cherry-pick layout (#2315)

* add layout

* add layout

Author: Sunting78

docs/module_usage/tutorials/ocr_modules/layout_detection.md

@@ -11,7 +11,10 @@
 |模型|mAP(0.5)（%）|GPU推理耗时（ms）|CPU推理耗时 (ms)|模型存储大小（M）|介绍|
 |-|-|-|-|-|-|
 |PicoDet_layout_1x|86.8|13.0|91.3|7.4|基于PicoDet-1x在PubLayNet数据集训练的高效率版面区域定位模型，可定位包含文字、标题、表格、图片以及列表这5类区域|
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
 |PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
 |RT-DETR-H_layout_3cls|95.9|114.6|3832.6|470.1|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，包含3个类别：表格，图像和印章|
 |RT-DETR-H_layout_17cls|92.6|115.1|3827.2|470.2|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，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|
 

docs/module_usage/tutorials/ocr_modules/layout_detection_en.md

@@ -11,7 +11,10 @@ The core task of structure analysis is to parse and segment the content of input
 | Model | mAP(0.5) (%) | GPU Inference Time (ms) | CPU Inference Time (ms) | Model Size (M) | Description |
 |-|-|-|-|-|-|
 | PicoDet_layout_1x | 86.8 | 13.0 | 91.3 | 7.4 | An efficient layout area localization model trained on the PubLayNet dataset based on PicoDet-1x can locate five types of areas, including text, titles, tables, images, and lists. |
-| PicoDet-L_layout_3cls | 89.3 | 15.7 | 159.8 | 22.6 | An efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|An high-efficient layout area localization model trained on a self-constructed dataset based on PicoDet-S for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|A high-efficient layout area localization model trained on a self-constructed dataset based on PicoDet-S_layout_17cls for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
+|PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|An efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|A efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L_layout_17cls for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
 | RT-DETR-H_layout_3cls | 95.9 | 114.6 | 3832.6 | 470.1 | A high-precision layout area localization model trained on a self-constructed dataset based on RT-DETR-H for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
 | RT-DETR-H_layout_17cls | 92.6 | 115.1 | 3827.2 | 470.2 | A high-precision layout area localization model trained on a self-constructed dataset based on RT-DETR-H for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
 

docs/pipeline_deploy/high_performance_inference.md

@@ -192,7 +192,8 @@ PaddleX 为每个模型提供默认的高性能推理配置，并将其存储在
   <tr>
     <td rowspan="3">印章文本识别</td>
     <td>版面区域分析</td>
-    <td>PicoDet-L_layout_3cls<br/>RT-DETR-H_layout_3cls<br/>RT-DETR-H_layout_17cls</td>
+    <td>PicoDet-S_layout_3cls<br/>PicoDet-S_layout_17cls<details>
+    <summary><b>more</b></summary>PicoDet-L_layout_3cls<br/>PicoDet-L_layout_17cls<br/>RT-DETR-H_layout_3cls<br/>RT-DETR-H_layout_17cls</details></td>
   </tr>
 
   <tr>

docs/pipeline_deploy/high_performance_inference_en.md

@@ -183,7 +183,8 @@ PaddleX provides default high-performance inference configurations for each mode
   <tr>
     <td rowspan="3">Seal Text Recognition</td>
     <td>Layout Analysis</td>
-    <td>PicoDet-L_layout_3cls<br/>RT-DETR-H_layout_3cls<br/>RT-DETR-H_layout_17cls</td>
+    <td>PicoDet-S_layout_3cls<br/>PicoDet-S_layout_17cls<details>
+    <summary><b>more</b></summary>PicoDet-L_layout_3cls<br/>PicoDet-L_layout_17cls<br/>RT-DETR-H_layout_3cls<br/>RT-DETR-H_layout_17cls</details></td>
   </tr>
 
   <tr>

docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.md

@@ -51,7 +51,10 @@
 |模型|mAP(0.5)（%）|GPU推理耗时（ms）|CPU推理耗时 (ms)|模型存储大小（M）|介绍|
 |-|-|-|-|-|-|
 |PicoDet_layout_1x|86.8|13.0|91.3|7.4|基于PicoDet-1x在PubLayNet数据集训练的高效率版面区域定位模型，可定位包含文字、标题、表格、图片以及列表这5类区域|
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
 |PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
 |RT-DETR-H_layout_3cls|95.9|114.6|3832.6|470.1|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，包含3个类别：表格，图像和印章|
 |RT-DETR-H_layout_17cls|92.6|115.1|3827.2|470.2|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，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|
 

docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_en.md

@@ -50,7 +50,10 @@ The **PP-ChatOCRv3-doc** pipeline includes modules for **Table Structure Recogni
 | Model | mAP(0.5) (%) | GPU Inference Time (ms) | CPU Inference Time (ms) | Model Size (M) | Description |
 |-|-|-|-|-|-|
 | PicoDet_layout_1x | 86.8 | 13.0 | 91.3 | 7.4 | An efficient layout area localization model trained on the PubLayNet dataset based on PicoDet-1x can locate five types of areas, including text, titles, tables, images, and lists. |
-| PicoDet-L_layout_3cls | 89.3 | 15.7 | 159.8 | 22.6 | An efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|An high-efficient layout area localization model trained on a self-constructed dataset based on PicoDet-S for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|A high-efficient layout area localization model trained on a self-constructed dataset based on PicoDet-S_layout_17cls for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
+|PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|An efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|A efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L_layout_17cls for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
 | RT-DETR-H_layout_3cls | 95.9 | 114.6 | 3832.6 | 470.1 | A high-precision layout area localization model trained on a self-constructed dataset based on RT-DETR-H for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
 | RT-DETR-H_layout_17cls | 92.6 | 115.1 | 3827.2 | 470.2 | A high-precision layout area localization model trained on a self-constructed dataset based on RT-DETR-H for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
 

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md

@@ -49,6 +49,11 @@
 |模型|mAP(0.5)（%）|GPU推理耗时（ms）|CPU推理耗时 (ms)|模型存储大小（M）|介绍|
 |-|-|-|-|-|-|
 |PicoDet_layout_1x|86.8|13.0|91.3|7.4|基于PicoDet-1x在PubLayNet数据集训练的高效率版面区域定位模型，可定位包含文字、标题、表格、图片以及列表这5类区域|
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
+|PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
+|RT-DETR-H_layout_3cls|95.9|114.6|3832.6|470.1|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，包含3个类别：表格，图像和印章|
 |RT-DETR-H_layout_17cls|92.6|115.1|3827.2|470.2|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，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|
 
 

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_en.md

@@ -48,6 +48,11 @@ The **General Layout Parsing Pipeline** includes modules for table structure rec
 | Model | mAP(0.5) (%) | GPU Inference Time (ms) | CPU Inference Time (ms) | Model Size (M) | Description |
 |-|-|-|-|-|-|
 | PicoDet_layout_1x | 86.8 | 13.0 | 91.3 | 7.4 | An efficient layout area localization model trained on the PubLayNet dataset based on PicoDet-1x can locate five types of areas, including text, titles, tables, images, and lists. |
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|An high-efficient layout area localization model trained on a self-constructed dataset based on PicoDet-S for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|A high-efficient layout area localization model trained on a self-constructed dataset based on PicoDet-S_layout_17cls for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
+|PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|An efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|A efficient layout area localization model trained on a self-constructed dataset based on PicoDet-L_layout_17cls for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
+| RT-DETR-H_layout_3cls | 95.9 | 114.6 | 3832.6 | 470.1 | A high-precision layout area localization model trained on a self-constructed dataset based on RT-DETR-H for scenarios such as Chinese and English papers, magazines, and research reports includes three categories: tables, images, and seals. |
 | RT-DETR-H_layout_17cls | 92.6 | 115.1 | 3827.2 | 470.2 | A high-precision layout area localization model trained on a self-constructed dataset based on RT-DETR-H for scenarios such as Chinese and English papers, magazines, and research reports includes 17 common layout categories, namely: paragraph titles, images, text, numbers, abstracts, content, chart titles, formulas, tables, table titles, references, document titles, footnotes, headers, algorithms, footers, and seals. |
 
 **Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built layout region analysis dataset, containing 10,000 images of common document types, including English and Chinese papers, magazines, research reports, etc. 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.**

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.md

@@ -22,7 +22,11 @@
 
 |模型|mAP(0.5)（%）|GPU推理耗时（ms）|CPU推理耗时 (ms)|模型存储大小（M）|介绍|
 |-|-|-|-|-|-|
+|PicoDet_layout_1x|86.8|13.0|91.3|7.4|基于PicoDet-1x在PubLayNet数据集训练的高效率版面区域定位模型，可定位包含文字、标题、表格、图片以及列表这5类区域|
+|PicoDet-S_layout_3cls|87.1|13.5 |45.8 |4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-S_layout_17cls|70.3|13.6|46.2|4.8|基于PicoDet-S轻量模型在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
 |PicoDet-L_layout_3cls|89.3|15.7|159.8|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，包含3个类别：表格，图像和印章|
+|PicoDet-L_layout_17cls|79.9|17.2 |160.2|22.6|基于PicoDet-L在中英文论文、杂志和研报等场景上自建数据集训练的高效率版面区域定位模型，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|
 |RT-DETR-H_layout_3cls|95.9|114.6|3832.6|470.1|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，包含3个类别：表格，图像和印章|
 |RT-DETR-H_layout_17cls|92.6|115.1|3827.2|470.2|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，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|