add layout inference time (#3339)

Co-authored-by: cuicheng01 <45199522+cuicheng01@users.noreply.github.com>

docs/module_usage/tutorials/ocr_modules/layout_detection.en.md

@@ -25,24 +25,24 @@ The core task of structure analysis is to parse and segment the content of input
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>A high-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>A high-efficiency layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.</td>
 </tr>

docs/module_usage/tutorials/ocr_modules/layout_detection.md

@@ -25,24 +25,24 @@ comments: true
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / - </td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.en.md

@@ -73,24 +73,24 @@ The formula recognition pipeline is designed to solve formula recognition tasks
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>A high-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>A high-efficiency layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.md

@@ -74,24 +74,24 @@ comments: true
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.en.md

@@ -47,74 +47,167 @@ The <b>General Layout Parsing Pipeline</b> includes modules for table structure
 <th>mAP(0.5) (%)</th>
 <th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
-<th>Model Size (M)</th>
-<th>Description</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Trained Model</a></td>
-<td>86.8</td>
-<td>9.03 / 3.10</td>
-<td>25.82 / 20.70</td>
-<td>7.4</td>
-<td>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.</td>
+<td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
+<td>90.4</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
+<td>123.76 M</td>
+<td>A high-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.</td>
 </tr>
 <tr>
-<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Trained Model</a></td>
-<td>95.7</td>
+<td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
+<td>75.2</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
+<td>22.578</td>
+<td>A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-L.</td>
+</tr>
+<tr>
+<td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
+<td>70.9</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
+<td>4.834</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.</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 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:
+
+<details><summary> 👉 Details of Model List</summary>
+
+* <b>Table Layout Detection Model</b>
+<table>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Training Model</a></td>
+<td>97.5</td>
 <td>8.02 / 3.09</td>
 <td>23.70 / 20.41</td>
 <td>7.4 M</td>
-<td>An efficient layout area localization model trained on the PubLayNet dataset based on PicoDet-1x can locate one type of tables.</td>
+<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>
+<thead>
 <tr>
-<td>PicoDet-S_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">Trained Model</a></td>
-<td>87.1</td>
-<td>8.99 / 2.22</td>
-<td>16.11 / 8.73</td>
-<td>4.8</td>
-<td>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.</td>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
+</thead>
+<tbody>
 <tr>
-<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Trained Model</a></td>
-<td>70.3</td>
-<td>9.11 / 2.12</td>
-<td>15.42 / 9.12</td>
+<td>PicoDet-S_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">Training Model</a></td>
+<td>88.2</td>
+<td>8.99 / 2.22</td>
+<td>16.11 / 8.73</td>
 <td>4.8</td>
-<td>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.</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
 </tr>
 <tr>
-<td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Trained Model</a></td>
-<td>89.3</td>
+<td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Training Model</a></td>
+<td>89.0</td>
 <td>13.05 / 4.50</td>
 <td>41.30 / 41.30</td>
 <td>22.6</td>
-<td>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.</td>
-</tr>
-<tr>
-<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Trained Model</a></td>
-<td>79.9</td>
-<td>13.50 / 4.69</td>
-<td>43.32 / 43.32</td>
-<td>22.6</td>
-<td>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.</td>
+<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
-<td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Trained Model</a></td>
-<td>95.9</td>
+<td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Training Model</a></td>
+<td>95.8</td>
 <td>114.93 / 27.71</td>
 <td>947.56 / 947.56</td>
 <td>470.1</td>
-<td>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.</td>
+<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>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Training Model</a></td>
+<td>97.8</td>
+<td>9.03 / 3.10</td>
+<td>25.82 / 20.70</td>
+<td>7.4</td>
+<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>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Training Model</a></td>
+<td>87.4</td>
+<td>9.11 / 2.12</td>
+<td>15.42 / 9.12</td>
+<td>4.8</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
+</tr>
+<tr>
+<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Training Model</a></td>
+<td>89.0</td>
+<td>13.50 / 4.69</td>
+<td>43.32 / 43.32</td>
+<td>22.6</td>
+<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
-<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Trained Model</a></td>
-<td>92.6</td>
+<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Training Model</a></td>
+<td>98.3</td>
 <td>115.29 / 104.09</td>
 <td>995.27 / 995.27</td>
 <td>470.2</td>
-<td>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.</td>
+<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>

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md

@@ -72,24 +72,24 @@ comments: true
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.en.md

@@ -71,24 +71,24 @@ Layout analysis is a technology that extracts structured information from docume
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>A high-precision layout region detection model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exam papers, and research reports, based on RT-DETR-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>A layout region detection model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exam papers, and research reports, based on PicoDet-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>A high-efficiency layout region detection model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exam papers, and research reports, based on PicoDet-S.</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.md

@@ -70,24 +70,24 @@ comments: true
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.en.md

@@ -24,82 +24,67 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
 <th>Model Storage Size (M)</th>
-<th>Description</th>
+<th>Introduction</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Trained Model</a></td>
-<td>86.8</td>
-<td>9.03 / 3.10</td>
-<td>25.82 / 20.70</td>
-<td>7.4</td>
-<td>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.</td>
-</tr>
-<tr>
-<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Trained Model</a></td>
-<td>95.7</td>
-<td>8.02 / 3.09</td>
-<td>23.70 / 20.41</td>
-<td>7.4 M</td>
-<td>An efficient layout area localization model trained on the PubLayNet dataset based on PicoDet-1x can locate one type of tables.</td>
-</tr>
-<tr>
-<td>PicoDet-S_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">Trained Model</a></td>
-<td>87.1</td>
-<td>8.99 / 2.22</td>
-<td>16.11 / 8.73</td>
-<td>4.8</td>
-<td>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.</td>
-</tr>
-<tr>
-<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Trained Model</a></td>
-<td>70.3</td>
-<td>9.11 / 2.12</td>
-<td>15.42 / 9.12</td>
-<td>4.8</td>
-<td>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.</td>
-</tr>
-<tr>
-<td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Trained Model</a></td>
-<td>89.3</td>
-<td>13.05 / 4.50</td>
-<td>41.30 / 41.30</td>
-<td>22.6</td>
-<td>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.</td>
+<td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
+<td>90.4</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
+<td>123.76 M</td>
+<td>A high-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.</td>
 </tr>
 <tr>
-<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Trained Model</a></td>
-<td>79.9</td>
-<td>13.50 / 4.69</td>
-<td>43.32 / 43.32</td>
-<td>22.6</td>
-<td>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.</td>
+<td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
+<td>75.2</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
+<td>22.578</td>
+<td>A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
-<td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Trained Model</a></td>
-<td>95.9</td>
-<td>114.93 / 27.71</td>
-<td>947.56 / 947.56</td>
-<td>470.1</td>
-<td>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.</td>
-</tr>
-<tr>
-<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Trained Model</a></td>
-<td>92.6</td>
-<td>115.29 / 104.09</td>
-<td>995.27 / 995.27</td>
-<td>470.2</td>
-<td>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.</td>
+<td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
+<td>70.9</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
+<td>4.834</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.</td>
 </tr>
 </tbody>
 </table>
 
-> ❗ The above listed are the <b>3 core models</b> that the layout detection module mainly supports. This module supports a total of <b>11 full models</b>, including multiple models predefined with different categories. Among them, there are 9 models that include the seal category. In addition to the above 3 core models, the remaining model list is as follows:
 
-<details><summary> 👉Model List Details</summary>
+> ❗ 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:
 
-* <b>3-category Layout Detection Models, including table, image, and seal</b>
+<details><summary> 👉 Details of Model List</summary>
+
+* <b>Table Layout Detection Model</b>
+<table>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Training Model</a></td>
+<td>97.5</td>
+<td>8.02 / 3.09</td>
+<td>23.70 / 20.41</td>
+<td>7.4 M</td>
+<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>
 <thead>
 <tr>
@@ -118,7 +103,7 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <td>8.99 / 2.22</td>
 <td>16.11 / 8.73</td>
 <td>4.8</td>
-<td>A high-efficiency layout area localization model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on the lightweight PicoDet-S model</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
 </tr>
 <tr>
 <td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Training Model</a></td>
@@ -126,7 +111,7 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <td>13.05 / 4.50</td>
 <td>41.30 / 41.30</td>
 <td>22.6</td>
-<td>A layout area localization model with balanced efficiency and accuracy, trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on PicoDet-L</td>
+<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
 <td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Training Model</a></td>
@@ -134,12 +119,35 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <td>114.93 / 27.71</td>
 <td>947.56 / 947.56</td>
 <td>470.1</td>
-<td>A high-precision layout area localization model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on RT-DETR-H</td>
+<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>5-Class English Document Area Detection Model, including Text, Title, Table, Image, and List</b>
+<table>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Training Model</a></td>
+<td>97.8</td>
+<td>9.03 / 3.10</td>
+<td>25.82 / 20.70</td>
+<td>7.4</td>
+<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-category Layout Detection Models, including 17 common layout categories: paragraph title, image, text, number, abstract, content, figure title, formula, table, table title, reference, document title, footnote, header, algorithm, footer, seal</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>
 <thead>
 <tr>
@@ -158,7 +166,7 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <td>9.11 / 2.12</td>
 <td>15.42 / 9.12</td>
 <td>4.8</td>
-<td>A high-efficiency layout area localization model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on the lightweight PicoDet-S model</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
 </tr>
 <tr>
 <td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Training Model</a></td>
@@ -166,7 +174,7 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <td>13.50 / 4.69</td>
 <td>43.32 / 43.32</td>
 <td>22.6</td>
-<td>A layout area localization model with balanced efficiency and accuracy, trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on PicoDet-L</td>
+<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
 <td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Training Model</a></td>
@@ -174,11 +182,12 @@ The seal text recognition pipeline is used to recognize the text content of seal
 <td>115.29 / 104.09</td>
 <td>995.27 / 995.27</td>
 <td>470.2</td>
-<td>A high-precision layout area localization model trained on a self-built dataset for Chinese and English papers, magazines, and research reports based on RT-DETR-H</td>
+<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>
 
+
 <p><b>Document Image Orientation Classification Module (Optional):</b></p>
 <table>
 <thead>

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.md

@@ -33,24 +33,24 @@ comments: true
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.en.md

@@ -203,24 +203,24 @@ SVTRv2 is a server-side text recognition model developed by the OpenOCR team fro
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>A high-precision layout region localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports, based on RT-DETR-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>A balanced precision and efficiency layout region localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports, based on PicoDet-L.</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>A high-efficiency layout region localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports, based on PicoDet-S.</td>
 </tr>

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md

@@ -197,24 +197,24 @@ SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队
 <tr>
 <td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/practical_tutorials/document_scene_information_extraction(layout_detection)_tutorial.en.md

@@ -91,82 +91,176 @@ PaddleX provides 4 end-to-end layout detection models, which can be referenced i
 <table>
 <thead>
 <tr>
-<th>Model</th>
+<th>Model</th><th>Model Download Link</th>
 <th>mAP(0.5) (%)</th>
-<th>GPU Inference Time (ms)</th>
-<th>CPU Inference Time (ms)</th>
-<th>Model Size (M)</th>
-<th>Description</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td>PicoDet_layout_1x</td>
-<td>86.8</td>
-<td>13.0</td>
-<td>91.3</td>
-<td>7.4</td>
-<td>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.</td>
+<td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
+<td>90.4</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
+<td>123.76 M</td>
+<td>A high-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.</td>
+</tr>
+<tr>
+<td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
+<td>75.2</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
+<td>22.578</td>
+<td>A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
-<td>PicoDet_layout_1x_table</td>
-<td>95.7</td>
-<td>12.623</td>
-<td>90.8934</td>
+<td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
+<td>70.9</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
+<td>4.834</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.</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 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:
+
+<details><summary> 👉 Details of Model List</summary>
+
+* <b>Table Layout Detection Model</b>
+<table>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Training Model</a></td>
+<td>97.5</td>
+<td>8.02 / 3.09</td>
+<td>23.70 / 20.41</td>
 <td>7.4 M</td>
-<td>An efficient layout area localization model trained on the PubLayNet dataset based on PicoDet-1x can locate one type of tables.</td>
+<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>
+<thead>
 <tr>
-<td>PicoDet-S_layout_3cls</td>
-<td>87.1</td>
-<td>13.5</td>
-<td>45.8</td>
-<td>4.8</td>
-<td>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.</td>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
 </tr>
+</thead>
+<tbody>
 <tr>
-<td>PicoDet-S_layout_17cls</td>
-<td>70.3</td>
-<td>13.6</td>
-<td>46.2</td>
+<td>PicoDet-S_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">Training Model</a></td>
+<td>88.2</td>
+<td>8.99 / 2.22</td>
+<td>16.11 / 8.73</td>
 <td>4.8</td>
-<td>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.</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
 </tr>
 <tr>
-<td>PicoDet-L_layout_3cls</td>
-<td>89.3</td>
-<td>15.7</td>
-<td>159.8</td>
+<td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Training Model</a></td>
+<td>89.0</td>
+<td>13.05 / 4.50</td>
+<td>41.30 / 41.30</td>
 <td>22.6</td>
-<td>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.</td>
+<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
-<td>PicoDet-L_layout_17cls</td>
-<td>79.9</td>
-<td>17.2</td>
-<td>160.2</td>
-<td>22.6</td>
-<td>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.</td>
+<td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Training Model</a></td>
+<td>95.8</td>
+<td>114.93 / 27.71</td>
+<td>947.56 / 947.56</td>
+<td>470.1</td>
+<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>
+<thead>
 <tr>
-<td>RT-DETR-H_layout_3cls</td>
-<td>95.9</td>
-<td>114.6</td>
-<td>3832.6</td>
-<td>470.1</td>
-<td>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.</td>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Training Model</a></td>
+<td>97.8</td>
+<td>9.03 / 3.10</td>
+<td>25.82 / 20.70</td>
+<td>7.4</td>
+<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>
+<thead>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5) (%)</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Training Model</a></td>
+<td>87.4</td>
+<td>9.11 / 2.12</td>
+<td>15.42 / 9.12</td>
+<td>4.8</td>
+<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
+</tr>
+<tr>
+<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Training Model</a></td>
+<td>89.0</td>
+<td>13.50 / 4.69</td>
+<td>43.32 / 43.32</td>
+<td>22.6</td>
+<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
 </tr>
 <tr>
-<td>RT-DETR-H_layout_17cls</td>
-<td>92.6</td>
-<td>115.1</td>
-<td>3827.2</td>
+<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Training Model</a></td>
+<td>98.3</td>
+<td>115.29 / 104.09</td>
+<td>995.27 / 995.27</td>
 <td>470.2</td>
-<td>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.</td>
+<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 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.</b>
+<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>
+</details>
 
 
 ## 4. Data Preparation and Verification

docs/practical_tutorials/document_scene_information_extraction(layout_detection)_tutorial.md

@@ -120,8 +120,10 @@ PaddleX 提供了 11 个版面区域定位模型，具体可参考 [模型列表
 <tr>
 <th>模型</th>
 <th>mAP(0.5)（%）</th>
-<th>GPU推理耗时（ms）</th>
-<th>CPU推理耗时 (ms)</th>
+<th>GPU推理耗时（ms）
+[常规模式 / 高性能模式]</th>
+<th>CPU推理耗时（ms）
+[常规模式 / 高性能模式]</th>
 <th>模型存储大小（M）</th>
 <th>介绍</th>
 </tr>
@@ -130,24 +132,24 @@ PaddleX 提供了 11 个版面区域定位模型，具体可参考 [模型列表
 <tr>
 <td>PP-DocLayout-L</td>
 <td>90.4</td>
-<td>34.5252</td>
-<td>1454.27</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / -</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-M</td>
 <td>75.2</td>
-<td>15.9</td>
-<td>160.1</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
 <td>PP-DocLayout-S</td>
 <td>70.9</td>
-<td>13.8</td>
-<td>46.7</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷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</td>
 </tr>

docs/practical_tutorials/ts_anomaly_detection.en.md

@@ -338,7 +338,7 @@ SubModules:
   TSAnomalyDetection:
     module_name: ts_anomaly_detection
     model_name: PatchTST_ad
-    model_dir: ./output/inference  # 此处替换为您训练后得到的模型权重本地路径
+    model_dir: ./output/inference  # Fine-tuned model path
     batch_size: 1
 ```
 
@@ -349,8 +349,8 @@ from paddlex import create_pipeline
 pipeline = create_pipeline(pipeline="my_path/ts_anomaly_detection.yaml")
 output = pipeline.predict("pre_ts.csv")
 for res in output:
-    res.print() # 打印预测的结构化输出
-    res.save_to_csv("./output/") # 保存csv格式结果
+    res.print() 
+    res.save_to_csv("./output/") 
 ```
 
 For more parameters, please refer to the [Time Series Anomaly Detection Pipeline Usage Tutorial](../pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.en.md)

docs/practical_tutorials/ts_classification.en.md

@@ -309,7 +309,7 @@ SubModules:
   TSClassification:
     module_name: ts_classification
     model_name: TimesNet_cls
-    model_dir: ./output/inference  # 此处替换为您训练后得到的模型权重本地路径
+    model_dir: ./output/inference  # Fine-tuned model weights path
     batch_size: 1
 ```
 
@@ -321,8 +321,8 @@ from paddlex import create_pipeline
 pipeline = create_pipeline(pipeline="my_path/ts_classification.yaml")
 output = pipeline.predict("pre_ts.csv")
 for res in output:
-    res.print() # 打印预测的结构化输出
-    res.save_to_csv("./output/") # 保存csv格式结果
+    res.print() 
+    res.save_to_csv("./output/")
 ```
 
 For more parameters, please refer to the [Time Series Classification Pipeline Usage Tutorial](../pipeline_usage/tutorials/time_series_pipelines/time_series_classification.en.md)

docs/practical_tutorials/ts_forecast.en.md

@@ -491,7 +491,7 @@ SubModules:
   TSForecast:
     module_name: ts_forecast
     model_name: ./output/inference
-    model_dir: null # 此处替换为您训练后得到的模型权重本地路径
+    model_dir: null # fine-tuned model path
     batch_size: 1
 ```