cp CHANGELOG (#2575)

* Update README_en.md

* Update CHANGLOG.md

* Update CHANGLOG.en.md

Author: cuicheng01

README_en.md

@@ -48,6 +48,8 @@ PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePadd
 
 > [❗ Get more courses for free](https://aistudio.baidu.com/education/group/info/32160)
 
+🔥🔥 **11.15, 2024**, PaddleX 3.0 Beta2 open source version is officially released, PaddleX 3.0 Beta2 is fully compatible with the PaddlePaddle 3.0b2 version. <b>This update introduces new pipelines for general image recognition, face recognition, vehicle attribute recognition, and pedestrian attribute recognition. We have also developed 42 new models to fully support the Ascend 910B, with extensive documentation available on [GitHub Pages](https://paddlepaddle.github.io/PaddleX/latest/en/index.html).</b>
+
 🔥🔥 **9.30, 2024**, PaddleX 3.0 Beta1 open source version is officially released, providing **more than 200 models** that can be called with a simple Python API; achieve model full-process development based on unified commands, and open source the basic capabilities of the **PP-ChatOCRv3** pipeline; support **more than 100 models for high-performance inference and service-oriented deployment** (iterating continuously), **more than 7 key visual models for edge-deployment**; **more than 70 models have been adapted for the full development process of Ascend 910B**, **more than 15 models have been adapted for the full development process of Kunlun chips and Cambricon**
 
 🔥 **6.27, 2024**, PaddleX 3.0 Beta open source version is officially released, supporting the use of various mainstream hardware for pipeline and model development in a low-code manner on the local side.

docs/CHANGLOG.en.md

@@ -5,6 +5,36 @@ comments: true
 # Version Update Information
 
 ## Latest Version Information
+### PaddleX v3.0.0beta2 (11.15/2024)
+
+PaddleX 3.0 Beta2 is fully compatible with the PaddlePaddle 3.0b2 version. <b>This update introduces new pipelines for general image recognition, face recognition, vehicle attribute recognition, and pedestrian attribute recognition. We have also developed 42 new models to fully support the Ascend 910B, with extensive documentation available on [GitHub Pages](https://paddlepaddle.github.io/PaddleX/latest/en/index.html).</b> Here’s a detailed look at the new features and improvements:
+
+- <b>New Pipelines:</b>
+  - <b>[General Image Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.en.md):</b> Introducing a powerful pipeline with enhanced feature extraction models. This allows for user-defined image database recognition of unknown categories, providing more customizable recognition options compared to existing open-domain object detection. Supports high-performance inference and service deployment.
+  - <b>[Face Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.en.md):</b> This new pipeline enables the addition and removal of entries in the face database, with robust support for high-performance inference and service deployment.
+  - <b>[Vehicle Attribute Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.md):</b> Detect and recognize vehicle attributes such as color and model in images. Supports high-performance inference and service deployment.
+  - <b>[Pedestrian Attribute Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.en.md):</b> Detect and recognize pedestrian attributes such as age, gender, and clothing in images. Supports high-performance inference and service deployment.
+
+- <b>New Capabilities:</b>
+  - <b>Documentation Support:</b> Comprehensive [GitHub Pages](https://paddlepaddle.github.io/PaddleX/latest/en/index.html) is available, featuring search functionality and user commentary.
+  - <b>Benchmarking:</b> Print model inference benchmark information with related [documentation](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/module_usage/instructions/benchmark.en.md).
+  - <b>Model Development:</b> We have introduced 42 new model development processes specifically adapted for Ascend 910B. Check out the [model list](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/support_list/model_list_npu.en.md).
+
+- <b>Optimizations:</b>
+  - <b>Formula Recognition:</b> Now supports PDF input and visualization of results.
+  - <b>Seal Recognition:</b> Supports PDF format input.
+  - <b>Layout Parsing:</b> Improved the naming convention for saved images.
+  - <b>Pretrained Models:</b> Unified management of pretrained models, integrated into the default configuration file.
+  - <b>Model Saving:</b> Upgraded format to ensure high-performance inference.
+  - <b>Parameter Tuning:</b> Optimized default parameters for certain models to enable higher accuracy training.
+
+- <b>Bug Fixes:</b>
+  - Addressed inaccuracies and inappropriate expressions in documentation, and fixed some invalid URLs.
+  - Resolved issues with the PP-LCNet_x1_0_doc_ori inference model.
+  - Fixed bugs related to high-performance inference and service deployment.
+  - Corrected training configuration issues in SLANet and SLANet_plus that led to low accuracy.
+
+
 ### PaddleX v3.0.0beta1 (9.30/2024)
 PaddleX 3.0 Beta1 offers over 200 models accessible through a streamlined Python API for one-click deployment; realizes full-process model development based on unified commands, and opens source the foundational capabilities of the PP-ChatOCRv3 special model pipeline; supports high-performance inference and service-oriented deployment for over 100 models, as well as edge deployment for 7 key vision models; and fully adapts the development process of over 70 models to Huawei Ascend 910B, and over 15 models to XPU and MLU.
 

docs/CHANGLOG.md

@@ -5,6 +5,34 @@ comments: true
 # 版本更新信息
 
 ## 最新版本信息
+### PaddleX v3.0.0beta2(11.15/2024)
+PaddleX 3.0 Beta2 全面适配 PaddlePaddle 3.0b2 版本。**新增通用图像识别、人脸识别、车辆属性识别和行人属性识别产线，同时新增 42 个模型开发全流程适配昇腾 910B，并全面支持[GitHub 站点文档](https://paddlepaddle.github.io/PaddleX/latest/index.html)。** 具体新增能力如下：
+
+- 新增产线：
+  - 新增[通用图像识别产线](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.md)，提供更强的特征提取模型，支持用户自定义图像数据库识别未知类别，相比当前开放域目标检测，可以自定义识别的总类更多。支持高性能推理和服务化部署；
+  - 新增[人脸识别产线](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md)，支持对人脸数据库的增加和删除，支持高性能推理和服务化部署；
+  - 新增[车辆属性识别产线](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.md)，支持对图像中的车辆进行检测和属性的识别，当前支持的属性有颜色和车型。支持高性能推理和服务化部署；
+  - 新增[行人属性识别线](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.md)，支持对图像中的行人进行检测和属性的识别，当前支持的属性有年龄、性别、穿着等。支持高性能推理和服务化部署。
+
+- 新增能力：
+  - 支持[GitHub 站点文档](https://paddlepaddle.github.io/PaddleX/latest/index.html)，支持用户搜索相关内容和对文档内容的评论；
+  - 支持打印模型的推理benchmark信息，相关[文档](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/module_usage/instructions/benchmark.md)；
+  - 新增 42 个模型开发全流程适配昇腾 910B，[模型列表](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/support_list/model_list_npu.md)。
+
+- 优化点：
+  - 公式识别产线，支持 PDF 格式输入，支持公式识别结果的可视化；
+  - 印章文本识别产线，支持 PDF 格式的输入；
+  - 通用版面解析产线，优化保存图片的名称；
+  - 预训练统一管理，将当前支持的模型的预训练统一管理，并内置到默认配置文件中；
+  - 升级模型保存的格式，为高性能推理提供保障；
+  - 优化部分模型的默认参数，为训练更高精度的模型提供保障。
+
+- BugFix：
+  - 修复部分文档表述错误或者不当的内容，修复部分 URL 失效的问题；
+  - 修复文档方向分类推理模型的bug；
+  - 修复部分高性能推理和服务化部署的bug；
+  - 修复 SLANet、SLANet_plus 训练精度为 0 的bug。
+
 ### PaddleX v3.0.0beta1(9.30/2024)
 PaddleX 3.0 Beta1 提供 200+ 模型通过极简的 Python API 一键调用；实现基于统一命令的模型全流程开发，并开源 PP-ChatOCRv3 特色模型产线基础能力；支持 100+ 模型高性能推理和服务化部署，7 类重点视觉模型端侧部署；70+ 模型开发全流程适配昇腾 910B，15+ 模型开发全流程适配昆仑芯和寒武纪。