Merge branch 'release/3.0-beta1' into develop

README.md

@@ -56,7 +56,9 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
 
  ## 📊 能力支持
 
-PaddleX的各个产线均支持本地**快速推理**，部分模型支持**在线体验**，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能部署](./docs/pipeline_deploy/high_performance_inference.md)/[服务化部署](./docs/pipeline_deploy/service_deploy.md)/[端侧部署](./docs/pipeline_deploy/lite_deploy.md)，如果不满意，您也可以使用产线的**二次开发**能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](./docs/pipeline_usage/pipeline_develop_guide.md)或各产线使用[教程](#-文档)。
+
+PaddleX的各个产线均支持本地**快速推理**，部分模型支持**在线体验**，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能推理](./docs/pipeline_deploy/high_performance_deploy.md)/[服务化部署](./docs/pipeline_deploy/service_deploy.md)/[端侧部署](./docs/pipeline_deploy/lite_deploy.md)，如果不满意，您也可以使用产线的**二次开发**能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](./docs/pipeline_usage/pipeline_develop_guide.md)或各产线使用[教程](#-文档)。
+
 
 此外，PaddleX 为开发者提供了基于[云端图形化开发界面](https://aistudio.baidu.com/pipeline/mine)的全流程开发工具, 点击【创建产线】，选择对应的任务场景和模型产线，就可以开启全流程开发。详细请参考[教程《零门槛开发产业级AI模型》](https://aistudio.baidu.com/practical/introduce/546656605663301)
 

README_en.md

@@ -21,6 +21,7 @@
 
 PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePaddle framework. It integrates numerous **ready-to-use pre-trained models**, enabling **full-process development** from model training to inference, supporting **a variety of mainstream hardware** both domestic and international, and aiding AI developers in industrial practice.
  
+
 |                                                            [**Image Classification**](./docs/pipeline_usage/tutorials/cv_pipelines/image_classification_en.md)                                                            |                                                            [**Multi-label Image Classification**](./docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification_en.md)                                                            |                                                            [**Object Detection**](./docs/pipeline_usage/tutorials/cv_pipelines/object_detection_en.md)                                                            |                                                            [**Instance Segmentation**](./docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation_en.md)                                                            |
 |:--------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|
 | <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/b302cd7e-e027-4ea6-86d0-8a4dd6d61f39" height="126px" width="180px"> | <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/multilabel_cls.png" height="126px" width="180px"> | <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/099e2b00-0bbe-4b20-9c5a-96b69e473bd2" height="126px" width="180px"> | <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/09f683b4-27df-4c24-b8a7-84da20fdd182" height="126px" width="180px"> |
@@ -54,6 +55,7 @@ PaddleX is dedicated to achieving pipeline-level model training, inference, and
 
 ## 📊 What can PaddleX do？
 
+
 All pipelines of PaddleX support **online experience** and local **fast inference**. You can quickly experience the effects of each pre-trained pipeline. If you are satisfied with the effects of the pre-trained pipeline, you can directly perform [high-performance inference](./docs/pipeline_deploy/high_performance_inference_en.md) / [serving deployment](./docs/pipeline_deploy/service_deploy_en.md) / [edge deployment](./docs/pipeline_deploy/lite_deploy_en.md) on the pipeline. If not satisfied, you can also **Custom Development** to improve the pipeline effect. For the complete pipeline development process, please refer to the [PaddleX pipeline Development Tool Local Use Tutorial](./docs/pipeline_usage/pipeline_develop_guide_en.md).
 
 In addition, PaddleX provides developers with a full-process efficient model training and deployment tool based on a [cloud-based GUI](https://aistudio.baidu.com/pipeline/mine). Developers **do not need code development**, just need to prepare a dataset that meets the pipeline requirements to **quickly start model training**. For details, please refer to the tutorial ["Developing Industrial-level AI Models with Zero Barrier"](https://aistudio.baidu.com/practical/introduce/546656605663301).

docs/installation/installation.md

@@ -176,4 +176,4 @@ paddlex --install --platform gitee.com
 ```
 All packages are installed.
 ```
-更多硬件环境的PaddleX安装请参考[PaddleX多硬件使用指南](../other_devices_support/multi_devices_use_guide.md)
+更多硬件环境的PaddleX安装请参考[PaddleX多硬件使用指南](../other_devices_support/multi_devices_use_guide.md)

docs/installation/installation_en.md

@@ -175,4 +175,4 @@ After installation, you will see the following prompt:
 All packages are installed.
 ```
 
-For PaddleX installation on more hardware environments, please refer to the [PaddleX Multi-hardware Usage Guide](../other_devices_support/multi_devices_use_guide_en.md)
+For PaddleX installation on more hardware environments, please refer to the [PaddleX Multi-hardware Usage Guide](../other_devices_support/multi_devices_use_guide_en.md)

docs/pipeline_usage/pipeline_develop_guide.md

@@ -182,7 +182,8 @@ Pipeline:
 
 此外，PaddleX 也提供了其他三种部署方式，详细说明如下：
 
-🚀 **高性能部署**：在实际生产环境中，许多应用对部署策略的性能指标（尤其是响应速度）有着较严苛的标准，以确保系统的高效运行与用户体验的流畅性。为此，PaddleX 提供高性能推理插件，旨在对模型推理及前后处理进行深度性能优化，实现端到端流程的显著提速，详细的高性能部署流程请参考[PaddleX高性能部署指南](../pipeline_deploy/high_performance_inference.md)。
+
+🚀 **高性能推理**：在实际生产环境中，许多应用对部署策略的性能指标（尤其是响应速度）有着较严苛的标准，以确保系统的高效运行与用户体验的流畅性。为此，PaddleX 提供高性能推理插件，旨在对模型推理及前后处理进行深度性能优化，实现端到端流程的显著提速，详细的高性能部署流程请参考[PaddleX高性能部署指南](../pipeline_deploy/high_performance_deploy.md)。
 
 ☁️ **服务化部署**：服务化部署是实际生产环境中常见的一种部署形式。通过将推理功能封装为服务，客户端可以通过网络请求来访问这些服务，以获取推理结果。PaddleX 支持用户以低成本实现产线的服务化部署，详细的服务化部署流程请参考[PaddleX服务化部署指南](../pipeline_deploy/service_deploy.md)。
 

docs/pipeline_usage/pipeline_develop_guide_en.md

@@ -178,6 +178,7 @@ If you need to apply the pipeline directly in your Python project, you can refer
 In addition, PaddleX also provides three other deployment methods, with detailed instructions as follows:
 
 
+
 🚀 **high-performance inference**: In actual production environments, many applications have stringent standards for the performance metrics (especially response speed) of deployment strategies to ensure efficient system operation and smooth user experience. To this end, PaddleX provides high-performance inference plugins that aim to deeply optimize model inference and pre/post-processing for significant speedups in the end-to-end process. Refer to the [PaddleX High-Performance Inference Guide](../pipeline_deploy/high_performance_inference_en.md) for detailed high-performance inference procedures.
 
 ☁️ **Service-Oriented Deployment**: Service-oriented deployment is a common deployment form in actual production environments. By encapsulating inference functions as services, clients can access these services through network requests to obtain inference results. PaddleX supports users in achieving low-cost service-oriented deployment of pipelines. Refer to the [PaddleX Service-Oriented Deployment Guide](../pipeline_deploy/service_deploy_en.md) for detailed service-oriented deployment procedures.

docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md

@@ -652,7 +652,7 @@ if __name__ == "__main__":
     print("Final result:")
     print(len(result_chat["chatResult"]))
 ```
-
+  
 **Note**: Please fill in your API key and secret key at `API_KEY` and `SECRET_KEY`.
 
 </details>
@@ -721,3 +721,4 @@ pipeline = create_pipeline(
 ```
 
 If you want to use the PP-ChatOCRv3-doc Pipeline on more types of hardware, please refer to the [PaddleX Multi-Device Usage Guide](../../../installation/multi_devices_use_guide_en.md).
+