[Doc]Update English version of some documents (#1083)

* 第一次提交

* 补充一处漏翻译

* deleted:    docs/en/quantize.md

* Update one translation

* Update en version

* Update one translation in code

* Standardize one writing

* Standardize one writing

* Update some en version

* Fix a grammer problem

* Update en version for api/vision result

* Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop

* Checkout the link in README in vision_results/ to the en documents

* Modify a title

* Add link to serving/docs/

* Finish translation of demo.md

* Update english version of serving/docs/

* Update title of readme

* Update some links

* Modify a title

* Update some links

* Update en version of java android README

* Modify some titles

* Modify some titles

* Modify some titles

* modify article to document

* update some english version of documents in examples

* Add english version of documents in examples/visions

* Sync to current branch

* Add english version of documents in examples

* Add english version of documents in examples

* Add english version of documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

examples/vision/detection/yolov5/quantize/python/README.md

@@ -1,31 +1,32 @@
+English | [简体中文](README_CN.md)
 # YOLOv5s量化模型 Python部署示例
-本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv5量化模型在CPU/GPU上的部署推理加速.
+`infer.py` in this directory can help you quickly complete the inference acceleration of YOLOv5s quantization model deployment on CPU/GPU.
 
-## 部署准备
-### FastDeploy环境准备
-- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
-- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+## Deployment Preparations
+### FastDeploy Environment Preparations
+- 1. For the software and hardware requirements, please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md). 
+- 2. For the installation of FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
 
-### 量化模型准备
-- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
-- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
+### Quantized Model Preparations
+- 1. You can directly use the quantized model provided by FastDeploy for deployment..
+- 2. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.
 
 
-## 以量化后的YOLOv5s模型为例, 进行部署
+## Take the Quantized YOLOv5s Model as an example for Deployment
 ```bash
-#下载部署示例代码
+# Download sample deployment code.
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd examples/vision/detection/yolov5/quantize/python
 
-#下载FastDeloy提供的yolov5s量化模型文件和测试图片
+# Download the yolov5s quantized model and test images provided by FastDeloy.
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
 tar -xvf yolov5s_quant.tar
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
-# 在CPU上使用ONNX Runtime推理量化模型
+# Use ONNX Runtime inference quantization model on CPU.
 python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend ort
-# 在GPU上使用TensorRT推理量化模型
+# Use TensorRT inference quantization model on GPU.
 python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt
-# 在GPU上使用Paddle-TensorRT推理量化模型
+# