[Doc]Add English version of documents in examples

examples/vision/README.md

@@ -1,33 +1,36 @@
-# 视觉模型部署
+English | [简体中文](README_CN.md) 
 
-本目录下提供了各类视觉模型的部署，主要涵盖以下任务类型
+# Visual Model Deployment
 
-| 任务类型           | 说明                                  | 预测结果结构体                                                                          |
+This directory provides the deployment of various visual models, including the following task types
+
+| Task Type           |  Description                               | Predicted Structure                                                                         |
 |:-------------- |:----------------------------------- |:-------------------------------------------------------------------------------- |
-| Detection      | 目标检测，输入图像，检测图像中物体位置，并返回检测框坐标及类别和置信度 | [DetectionResult](../../docs/api/vision_results/detection_result.md)       |
-| Segmentation   | 语义分割，输入图像，给出图像中每个像素的分类及置信度          | [SegmentationResult](../../docs/api/vision_results/segmentation_result.md) |
-| Classification | 图像分类，输入图像，给出图像的分类结果和置信度             | [ClassifyResult](../../docs/api/vision_results/classification_result.md)   |
-| FaceDetection | 人脸检测，输入图像，检测图像中人脸位置，并返回检测框坐标及人脸关键点             | [FaceDetectionResult](../../docs/api/vision_results/face_detection_result.md)   |
-| FaceAlignment |  人脸对齐(人脸关键点检测)，输入图像，返回人脸关键点            | [FaceAlignmentResult](../../docs/api/vision_results/face_alignment_result.md)   |
-| KeypointDetection   | 关键点检测，输入图像，返回图像中人物行为的各个关键点坐标和置信度         | [KeyPointDetectionResult](../../docs/api/vision_results/keypointdetection_result.md) |
-| FaceRecognition | 人脸识别，输入图像，返回可用于相似度计算的人脸特征的embedding            | [FaceRecognitionResult](../../docs/api/vision_results/face_recognition_result.md)   |
-| Matting | 抠图，输入图像，返回图片的前景每个像素点的Alpha值            | [MattingResult](../../docs/api/vision_results/matting_result.md)   |
-| OCR | 文本框检测，分类，文本框内容识别，输入图像，返回文本框坐标，文本框的方向类别以及框内的文本内容            | [OCRResult](../../docs/api/vision_results/ocr_result.md)   |
-| MOT | 多目标跟踪，输入图像，检测图像中物体位置，并返回检测框坐标，对象id及类别置信度        | [MOTResult](../../docs/api/vision_results/mot_result.md)   |
-| HeadPose | 头部姿态估计，返回头部欧拉角            | [HeadPoseResult](../../docs/api/vision_results/headpose_result.md)   |
+| Detection      | Target detection. Input the image, detect the object’s position in the image, and return the detected box coordinates, category, and confidence coefficient | [DetectionResult](../../docs/api/vision_results/detection_result.md)       |
+| Segmentation   | Semantic segmentation. Input the image and output the classification and confidence coefficient of each pixel         | [SegmentationResult](../../docs/api/vision_results/segmentation_result.md) |
+| Classification | Image classification. Input the image and output the classification result and confidence coefficient of the image             | [ClassifyResult](../../docs/api/vision_results/classification_result.md)   |
+| FaceDetection | Face detection. Input the image, detect the position of faces in the image, and return detected box coordinates and key points of faces            | [FaceDetectionResult](../../docs/api/vision_results/face_detection_result.md)   |
+| FaceAlignment |  Face alignment(key points detection).Input the image and return face key points           | [FaceAlignmentResult](../../docs/api/vision_results/face_alignment_result.md)   |
+| KeypointDetection   | Key point detection. Input the image and return the coordinates and confidence coefficient of the key points of the person's behavior in the image         | [KeyPointDetectionResult](../../docs/api/vision_results/keypointdetection_result.md) |
+| FaceRecognition | Face recognition. Input the image and return an embedding of facial features that can be used for similarity calculation            | [FaceRecognitionResult](../../docs/api/vision_results/face_recognition_result.md)   |
+| Matting | Matting. Input the image and return the Alpha value of each pixel in the foreground of the image           | [MattingResult](../../docs/api/vision_results/matting_result.md)   |
+| OCR | Text box detection, classification, and text box content recognition. Input the image and return the text box’s coordinates, orientation category, and content         | [OCRResult](../../docs/api/vision_results/ocr_result.md)   |
+| MOT | Multi-objective tracking. Input the image and detect the position of objects in the image, and return detected box coordinates, object id, and class confidence        | [MOTResult](../../docs/api/vision_results/mot_result.md)   |
+| HeadPose | Head posture estimation. Return head Euler angle            | [HeadPoseResult](../../docs/api/vision_results/headpose_result.md)   |
+
+## FastDeploy API Design
 
-## FastDeploy API设计
+Generally, visual models have a uniform task paradigm. When designing API (including C++/Python), FastDeploy conducts four steps to deploy visual models
 
-视觉模型具有较有统一任务范式，在设计API时（包括C++/Python），FastDeploy将视觉模型的部署拆分为四个步骤
+- Model loading
+- Image pre-processing
+- Model Inference
+- Post-processing of inference results
 
-- 模型加载
-- 图像预处理
-- 模型推理
-- 推理结果后处理
+Targeted at the vision suite of PaddlePaddle and external popular models, FastDeploy provides an end-to-end deployment service. Users merely prepare the model and follow these steps to complete the deployment
 
-FastDeploy针对飞桨的视觉套件，以及外部热门模型，提供端到端的部署服务，用户只需准备模型，按以下步骤即可完成整个模型的部署
+- Model Loading
+- Calling the `predict`interface
 
-- 加载模型
-- 调用`predict`接口
+When deploying visual models, FastDeploy supports one-click switching of the backend inference engine. Please refer to [How to switch model inference engine](../../docs/cn/faq/how_to_change_backend.md).
 
-FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/cn/faq/how_to_change_backend.md)。

examples/vision/README_CN.md

@@ -0,0 +1,34 @@
+[English](README_EN.md) | 简体中文
+# 视觉模型部署
+
+本目录下提供了各类视觉模型的部署，主要涵盖以下任务类型
+
+| 任务类型           | 说明                                  | 预测结果结构体                                                                          |
+|:-------------- |:----------------------------------- |:-------------------------------------------------------------------------------- |
+| Detection      | 目标检测，输入图像，检测图像中物体位置，并返回检测框坐标及类别和置信度 | [DetectionResult](../../docs/api/vision_results/detection_result.md)       |
+| Segmentation   | 语义分割，输入图像，给出图像中每个像素的分类及置信度          | [SegmentationResult](../../docs/api/vision_results/segmentation_result.md) |
+| Classification | 图像分类，输入图像，给出图像的分类结果和置信度             | [ClassifyResult](../../docs/api/vision_results/classification_result.md)   |
+| FaceDetection | 人脸检测，输入图像，检测图像中人脸位置，并返回检测框坐标及人脸关键点             | [FaceDetectionResult](../../docs/api/vision_results/face_detection_result.md)   |
+| FaceAlignment |  人脸对齐(人脸关键点检测)，输入图像，返回人脸关键点            | [FaceAlignmentResult](../../docs/api/vision_results/face_alignment_result.md)   |
+| KeypointDetection   | 关键点检测，输入图像，返回图像中人物行为的各个关键点坐标和置信度         | [KeyPointDetectionResult](../../docs/api/vision_results/keypointdetection_result.md) |
+| FaceRecognition | 人脸识别，输入图像，返回可用于相似度计算的人脸特征的embedding            | [FaceRecognitionResult](../../docs/api/vision_results/face_recognition_result.md)   |
+| Matting | 抠图，输入图像，返回图片的前景每个像素点的Alpha值            | [MattingResult](../../docs/api/vision_results/matting_result.md)   |
+| OCR | 文本框检测，分类，文本框内容识别，输入图像，返回文本框坐标，文本框的方向类别以及框内的文本内容            | [OCRResult](../../docs/api/vision_results/ocr_result.md)   |
+| MOT | 多目标跟踪，输入图像，检测图像中物体位置，并返回检测框坐标，对象id及类别置信度        | [MOTResult](../../docs/api/vision_results/mot_result.md)   |
+| HeadPose | 头部姿态估计，返回头部欧拉角            | [HeadPoseResult](../../docs/api/vision_results/headpose_result.md)   |
+
+## FastDeploy API设计
+
+视觉模型具有较有统一任务范式，在设计API时（包括C++/Python），FastDeploy将视觉模型的部署拆分为四个步骤
+
+- 模型加载
+- 图像预处理
+- 模型推理
+- 推理结果后处理
+
+FastDeploy针对飞桨的视觉套件，以及外部热门模型，提供端到端的部署服务，用户只需准备模型，按以下步骤即可完成整个模型的部署
+
+- 加载模型
+- 调用`predict`接口
+
+FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/cn/faq/how_to_change_backend.md)。

examples/vision/classification/paddleclas/README.md

@@ -1,34 +1,36 @@
-# PaddleClas 模型部署
+English | [简体中文](README_CN.md)
 
-## 模型版本说明
+# PaddleClas Model Deployment
+
+## Model Description
 
 - [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
 
-目前FastDeploy支持如下模型的部署
+Now FastDeploy supports the deployment of the following models
 
-- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
-- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
-- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
-- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
-- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
-- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
-- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
-- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
-- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
-- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
+- [PP-LCNet Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
+- [PP-LCNetV2 Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
+- [EfficientNet Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
+- [GhostNet Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [MobileNet Models(including v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [ShuffleNet Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [SqueezeNet Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
+- [Inception Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
+- [PP-HGNet Models](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
+- [ResNet Models（including vd series）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
 
-## 准备PaddleClas部署模型
+## Prepare PaddleClas Deployment Model
 
-PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
+For PaddleClas model export, refer to [Model Export](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
 
-注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
+Attention：The model exported by PaddleClas contains two files, including `inference.pdmodel` and `inference.pdiparams`. However, it is necessary to prepare the generic [inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml) file provided by PaddleClas to meet the requirements of deployment. FastDeploy will obtain from the yaml file the preprocessing information required during inference. FastDeploy will get the preprocessing information needed by the model from the yaml file. Developers can directly download this file. But they need to modify the configuration parameters in the yaml file based on personalized needs. Refer to the configuration information in the infer section of the PaddleClas model training [config.](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)
 
 
-## 下载预训练模型
+## Download Pre-trained Model
 
-为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
+For developers' testing, some models exported by PaddleClas (including the inference_cls.yaml file) are provided below. Developers can download them directly.
 
-| 模型                                                               | 参数文件大小    |输入Shape |  Top1 | Top5 |
+| Model                                                               | Parameter File Size    |Input Shape |  Top1 | Top5 |
 |:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
 | [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
 | [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz)  | 26MB  | 224x224 |77.04% | 93.27% |
@@ -50,8 +52,8 @@ PaddleClas模型导出，请参考其文档说明[模型导出](https://github.c
 | [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) |  274MB | 224x224 | 85.0% | 97.35% |
 | [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) |  98MB | 224x224 | 79.12% | 94.44% |
 
-## 详细部署文档
+## Detailed Deployment Documents
 
-- [Python部署](python)
-- [C++部署](cpp)
-- [服务化部署](serving)
+- [Python Deployment](python)
+- [C++ Deployment](cpp)
+- [Serving Deployment](serving)