repair doc of model_list and formula module (#4015)

* repair doc of model_list and formula module

* fix doc

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

@@ -13,59 +13,67 @@ The formula recognition module is a crucial component of OCR (Optical Character
 <table>
 <tr>
 <th>Model</th><th>Model Download Link</th>
-<th>Avg-BLEU(%)</th>
+<th>En-BLEU(%)</th>
+<th>Zh-BLEU(%)</th>
 <th>GPU 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>
 <td>UniMERNet</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">Training Model</a></td>
-<td>86.13</td>
+<td>85.91</td>
+<td>43.50</td>
 <td>2266.96/-</td>
 <td>-/-</td>
-<td>1.4 G</td>
+<td>1.53 G</td>
 <td>UniMERNet is a formula recognition model developed by Shanghai AI Lab. It uses Donut Swin as the encoder and MBartDecoder as the decoder. The model is trained on a dataset of one million samples, including simple formulas, complex formulas, scanned formulas, and handwritten formulas, significantly improving the recognition accuracy of real-world formulas.</td>
 <tr>
 <td>PP-FormulaNet-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">Training Model</a></td>
-<td>87.12</td>
+<td>87.00</td>
+<td>45.71</td>
 <td>202.25/-</td>
 <td>-/-</td>
-<td>167.9 M</td>
+<td>224 M</td>
 <td rowspan="2">PP-FormulaNet is an advanced formula recognition model developed by the Baidu PaddlePaddle Vision Team. The PP-FormulaNet-S version uses PP-HGNetV2-B4 as its backbone network. Through parallel masking and model distillation techniques, it significantly improves inference speed while maintaining high recognition accuracy, making it suitable for applications requiring fast inference. The PP-FormulaNet-L version, on the other hand, uses Vary_VIT_B as its backbone network and is trained on a large-scale formula dataset, showing significant improvements in recognizing complex formulas compared to PP-FormulaNet-S.</td>
 </tr>
 <td>PP-FormulaNet-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">Training Model</a></td>
-<td>92.13</td>
+<td>90.36</td>
+<td>45.78</td>
 <td>1976.52/-</td>
 <td>-/-</td>
-<td>535.2 M</td>
+<td>695 M</td>
 <tr>
 <td>PP-FormulaNet_plus-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-S_pretrained.pdparams">Training Model</a></td>
-<td>-</td>
+<td>88.71</td>
+<td>53.32</td>
+<td>191.69/-</td>
 <td>-/-</td>
-<td>-/-</td>
-<td>-</td>
-<td rowspan="2">- </td>
+<td>248 M</td>
+<td rowspan="3">PP-FormulaNet_plus is an enhanced version of the formula recognition model developed by the Baidu PaddlePaddle Vision Team, building upon the original PP-FormulaNet. Compared to the original version, PP-FormulaNet_plus utilizes a more diverse formula dataset during training, including sources such as Chinese dissertations, professional books, textbooks, exam papers, and mathematics journals. This expansion significantly improves the model’s recognition capabilities. Among the models, PP-FormulaNet_plus-M and PP-FormulaNet_plus-L have added support for Chinese formulas and increased the maximum number of predicted tokens for formulas from 1,024 to 2,560, greatly enhancing the recognition performance for complex formulas. Meanwhile, the PP-FormulaNet_plus-S model focuses on improving the recognition of English formulas. With these improvements, the PP-FormulaNet_plus series models perform exceptionally well in handling complex and diverse formula recognition tasks. </td>
 </tr>
 <tr>
 <td>PP-FormulaNet_plus-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-M_pretrained.pdparams">Training Model</a></td>
-<td>-</td>
-<td>-/-</td>
+<td>91.45</td>
+<td>89.76</td>
+<td>1301.56/-</td>
 <td>-/-</td>
-<td>-</td>
+<td>592 M</td>
 </tr>
 <tr>
 <td>PP-FormulaNet_plus-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-L_pretrained.pdparams">Training Model</a></td>
-<td>-</td>
+<td>92.22</td>
+<td>90.64</td>
+<td>1745.25/-</td>
 <td>-/-</td>
-<td>-/-</td>
-<td>-</td>
+<td>698 M</td>
 </tr>
 <tr>
 <td>LaTeX_OCR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">Training Model</a></td>
-<td>71.63</td>
-<td>-/-</td>
+<td>74.55</td>
+<td>39.96</td>
+<td>1244.61/-</td>
 <td>-/-</td>
-<td>89.7 M</td>
+<td>99 M</td>
 <td>LaTeX-OCR is a formula recognition algorithm based on an autoregressive large model. It uses Hybrid ViT as the backbone network and a transformer as the decoder, significantly improving the accuracy of formula recognition.</td>
 </tr>
 </table>
@@ -124,7 +132,7 @@ After installing the wheel package, you can complete the inference of the formul
 
 ```python
 from paddlex import create_model
-model = create_model(model_name="PP-FormulaNet-S")
+model = create_model(model_name="PP-FormulaNet_plus-M")
 output = model.predict(input="general_formula_rec_001.png", batch_size=1)
 for res in output:
     res.print()
@@ -135,7 +143,7 @@ for res in output:
 After running, the result obtained is:
 
 ````
-{'res': {'input_path': 'general_formula_rec_001.png', 'page_index': None, 'rec_formula': '\\zeta_{0}(\\nu)=-{\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z{\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}}\\ \\ {vec\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'}}
+{'res': {'input_path': 'general_formula_rec_001.png', 'page_index': None, 'rec_formula': '\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'}}
 ````
 
 The meanings of the running results parameters are as follows:
@@ -155,7 +163,7 @@ sudo apt-get install texlive texlive-latex-base texlive-xetex latex-cjk-all texl
 
 The explanations for the methods, parameters, etc., are as follows:
 
-* The `create_model` method instantiates the formula recognition model (here, `PP-FormulaNet-S` is used as an example), and the specific explanations are as follows:
+* The `create_model` method instantiates the formula recognition model (here, `PP-FormulaNet_plus-M` is used as an example), and the specific explanations are as follows:
 <table>
 <thead>
 <tr>
@@ -343,7 +351,7 @@ tar -xf ./dataset/ocr_rec_latexocr_dataset_example.tar -C ./dataset/
 A single command can complete data validation:
 
 ```bash
-python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 ```
@@ -426,13 +434,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>Then execute the command:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 </code></pre>
 <p>After the data conversion is executed, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
 <p>The above parameters also support being set by appending command line arguments:</p>
-<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example \
     -o CheckDataset.convert.enable=True \
@@ -457,13 +465,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>Then execute the command:</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 </code></pre>
 <p>After the data splitting is executed, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
 <p>The above parameters also support being set by appending command line arguments:</p>
-<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example \
     -o CheckDataset.split.enable=True \
@@ -472,21 +480,21 @@ CheckDataset:
 </code></pre></details>
 
