

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