 ### 4.2 Model Training
-Model training can be completed with a single command, taking the training of the formula recognition model PP-FormulaNet-S as an example:
+Model training can be completed with a single command, taking the training of the formula recognition model PP-FormulaNet_plus-M as an example:
 
 ```bash
-FLAGS_json_format_model=1 python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml  \
+FLAGS_json_format_model=1 python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml  \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 ```
 The following steps are required:
 
-* Specify the `.yaml` configuration file path for the model (here it is `PP-FormulaNet-S.yaml`,When training other models, you need to specify the corresponding configuration files. The relationship between the model and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list.en.md))
+* Specify the `.yaml` configuration file path for the model (here it is `PP-FormulaNet_plus-M.yaml`,When training other models, you need to specify the corresponding configuration files. The relationship between the model and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list.en.md))
 * Set the mode to model training: `-o Global.mode=train`
 * Specify the path to the training dataset: `-o Global.dataset_dir`.
 * Other related parameters can be set by modifying the `Global` and `Train` fields in the `.yaml` configuration file, or adjusted by appending parameters in the command line. For example, to specify training on the first two GPUs: `-o Global.device=gpu:0,1`; to set the number of training epochs to 10: `-o Train.epochs_iters=10`. For more modifiable parameters and their detailed explanations, refer to the configuration file instructions for the corresponding task module of the model [PaddleX Common Configuration File Parameters](../../instructions/config_parameters_common.en.md).
 *  Except for LaTeX_OCR_rec, the formula recognition models only support exporting models in JSON format. Therefore, during training, you need to set the parameter `FLAGS_json_format_model=1`.
-*  For the PP-FormulaNet-S, PP-FormulaNet-L, and UniMERNet models, additional Linux packages need to be installed during training. The specific command is as follows:
+*  For the PP-FormulaNet-S, PP-FormulaNet-L, PP-FormulaNet_plus-S、PP-FormulaNet_plus-M、PP-FormulaNet_plus-L and UniMERNet models, additional Linux packages need to be installed during training. The specific command is as follows:
 * New Feature: Paddle 3.0 support CINN (Compiler Infrastructure for Neural Networks) to accelerate training speed when using GPU device. Please specify `-o Train.dy2st=True` to enable it.
 
 ```bash
@@ -518,13 +526,13 @@ python -m pip install Wand
 After completing model training, you can evaluate the specified model weight file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation can be done with a single command:
 
 ```bash
-python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml  \
+python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml  \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 ```
 Similar to model training, the following steps are required:
 
-* Specify the `.yaml` configuration file path for the model (here it is `PP-FormulaNet-S.yaml`)
+* Specify the `.yaml` configuration file path for the model (here it is `PP-FormulaNet_plus-M.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Specify the path to the validation dataset: `-o Global.dataset_dir`.
 Other related parameters can be set by modifying the `Global` and `Evaluate` fields in the `.yaml` configuration file, detailed instructions can be found in [PaddleX Common Configuration File Parameters](../../instructions/config_parameters_common.en.md).
@@ -542,14 +550,14 @@ After completing model training and evaluation, you can use the trained model we
 #### 4.4.1 Model Inference
 To perform inference prediction through the command line, simply use the following command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png) to your local machine.
 ```bash
-python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_accuracy/inference" \
     -o Predict.input="general_formula_rec_001.png"
 ```
 Similar to model training and evaluation, the following steps are required:
 
-* Specify the `.yaml` configuration file path for the model (here it is `PP-FormulaNet-S.yaml`)
+* Specify the `.yaml` configuration file path for the model (here it is `PP-FormulaNet_plus-M.yaml`)
 * Set the mode to model inference prediction: `-o Global.mode=predict`
 * Specify the model weights path: `-o Predict.model_dir="./output/best_accuracy/inference"`
 * Specify the input data path: `-o Predict.input="..."`.

docs/module_usage/tutorials/ocr_modules/formula_recognition.md

@@ -12,60 +12,71 @@ comments: true
 <table>
 <tr>
 <th>模型</th><th>模型下载链接</th>
-<th>Avg-BLEU(%)</th>
+<th>En-BLEU(%)</th>
+<th>Zh-BLEU(%)</th>
 <th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>模型存储大小 (M)</th>
 <th>介绍</th>
 </tr>
 <td>UniMERNet</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">训练模型</a></td>
-<td>86.13</td>
+<td>85.91</td>
+<td>43.50</td>
 <td>2266.96/-</td>
 <td>-/-</td>
-<td>1.4 G</td>
+<td>1.53 G</td>
 <td>UniMERNet是由上海AI Lab研发的一款公式识别模型。该模型采用Donut Swin作为编码器，MBartDecoder作为解码器，并通过在包含简单公式、复杂公式、扫描捕捉公式和手写公式在内的一百万数据集上进行训练，大幅提升了模型对真实场景公式的识别准确率</td>
 <tr>
 <td>PP-FormulaNet-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">训练模型</a></td>
-<td>87.12</td>
+<td>87.00</td>
+<td>45.71</td>
 <td>202.25/-</td>
 <td>-/-</td>
-<td>167.9 M</td>
+<td>224 M</td>
 <td rowspan="2">PP-FormulaNet 是由百度飞桨视觉团队开发的一款先进的公式识别模型，支持5万个常见LateX源码词汇的识别。PP-FormulaNet-S 版本采用了 PP-HGNetV2-B4 作为其骨干网络，通过并行掩码和模型蒸馏等技术，大幅提升了模型的推理速度，同时保持了较高的识别精度，适用于简单印刷公式、跨行简单印刷公式等场景。而 PP-FormulaNet-L 版本则基于 Vary_VIT_B 作为骨干网络，并在大规模公式数据集上进行了深入训练，在复杂公式的识别方面，相较于PP-FormulaNet-S表现出显著的提升，适用于简单印刷公式、复杂印刷公式、手写公式等场景。 </td>
 
 </tr>
 <td>PP-FormulaNet-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">训练模型</a></td>
-<td>92.13</td>
+<td>90.36</td>
+<td>45.78</td>
 <td>1976.52/-</td>
 <td>-/-</td>
-<td>535.2 M</td>
+<td>695 M</td>
 <tr>
 <td>PP-FormulaNet_plus-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-S_pretrained.pdparams">训练模型</a></td>
-<td>-</td>
+<td>88.71</td>
+<td>53.32</td>
+<td>191.69/-</td>
 <td>-/-</td>
-<td>-/-</td>
-<td>-</td>
-<td rowspan="2">- </td>
+<td>248 M</td>
+<td rowspan="3">PP-FormulaNet_plus 是百度飞桨视觉团队在 PP-FormulaNet 的基础上开发的增强版公式识别模型。与原版相比，PP-FormulaNet_plus 在训练中使用了更为丰富的公式数据集，包括中文学位论文、专业书籍、教材试卷以及数学期刊等多种来源。这一扩展显著提升了模型的识别能力。
+
+其中，PP-FormulaNet_plus-M 和 PP-FormulaNet_plus-L 模型新增了对中文公式的支持，并将公式的最大预测 token 数从 1024 扩大至 2560，大幅提升了对复杂公式的识别性能。同时，PP-FormulaNet_plus-S 模型则专注于增强英文公式的识别能力。通过这些改进，PP-FormulaNet_plus 系列模型在处理复杂多样的公式识别任务时表现更加出色。 </td>
 </tr>
 <tr>
 <td>PP-FormulaNet_plus-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-M_pretrained.pdparams">训练模型</a></td>
-<td>-</td>
-<td>-/-</td>
+<td>91.45</td>
+<td>89.76</td>
+<td>1301.56/-</td>
 <td>-/-</td>
-<td>-</td>
+<td>592 M</td>
 </tr>
 <tr>
 <td>PP-FormulaNet_plus-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-L_pretrained.pdparams">训练模型</a></td>
-<td>-</td>
+<td>92.22</td>
+<td>90.64</td>
+<td>1745.25/-</td>
 <td>-/-</td>
-<td>-/-</td>
-<td>-</td>
+<td>698 M</td>
 </tr>
+
 <tr>
 <td>LaTeX_OCR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">训练模型</a></td>
-<td>71.63</td>
-<td>-/-</td>
+<td>74.55</td>
+<td>39.96</td>
+<td>1244.61/-</td>
 <td>-/-</td>
-<td>89.7 M</td>
+<td>99 M</td>
 <td>LaTeX-OCR是一种基于自回归大模型的公式识别算法，通过采用 Hybrid ViT 作为骨干网络，transformer作为解码器，显著提升了公式识别的准确性。</td>
 </tr>
 </table>
@@ -120,7 +131,7 @@ wheel 包的安装后，几行代码即可完成公式识别模块的推理，
 
 ```python
 from paddlex import create_model
-model = create_model(model_name="PP-FormulaNet-S")
+model = create_model(model_name="PP-FormulaNet_plus-M")
 output = model.predict(input="general_formula_rec_001.png", batch_size=1)
 for res in output:
     res.print()
@@ -129,7 +140,7 @@ for res in output:
 ```
 运行后，得到的结果为：
 ```bash
-{'res': {'input_path': 'general_formula_rec_001.png', 'page_index': None, 'rec_formula': '\\zeta_{0}(\\nu)=-{\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z{\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}}\\ \\ {vec\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'}}
+{'res': {'input_path': 'general_formula_rec_001.png', 'page_index': None, 'rec_formula': '\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'}}
 ```
 运行结果参数含义如下：
 - `input_path`：表示输入待预测公式图像的路径
@@ -149,7 +160,7 @@ sudo apt-get install texlive texlive-latex-base texlive-xetex latex-cjk-all texl
 
 相关方法、参数等说明如下：
 
-* `create_model`实例化公式识别模型（此处以`PP-FormulaNet-S`为例），具体说明如下：
+* `create_model`实例化公式识别模型（此处以`PP-FormulaNet_plus-M`为例），具体说明如下：
 <table>
 <thead>
 <tr>
@@ -338,7 +349,7 @@ tar -xf ./dataset/ocr_rec_latexocr_dataset_example.tar -C ./dataset/
 一行命令即可完成数据校验：
 
 ```bash
-python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 ```
@@ -422,13 +433,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>随后执行命令：</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 </code></pre>
 <p>数据转换执行之后，原有标注文件会被在原路径下重命名为 <code>xxx.bak</code>。</p>
 <p>以上参数同样支持通过追加命令行参数的方式进行设置：</p>
-<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example \
     -o CheckDataset.convert.enable=True \
@@ -453,13 +464,13 @@ CheckDataset:
   ......
 </code></pre>
 <p>随后执行命令：</p>
-<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 </code></pre>
 <p>数据划分执行之后，原有标注文件会被在原路径下重命名为 <code>xxx.bak</code>。</p>
 <p>以上参数同样支持通过追加命令行参数的方式进行设置：</p>
-<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+<pre><code class="language-bash">python main.py -c  paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example \
     -o CheckDataset.split.enable=True \
@@ -468,21 +479,21 @@ CheckDataset:
 </code></pre></details>
 
 ### 4.2 模型训练
-一条命令即可完成模型的训练，以此处公式识别模型 PP-FormulaNet-S 的训练为例：
+一条命令即可完成模型的训练，以此处公式识别模型 PP-FormulaNet_plus-M 的训练为例：
 
 ```bash
-FLAGS_json_format_model=1 python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml  \
+FLAGS_json_format_model=1 python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml  \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 ```
 需要如下几步：
 
-* 指定模型的`.yaml` 配置文件路径（此处为`PP-FormulaNet-S.yaml`，训练其他模型时，需要的指定相应的配置文件，模型和配置的文件的对应关系，可以查阅[PaddleX模型列表（CPU/GPU）](../../../support_list/models_list.md)）
+* 指定模型的`.yaml` 配置文件路径（此处为`PP-FormulaNet_plus-M.yaml`，训练其他模型时，需要的指定相应的配置文件，模型和配置的文件的对应关系，可以查阅[PaddleX模型列表（CPU/GPU）](../../../support_list/models_list.md)）
 * 指定模式为模型训练：`-o Global.mode=train`
 * 指定训练数据集路径：`-o Global.dataset_dir`
 * 其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置，也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练：`-o Global.device=gpu:0,1`；设置训练轮次数为 10：`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释，可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 * 除 LaTeX_OCR_rec外， 公式识别模型只支持导出json格式的模型，因此训练时需要设置参数`FLAGS_json_format_model=1`。
-* 对于 PP-FormulaNet-S、PP-FormulaNet-L、UniMERNet 模型，在训练还需要安装额外的Linux包，具体命令如下：
+* 对于 PP-FormulaNet-S、PP-FormulaNet-L、PP-FormulaNet_plus-S、PP-FormulaNet_plus-M、PP-FormulaNet_plus-L 和 UniMERNet 模型，在训练还需要安装额外的Linux包，具体命令如下：
 ```bash
 sudo apt-get update
 sudo apt-get install libmagickwand-dev
@@ -511,13 +522,13 @@ python -m pip install Wand
 在完成模型训练后，可以对指定的模型权重文件在验证集上进行评估，验证模型精度。使用 PaddleX 进行模型评估，一条命令即可完成模型的评估：
 
 ```bash
-python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml  \
+python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml  \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
 ```
 与模型训练类似，需要如下几步：
 
-* 指定模型的`.yaml` 配置文件路径（此处为`PP-FormulaNet-S.yaml`）
+* 指定模型的`.yaml` 配置文件路径（此处为`PP-FormulaNet_plus-M.yaml`）
 * 指定模式为模型评估：`-o Global.mode=evaluate`
 * 指定验证数据集路径：`-o Global.dataset_dir`
 其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Evaluate`下的字段来进行设置，详细请参考[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
@@ -534,14 +545,14 @@ python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.ya
 
 * 通过命令行的方式进行推理预测，只需如下一条命令。运行以下代码前，请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png)到本地。
 ```bash
-python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
+python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_accuracy/inference" \
     -o Predict.input="general_formula_rec_001.png"
 ```
 与模型训练和评估类似，需要如下几步：
 
-* 指定模型的`.yaml` 配置文件路径（此处为`PP-FormulaNet-S.yaml`）
+* 指定模型的`.yaml` 配置文件路径（此处为`PP-FormulaNet_plus-M.yaml`）
 * 指定模式为模型推理预测：`-o Global.mode=predict`
 * 指定模型权重路径：`-o Predict.model_dir="./output/best_accuracy/inference"`
 * 指定输入数据路径：`-o Predict.input="..."`

docs/module_usage/tutorials/speech_modules/multilingual_speech_recognition.md

@@ -49,7 +49,7 @@ comments: true
     <td>-</td>
   </tr>
   <tr>
-    <td>whisper_small</td>
+    <td>whisper_tiny</td>
     <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/whisper_tiny.tar">whisper_tiny</a></td>
     <td>680kh</td>
     <td>145M</td>

docs/support_list/models_list.en.md

@@ -876,30 +876,6 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 </table>
 <b>Note: The above accuracy metrics are based on the AliProducts recall@1.</b>
 
-## [Document Orientation Classification Module](../module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md)
-<table>
-<thead>
-<tr>
-<th>Model Name</th>
-<th>Top-1 Acc (%)</th>
-<th>GPU 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</th>
-<th>yaml File</th>
-<th>Model Download Link</th></tr>
-</thead>
-<tbody>
-<tr>
-<td>PP-LCNet_x1_0_doc_ori</td>
-<td>99.06</td>
-<td>2.31 / 0.43</td>
-<td>3.37 / 1.27</td>
-<td>7</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_text_orientation/PP-LCNet_x1_0_doc_ori.yaml">PP-LCNet_x1_0_doc_ori.yaml</a></td>
-<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">Training Model</a></td></tr>
-</tbody>
-</table>
-<b>Note: The above accuracy metrics are based on the Top-1 Acc of the internal dataset of PaddleX.</b>
 
 ## [Face Feature Module](../module_usage/tutorials/cv_modules/face_feature.en.md)
 <table>
@@ -1301,6 +1277,47 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>351.5 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/YOLOX-X.yaml">YOLOX-X.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/YOLOX-X_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-X_pretrained.pdparams">Training Model</a></td></tr>
+
+
+
+
+
+<tr>
+<td>Co-Deformable-DETR-R50</td>
+<td>49.7</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>184 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-Deformable-DETR-R50.yaml">Co-Deformable-DETR-R50.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-Deformable-DETR-R50_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-Deformable-DETR-R50_pretrained.pdparams">Training Model</a></td></tr>
+
+<tr>
+<td>Co-Deformable-DETR-Swin-T</td>
+<td>48.0</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>    187 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-Deformable-DETR-Swin-T.yaml">Co-Deformable-DETR-Swin-T.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-Deformable-DETR-Swin-T_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-Deformable-DETR-Swin-T_pretrained.pdparams">Training Model</a></td></tr>
+
+<tr>
+<td>Co-DINO-Swin-L</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>841 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-DINO-Swin-L.yaml">Co-DINO-Swin-L.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-DINO-Swin-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-DINO-Swin-L_pretrained.pdparams">Training Model</a></td></tr>
+
+<tr>
+<td>Co-DINO-R50</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>187 M </td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-DINO-R50.yaml">Co-DINO-R50.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-DINO-R50_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-DINO-R50_pretrained.pdparams">Training Model</a></td></tr>
+
 </tbody>
 </table>
 <b>Note: The above accuracy metrics are based on the COCO2017 validation set mAP(0.5:0.95).</b>
@@ -1327,7 +1344,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/small_object_detection/PP-YOLOE_plus_SOD-S.yaml">PP-YOLOE_plus_SOD-S.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus_SOD-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-S_pretrained.pdparams">Training Model</a></td></tr>
 </tbody>
-</table>
+
 <tr>
 <td>PP-YOLOE_plus_SOD-L</td>
 <td>31.9</td>
@@ -1346,7 +1363,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/small_object_detection/PP-YOLOE_plus_SOD-largesize-L.yaml">PP-YOLOE_plus_SOD-largesize-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus_SOD-largesize-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-largesize-L_pretrained.pdparams">Training Model</a></td>
 </tr>
-
+</table>
 
 <b>Note: The above accuracy metrics are based on the validation set mAP(0.5:0.95) of </b>[VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset)<b>.</b>
 
@@ -1360,6 +1377,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <th>GPU 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>yaml File</th>
 <th>Model Download Link</th>
 </tr>
 <tr>
@@ -1369,6 +1387,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>253.72</td>
 <td>1807.4</td>
 <td>658.3</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_detection/GroundingDINO-T.yaml">GroundingDINO-T.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/GroundingDINO-T_infer.tar">Inference Model</a></td>
 </tr>
 <tr>
@@ -1378,6 +1397,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>24.32</td>
 <td>374.89</td>
 <td>421.4</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_detection/YOLO-Worldv2-L.yaml">YOLO-Worldv2-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/YOLO-Worldv2-L_infer.tar">Inference Model</a></td>
 </tr>
 </table>
@@ -1391,6 +1411,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <th>GPU 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>yaml File</th>
 <th>Model Download Link</th>
 </tr>
 <tr>
@@ -1398,6 +1419,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>144.9</td>
 <td>33920.7</td>
 <td>2433.7</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_segmentation/SAM-H_box.yaml">SAM-H_box.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SAM-H_box_infer.tar">Inference Model</a></td>
 </tr>
 <tr>
@@ -1405,6 +1427,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>144.9</td>
 <td>33920.7</td>
 <td>2433.7</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_segmentation/SAM-H_point.yaml">SAM-H_point.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SAM-H_point_infer.tar">Inference Model</a></td>
 </tr>
 </table>
@@ -1427,7 +1450,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>20.7039</td>
 <td>157.942</td>
 <td>211.0 M</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/rotated_object_detection/PP-YOLOE-R-L.yaml">PP-YOLOE-R.yaml</a></td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/rotated_object_detection/PP-YOLOE-R-L.yaml">PP-YOLOE-R-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE-R-L_infer.tar">Inference Model</a>/<a href="https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_l_3x_dota.pdparams">Training Model</a></td>
 </tr>
 </table>
@@ -1542,7 +1565,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>22.54 / 8.33</td>
 <td>138.67 / 138.67</td>
 <td>26.5 M</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/face_detection/PP-YOLOE_plus-S_face.yaml">PP-YOLOE_plus-S_face</a></td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/face_detection/PP-YOLOE_plus-S_face.yaml">PP-YOLOE_plus-S_face.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus-S_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-S_face_pretrained.pdparams">Training Model</a></td></tr>
 </tbody>
 </table>
@@ -1574,6 +1597,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 </table>
 <b>Note: The above precision metrics are the average anomaly scores on the validation set of </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b>.</b>
 
+## [ Human Keypoint Detection Module](../module_usage/tutorials//cv_modules/human_keypoint_detection.en.md)
 
 <table>
 <tr>
@@ -1771,6 +1795,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>229.7 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/SegFormer-B5.yaml">SegFormer-B5.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SegFormer-B5 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B5 (slice)_pretrained.pdparams">Training Model</a></td></tr>
+
 </tbody>
 </table>
 <b>Note: The above accuracy metrics are based on the </b>[Cityscapes](https://www.cityscapes-dataset.com/)<b> dataset mIoU.</b>
@@ -1818,6 +1843,26 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>6.1M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/SeaFormer_tiny.yaml">SeaFormer_tiny.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SeaFormer_tiny (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_tiny (slice)_pretrained.pdparams">Training Model</a></td></tr>
+
+
+<tr>
+<td>MaskFormer_small</td>
+<td>49.70</td>
+<td>69.856</td>
+<td>-</td>
+<td>242.5 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/MaskFormer_small.yaml">MaskFormer_small.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MaskFormer_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_small_pretrained.pdparams">Training Model</a></td></tr>
+
+<tr>
+<td>MaskFormer_tiny</td>
+<td>46.69</td>
+<td>50.157</td>
+<td>-</td>
+<td>160.5 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/MaskFormer_tiny.yaml">MaskFormer_tiny.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MaskFormer_tiny_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_tiny_pretrained.pdparams">Training Model</a></td></tr>
+
 </tbody>
 </table>
 <b>Note: The above accuracy metrics are based on the </b>[ADE20k](https://groups.csail.mit.edu/vision/datasets/ADE20K/)<b> dataset. "Slice" indicates that the input images have been cropped.</b>
@@ -1987,6 +2032,26 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 </tr>
 </thead>
 <tbody>
+
+<tr>
+<td>PP-OCRv5_server_det</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>101</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv5_server_det.yaml">PP-OCRv5_server_det.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">Training Model</a></td>
+</tr>
+<tr>
+<td>PP-OCRv5_mobile_det</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>20</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml">PP-OCRv5_mobile_det.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">Training Model</a></td>
+</tr>
+
 <tr>
 <td>PP-OCRv4_server_det</td>
 <td>82.56</td>
@@ -2073,6 +2138,26 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <th>yaml File</th>
 <th>Model Download Link</th>
 </tr>
+
+<tr>
+<td>PP-OCRv5_server_rec</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>206 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv5_server_rec.yaml">PP-OCRv5_server_rec.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+</tr>
+<tr>
+<td>PP-OCRv5_mobile_rec</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>137 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv5_mobile_rec.yaml">PP-OCRv5_mobile_rec.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_rec_infer.tar">Inference Model</a>/<a href="">Training Model</a></td>
+</tr>
+
 <tr>
 <td>PP-OCRv4_server_rec_doc</td>
 <td>81.53</td>
@@ -2295,7 +2380,8 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <table>
 <tr>
 <th>Model</th>
-<th>Avg-BLEU(%)</th>
+<th>En-BLEU(%)</th>
+<th>Zh-BLEU(%)</th>
 <th>GPU 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>
@@ -2303,34 +2389,68 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <th>Model Download Link</th>
 </tr>
 <td>UniMERNet</td>
-<td>86.13</td>
+<td>85.91</td>
+<td>43.50</td>
 <td>2266.96/-</td>
 <td>-/-</td>
-<td>1.4 G</td>
+<td>1.53 G</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/UniMERNet.yaml">UniMERNet.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">Training Model</a></td>
 <tr>
 <td>PP-FormulaNet-S</td>
-<td>87.12</td>
+<td>87.00</td>
+<td>45.71</td>
 <td>202.25/-</td>
 <td>-/-</td>
-<td>167.9 M</td>
+<td>224 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml">PP-FormulaNet-S.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">Training Model</a></td>
 </tr>
 <td>PP-FormulaNet-L</td>
-<td>92.13</td>
+<td>90.36</td>
+<td>45.78</td>
 <td>1976.52/-</td>
 <td>-/-</td>
-<td>535.2 M</td>
+<td>695 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet-L.yaml">PP-FormulaNet-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">Training Model</a></td>
 <tr>
-<td>LaTeX_OCR_rec</td>
-<td>71.63</td>
+<td>PP-FormulaNet_plus-S</td>
+<td>88.71</td>
+<td>53.32</td>
+<td>191.69/-</td>
 <td>-/-</td>
+<td>248 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-S.yaml">PP-FormulaNet_plus-S.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-S_pretrained.pdparams">Training Model</a></td>
+</tr>
+<tr>
+<td>PP-FormulaNet_plus-M</td>
+<td>91.45</td>
+<td>89.76</td>
+<td>1301.56/-</td>
 <td>-/-</td>
-<td>89.7 M</td>
+<td>592 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml">PP-FormulaNet_plus-M.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-M_pretrained.pdparams">Training Model</a></td>
+</tr>
+<tr>
+<td>PP-FormulaNet_plus-L</td>
+<td>92.22</td>
+<td>90.64</td>
+<td>1745.25/-</td>
+<td>-/-</td>
+<td>698 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-L.yaml">PP-FormulaNet_plus-L.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-L_pretrained.pdparams">Training Model</a></td>
+</tr>
+<tr>
+<td>LaTeX_OCR_rec</td>
+<td>74.55</td>
+<td>39.96</td>
+<td>1244.61/-</td>
+<td>-/-</td>
+<td>99 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/LaTeX_OCR_rec.yaml">LaTeX_OCR_rec.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">Training Model</a></td>
 </tr>
@@ -2388,25 +2508,28 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 
 <table>
 <tr>
-<th>Model</th><th>Model Download Link</th>
+<th>Model</th>
 <th>mAP(%)</th>
 <th>GPU 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>
+<th>yaml File</th>
+<th>Model Download Link</th>
 </tr>
 <tr>
 <td>RT-DETR-L_wired_table_cell_det</td>
-<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wired_table_cell_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">Training Model</a></td>
+
 <td rowspan="2">82.7</td>
 <td rowspan="2">35.00 / 10.45</td>
 <td rowspan="2">495.51 / 495.51</td>
 <td rowspan="2">124M</td>
-<td rowspan="2">RT-DETR is the first real-time end-to-end object detection model. The Baidu PaddlePaddle Vision Team, based on RT-DETR-L as the base model, has completed pretraining on a self-built table cell detection dataset, achieving good performance for both wired and wireless table cell detection.
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml">RT-DETR-L_wired_table_cell_det.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wired_table_cell_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">Training Model</a></td>
 </td>
 </tr>
 <tr>
 <td>RT-DETR-L_wireless_table_cell_det</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_cells_detection/RT-DETR-L_wireless_table_cell_det.yaml">RT-DETR-L_wireless_table_cell_det.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wireless_table_cell_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wireless_table_cell_det_pretrained.pdparams">Training Model</a></td>
 </tr>
 </table>
@@ -2426,10 +2549,10 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 </tr>
 <tr>
 <td>PP-LCNet_x1_0_table_cls</td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
+<td>94.2</td>
+<td>2.35 / 0.47</td>
+<td>4.03 / 1.35</td>
+<td>6.6M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_classification/PP-LCNet_x1_0_table_cls.yaml">PP-LCNet_x1_0_table_cls.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/CLIP_vit_base_patch16_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">Training Model</a></td>
 </tr>
@@ -2465,6 +2588,110 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 
 ## [Layout Detection Module](../module_usage/tutorials/ocr_modules/layout_detection.en.md)
 
+* <b>Layout detection model, including 20 common categories: document title, section title, text, page number, abstract, table of contents, references, footnote, header, footer, algorithm, formula, formula number, image, table, figure and table captions (figure caption, table caption, and chart caption), stamp, chart, sidebar text, and reference content.</b>
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mAP(0.5)（%）</th>
+<th>GPU 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</th>
+<th>yaml File</th>
+<th>Model Download Link</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-DocLayout_plus-L</td>
+<td>83.2</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / - </td>
+<td>126.01 </td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout_plus-L.yaml">PP-DocLayout_plus-L.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout_plus-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout_plus-L_pretrained.pdparams">Training Model</a></td>
+</tr>
+</tbody>
+</table>
+
+<b>Note: The evaluation set for the accuracy metrics mentioned above is a custom-built layout detection dataset, which includes 1,300 document-type images such as Chinese and English papers, magazines, newspapers, research reports, PPTs, exam papers, and textbooks.</b>
+
+* <b>Layout detection model, including 20 common categories: document title, section title, text, page number, abstract, table of contents, references, footnote, header, footer, algorithm, formula, formula number, image, table, figure and table captions (figure caption, table caption, and chart caption), stamp, chart, sidebar text, and reference content.</b>
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mAP(0.5)（%）</th>
+<th>GPU 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</th>
+<th>yaml File</th>
+<th>Model Download Link</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-DocBlockLayout</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / - </td>
+<td>123.92 </td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocBlockLayout.yaml">PP-DocBlockLayout.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBlockLayout_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocBlockLayout_pretrained.pdparams">Training Model</a></td>
+</tr>
+</tbody>
+</table>
+
+<b>Note: The evaluation set for the accuracy metrics mentioned above is a custom-built layout detection dataset, which includes 1,300 document-type images such as Chinese and English papers, magazines, newspapers, research reports, PPTs, exam papers, and textbooks.</b>
+
+
+* <b>The layout detection model includes 23 common categories: document title, paragraph title, text, page number, abstract, table of contents, references, footnotes, header, footer, algorithm, formula, formula number, image, figure caption, table, table caption, seal, figure title, figure, header image, footer image, and sidebar text. </b>
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mAP(0.5)（%）</th>
+<th>GPU 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</th>
+<th>yaml File</th>
+<th>Model Download Link</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-DocLayout-L</td>
+<td>90.4</td>
+<td>34.6244 / 10.3945</td>
+<td>510.57 / - </td>
+<td>123.76 </td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout-L.yaml">PP-DocLayout-L.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/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>
+</tr>
+<tr>
+<td>PP-DocLayout-M</td>
+<td>75.2</td>
+<td>13.3259 / 4.8685</td>
+<td>44.0680 / 44.0680</td>
+<td>22.578</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout-M.yaml">PP-DocLayout-M.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/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>
+</tr>
+<tr>
+<td>PP-DocLayout-S</td>
+<td>70.9</td>
+<td>8.3008 / 2.3794</td>
+<td>10.0623 / 9.9296</td>
+<td>4.834</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout-S.yaml">PP-DocLayout-S.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/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>
+</tr>
+</tbody>
+</table>
+
+<b>Note: The evaluation set for the accuracy metrics mentioned above is a custom-built layout region detection dataset, which includes 500 common document-type images such as Chinese and English papers, magazines, and research reports.</b>
+
+
 * <b>Table Layout Detection Model</b>
 <table>
 <thead>
@@ -2583,7 +2810,6 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PicoDet-S_layout_17cls.yaml">PicoDet-S_layout_17cls.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/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>
 </tr>
-</tbody></table>
 <tr>
 <td>PicoDet-L_layout_17cls</td>
 <td>89.0</td>
@@ -2602,7 +2828,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/RT-DETR-H_layout_17cls.yaml">RT-DETR-H_layout_17cls.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/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>
 </tr>
-
+</table>
 
 <b>Note: The evaluation set for the above accuracy metrics is the layout area detection dataset built by PaddleOCR, which includes 892 images of common document types such as Chinese and English papers, magazines, and research reports. </b>
 
@@ -2775,6 +3001,16 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>320 K</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/ts_anomaly_detection/PatchTST_ad.yaml">PatchTST_ad.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PatchTST_ad_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PatchTST_ad_pretrained.pdparams">Training Model</a></td></tr>
+
+<tr>
+<td>TimesNet_ad</td>
+<td>-</td>
+<td>-</td>
+<td>-</td>
+<td>1000 K</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/ts_anomaly_detection/TimesNet_ad.yaml">TimesNet_ad.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/TimesNet_ad_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/TimesNet_ad_pretrained.pdparams">Training Model</a></td></tr>
+
 </tbody>
 </table>
 <b>Note: The above precision metrics are measured from the </b>[PSM](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ts_anomaly_examples.tar)<b> dataset.</b>
@@ -2849,7 +3085,7 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <td>680kh</td>
 <td>145M</td>
 <td>-</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/multilingual_speech_recognition/whisper_small.yaml">whisper_small.yaml</a></td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/multilingual_speech_recognition/whisper_tiny.yaml">whisper_tiny.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/whisper_tiny.tar">Inference Model</a></td>
 </tr>
 </table>
@@ -2915,30 +3151,48 @@ PaddleX includes multiple pipelines, each containing several modules, and each m
 <th>Model</th>
 <th>Model Parameter Size（B）</th>
 <th>Model Storage Size（GB）</th>
+<th>yaml File</th>
 <th>Model Download Lin</th>
 </tr>
 <tr>
 <td>PP-DocBee-2B</td>
 <td>2</td>
 <td>4.2</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_vlm/PP-DocBee-2B.yaml">PP-DocBee-2B.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee-2B_infer.tar">Inference Model</a></td>
 </tr>
 <tr>
 <td>PP-DocBee-7B</td>
 <td>7</td>
 <td>15.8</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_vlm/PP-DocBee-7B.yaml">PP-DocBee-7B.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee-7B_infer.tar">Inference Model</a></td>
 </tr>
 <tr>
 <td>PP-DocBee2-3B</td>
 <td>3</td>
 <td>7.6</td>
+<td></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee2-3B_infer.tar">Inference Model</a></td>
 </tr>
+</table>
+
+
+## [Chart Parsing Model Module](../module_usage/tutorials/vlm_modules/chart_parsing.en.md)
+
+<table>
+<tr>
+<th>Model</th>
+<th>Model Parameter Size（B）</th>
+<th>Model Storage Size（GB）</th>
+<th>yaml File</th>
+<th>Model Download Lin</th>
+</tr>
 <tr>
 <td>PP-Chart2Table</td>
 <td>0.58</td>
 <td>1.4</td>
+<td></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-Chart2Table_infer.tar">Inference Model</a></td>
 </tr>
 </table>

docs/support_list/models_list.md

@@ -1190,6 +1190,47 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>351.5 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/YOLOX-X.yaml">YOLOX-X.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/YOLOX-X_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-X_pretrained.pdparams">训练模型</a></td></tr>
+
+
+
+
+
+<tr>
+<td>Co-Deformable-DETR-R50</td>
+<td>49.7</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>184 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-Deformable-DETR-R50.yaml">Co-Deformable-DETR-R50.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-Deformable-DETR-R50_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-Deformable-DETR-R50_pretrained.pdparams">训练模型</a></td></tr>
+
+<tr>
+<td>Co-Deformable-DETR-Swin-T</td>
+<td>48.0</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>    187 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-Deformable-DETR-Swin-T.yaml">Co-Deformable-DETR-Swin-T.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-Deformable-DETR-Swin-T_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-Deformable-DETR-Swin-T_pretrained.pdparams">训练模型</a></td></tr>
+
+<tr>
+<td>Co-DINO-Swin-L</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>841 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-DINO-Swin-L.yaml">Co-DINO-Swin-L.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-DINO-Swin-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-DINO-Swin-L_pretrained.pdparams">训练模型</a></td></tr>
+
+<tr>
+<td>Co-DINO-R50</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>187 M </td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/object_detection/Co-DINO-R50.yaml">Co-DINO-R50.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/Co-DINO-R50_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Co-DINO-R50_pretrained.pdparams">训练模型</a></td></tr>
+
 </tbody>
 </table>
 <b>注：以上精度指标为 </b>[COCO2017](https://cocodataset.org/#home)<b> 验证集 mAP(0.5:0.95)。</b>
@@ -1245,6 +1286,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>模型存储大小（M）</th>
+<th>yaml 文件</th>
 <th>模型下载链接</th>
 </tr>
 <tr>
@@ -1254,6 +1296,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>253.72</td>
 <td>1807.4</td>
 <td>658.3</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_detection/GroundingDINO-T.yaml">GroundingDINO-T.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/GroundingDINO-T_infer.tar">推理模型</a></td>
 </tr>
 <tr>
@@ -1263,6 +1306,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>24.32</td>
 <td>374.89</td>
 <td>421.4</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_detection/YOLO-Worldv2-L.yaml">YOLO-Worldv2-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/YOLO-Worldv2-L_infer.tar">推理模型</a></td>
 </tr>
 </table>
@@ -1276,6 +1320,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>模型存储大小（M）</th>
+<th>yaml 文件</th>
 <th>模型下载链接</th>
 </tr>
 <tr>
@@ -1283,6 +1328,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>144.9</td>
 <td>33920.7</td>
 <td>2433.7</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_segmentation/SAM-H_box.yaml">SAM-H_box.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SAM-H_box_infer.tar">推理模型</a></td>
 </tr>
 <tr>
@@ -1290,6 +1336,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>144.9</td>
 <td>33920.7</td>
 <td>2433.7</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/open_vocabulary_segmentation/SAM-H_point.yaml">SAM-H_point.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SAM-H_point_infer.tar">推理模型</a></td>
 </tr>
 </table>
@@ -1313,7 +1360,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>20.7039</td>
 <td>157.942</td>
 <td>211.0 M</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/rotated_object_detection/PP-YOLOE-R-L.yamll">PP-YOLOE-R.yaml</a></td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/rotated_object_detection/PP-YOLOE-R-L.yaml">PP-YOLOE-R-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE-R-L_infer.tar">推理模型</a>/<a href="https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_l_3x_dota.pdparams">训练模型</a></td>
 </tr>
 </table>
@@ -1428,7 +1475,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>22.54 / 8.33</td>
 <td>138.67 / 138.67</td>
 <td>26.5 M</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/face_detection/PP-YOLOE_plus-S_face.yaml">PP-YOLOE_plus-S_face</a></td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/face_detection/PP-YOLOE_plus-S_face.yaml">PP-YOLOE_plus-S_face.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus-S_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-S_face_pretrained.pdparams">训练模型</a></td></tr>
 </tbody>
 </table>
@@ -1693,6 +1740,24 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>6.1M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/SeaFormer_tiny.yaml">SeaFormer_tiny.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SeaFormer_tiny (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_tiny (slice)_pretrained.pdparams">训练模型</a></td></tr>
+
+<tr>
+<td>MaskFormer_small</td>
+<td>49.70</td>
+<td>69.856</td>
+<td>-</td>
+<td>242.5 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/MaskFormer_small.yaml">MaskFormer_small.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MaskFormer_small_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_small_pretrained.pdparams">训练模型</a></td></tr>
+
+<tr>
+<td>MaskFormer_tiny</td>
+<td>46.69</td>
+<td>50.157</td>
+<td>-</td>
+<td>160.5 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/semantic_segmentation/MaskFormer_tiny.yaml">MaskFormer_tiny.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MaskFormer_tiny_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_tiny_pretrained.pdparams">训练模型</a></td></tr>
 </tbody>
 </table>
 <b>注：以上精度指标为 </b>[ADE20k](https://groups.csail.mit.edu/vision/datasets/ADE20K/)<b> 数据集, slice 表示对输入图像进行了切图操作。</b>
@@ -1850,6 +1915,24 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 </thead>
 <tbody>
 <tr>
+<td>PP-OCRv5_server_det</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>101</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv5_server_det.yaml">PP-OCRv5_server_det.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">训练模型</a></td>
+</tr>
+<tr>
+<td>PP-OCRv5_mobile_det</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>20</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml">PP-OCRv5_mobile_det.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">训练模型</a></td>
+</tr>
+<tr>
 <td>PP-OCRv4_server_det</td>
 <td>82.56</td>
 <td>83.34 / 80.91</td>
@@ -1936,6 +2019,24 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <th>模型下载链接</th>
 </tr>
 <tr>
+<td>PP-OCRv5_server_rec</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>206 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv5_server_rec.yaml">PP-OCRv5_server_rec.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+</tr>
+<tr>
+<td>PP-OCRv5_mobile_rec</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / -</td>
+<td>137 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv5_mobile_rec.yaml">PP-OCRv5_mobile_rec.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td>
+</tr>
+<tr>
 <td>PP-OCRv4_server_rec_doc</td>
 <td>81.53</td>
 <td>6.65 / 2.38</td>
@@ -2172,7 +2273,8 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 <table>
 <tr>
 <th>模型</th>
-<th>Avg-BLEU(%)</th>
+<th>En-BLEU(%)</th>
+<th>Zh-BLEU(%)</th>
 <th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>模型存储大小 (M)</th>
@@ -2180,34 +2282,68 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 <th>模型下载链接</th>
 </tr>
 <td>UniMERNet</td>
-<td>86.13</td>
+<td>85.91</td>
+<td>43.50</td>
 <td>2266.96/-</td>
 <td>-/-</td>
-<td>1.4 G</td>
+<td>1.53 G</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/UniMERNet.yaml">UniMERNet.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">训练模型</a></td>
 <tr>
 <td>PP-FormulaNet-S</td>
-<td>87.12</td>
+<td>87.00</td>
+<td>45.71</td>
 <td>202.25/-</td>
 <td>-/-</td>
-<td>167.9 M</td>
+<td>224 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml">PP-FormulaNet-S.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">训练模型</a></td>
 </tr>
 <td>PP-FormulaNet-L</td>
-<td>92.13</td>
+<td>90.36</td>
+<td>45.78</td>
 <td>1976.52/-</td>
 <td>-/-</td>
-<td>535.2 M</td>
+<td>695 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet-L.yaml">PP-FormulaNet-L.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">训练模型</a></td>
 <tr>
-<td>LaTeX_OCR_rec</td>
-<td>71.63</td>
+<td>PP-FormulaNet_plus-S</td>
+<td>88.71</td>
+<td>53.32</td>
+<td>191.69/-</td>
 <td>-/-</td>
+<td>248 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-S.yaml">PP-FormulaNet_plus-S.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-S_pretrained.pdparams">训练模型</a></td>
+</tr>
+<tr>
+<td>PP-FormulaNet_plus-M</td>
+<td>91.45</td>
+<td>89.76</td>
+<td>1301.56/-</td>
+<td>-/-</td>
+<td>592 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml">PP-FormulaNet_plus-M.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-M_pretrained.pdparams">训练模型</a></td>
+</tr>
+<tr>
+<td>PP-FormulaNet_plus-L</td>
+<td>92.22</td>
+<td>90.64</td>
+<td>1745.25/-</td>
+<td>-/-</td>
+<td>698 M</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-L.yaml">PP-FormulaNet_plus-L.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-L_pretrained.pdparams">训练模型</a></td>
+</tr>
+<tr>
+<td>LaTeX_OCR_rec</td>
+<td>74.55</td>
+<td>39.96</td>
+<td>1244.61/-</td>
 <td>-/-</td>
-<td>89.7 M</td>
+<td>99 M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/LaTeX_OCR_rec.yaml">LaTeX_OCR_rec.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">训练模型</a></td>
 </tr>
@@ -2266,25 +2402,27 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 
 <table>
 <tr>
-<th>模型</th><th>模型下载链接</th>
+<th>模型</th>
 <th>mAP(%)</th>
 <th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
 <th>模型存储大小 (M)</th>
-<th>介绍</th>
+<th>yaml 文件</th>
+<th>模型下载链接</th>
 </tr>
 <tr>
 <td>RT-DETR-L_wired_table_cell_det</td>
-<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wired_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">训练模型</a></td>
+
 <td rowspan="2">82.7</td>
 <td rowspan="2">35.00 / 10.45</td>
 <td rowspan="2">495.51 / 495.51</td>
 <td rowspan="2">124M</td>
-<td rowspan="2">RT-DETR 是第一个实时的端到端目标检测模型。百度飞桨视觉团队基于 RT-DETR-L 作为基础模型，在自建表格单元格检测数据集上完成预训练，实现了对有线表格、无线表格均有较好性能的表格单元格检测。
-</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml">RT-DETR-L_wired_table_cell_det.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wired_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">训练模型</a></td>
 </tr>
 <tr>
 <td>RT-DETR-L_wireless_table_cell_det</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_cells_detection/RT-DETR-L_wireless_table_cell_det.yaml">RT-DETR-L_wireless_table_cell_det.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wireless_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wireless_table_cell_det_pretrained.pdparams">训练模型</a></td>
 </tr>
 </table>
@@ -2304,10 +2442,10 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 </tr>
 <tr>
 <td>PP-LCNet_x1_0_table_cls</td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
-<td>--</td>
+<td>94.2</td>
+<td>2.35 / 0.47</td>
+<td>4.03 / 1.35</td>
+<td>6.6M</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_classification/PP-LCNet_x1_0_table_cls.yaml">PP-LCNet_x1_0_table_cls.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/CLIP_vit_base_patch16_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">训练模型</a></td>
 </tr>
@@ -2369,6 +2507,35 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 
 <b>注：以上精度指标的评估集是自建的版面区域检测数据集，包含中英文论文、杂志、报纸、研报、PPT、试卷、课本等 1300 张文档类型图片。</b>
 
+* <b>版面检测模型，包含20个常见的类别：文档标题、段落标题、文本、页码、摘要、目录、参考文献、脚注、页眉、页脚、算法、公式、公式编号、图像、表格、图和表标题（图标题、表格标题和图表标题）、印章、图表、侧栏文本和参考文献内容</b>
+<table>
+<thead>
+<tr>
+<th>模型</th>
+<th>mAP(0.5)（%）</th>
+<th>GPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
+<th>CPU推理耗时（ms）<br/>[常规模式 / 高性能模式]</th>
+<th>模型存储大小（M）</th>
+<th>yaml文件</th>
+<th>模型下载链接</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-DocBlockLayout</td>
+<td>-</td>
+<td>- / -</td>
+<td>- / - </td>
+<td>123.92 </td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocBlockLayout.yaml">PP-DocBlockLayout.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBlockLayout_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocBlockLayout_pretrained.pdparams">训练模型</a></td>
+</tr>
+</tbody>
+</table>
+
+<b>注：以上精度指标的评估集是自建的版面区域检测数据集，包含中英文论文、杂志、报纸、研报、PPT、试卷、课本等 1300 张文档类型图片。</b>
+
+
 * <b>版面检测模型，包含23个常见的类别：文档标题、段落标题、文本、页码、摘要、目录、参考文献、脚注、页眉、页脚、算法、公式、公式编号、图像、图表标题、表格、表格标题、印章、图表标题、图表、页眉图像、页脚图像、侧栏文本</b>
 <table>
 <thead>
@@ -2726,6 +2893,16 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 <td>320 K</td>
 <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/ts_anomaly_detection/PatchTST_ad.yaml">PatchTST_ad.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PatchTST_ad_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PatchTST_ad_pretrained.pdparams">训练模型</a></td></tr>
+
+<tr>
+<td>TimesNet_ad</td>
+<td>-</td>
+<td>-</td>
+<td>-</td>
+<td>1000 K</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/ts_anomaly_detection/TimesNet_ad.yaml">TimesNet_ad.yaml</a></td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/TimesNet_ad_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/TimesNet_ad_pretrained.pdparams">训练模型</a></td></tr>
+
 </tbody>
 </table>
 <b>注：以上精度指标测量自 </b>[PSM](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ts_anomaly_examples.tar)<b> 数据集。</b>
@@ -2796,11 +2973,11 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/whisper_base.tar">推理模型</a></td>
 </tr>
 <tr>
-<td>whisper_small</td>
+<td>whisper_tiny</td>
 <td>680kh</td>
 <td>145M</td>
 <td>-</td>
-<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/multilingual_speech_recognition/whisper_small.yaml">whisper_small.yaml</a></td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/multilingual_speech_recognition/whisper_tiny.yaml">whisper_tiny.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/whisper_tiny.tar">推理模型</a></td>
 </tr>
 </table>
@@ -2866,30 +3043,47 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 <th>模型</th>
 <th>模型参数尺寸（B）</th>
 <th>模型存储大小（GB）</th>
+<th>yaml文件</th>
 <th>模型下载链接</th>
 </tr>
 <tr>
 <td>PP-DocBee-2B</td>
 <td>2</td>
 <td>4.2</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_vlm/PP-DocBee-2B.yaml">PP-DocBee-2B.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee-2B_infer.tar">推理模型</a></td>
 </tr>
 <tr>
 <td>PP-DocBee-7B</td>
 <td>7</td>
 <td>15.8</td>
+<td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_vlm/PP-DocBee-7B.yaml">PP-DocBee-7B.yaml</a></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee-7B_infer.tar">推理模型</a></td>
 </tr>
 <tr>
 <td>PP-DocBee2-3B</td>
 <td>3</td>
 <td>7.6</td>
+<td></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee2-3B_infer.tar">推理模型</a></td>
 </tr>
+</table>
+
+## [图表解析模型模块](../module_usage/tutorials/vlm_modules/chart_parsing.md)
+
+<table>
+<tr>
+<th>模型</th>
+<th>模型参数尺寸（B）</th>
+<th>模型存储大小（GB）</th>
+<th>yaml文件</th>
+<th>模型下载链接</th>
+</tr>
 <tr>
 <td>PP-Chart2Table</td>
 <td>0.58</td>
 <td>1.4</td>
+<td></td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-Chart2Table_infer.tar">推理模型</a></td>
 </tr>
 </table>

paddlex/configs/pipelines/formula_recognition.yaml

@@ -7,7 +7,7 @@ use_doc_preprocessor: True
 SubModules:
   LayoutDetection:
     module_name: layout_detection
-    model_name: PP-DocLayout-L
+    model_name: PP-DocLayout_plus-L
     model_dir: null
     threshold: 0.5
     layout_nms: True
@@ -17,7 +17,7 @@ SubModules:
 
   FormulaRecognition:
     module_name: formula_recognition
-    model_name: PP-FormulaNet_plus-L
+    model_name: PP-FormulaNet_plus-M
     model_dir: null
     batch_size: 5
 

paddlex/inference/models/formula_recognition/processors.py

@@ -882,15 +882,6 @@ class UniMERNetDecode(object):
         replaced_formula = pattern.sub(replacer, formula)
         return replaced_formula.replace('"', "")
 
-    def remove_chinese_text_wrapping(self, formula):
-        pattern = re.compile(r"\\text\s*{\s*([^}]*?[\u4e00-\u9fff]+[^}]*?)\s*}")
-
-        def replacer(match):
-            return match.group(1)
-
-        replaced_formula = pattern.sub(replacer, formula)
-        return replaced_formula.replace('"', "")
-
     def post_process(self, text: str) -> str:
         """Post-processes a string by fixing text and normalizing it.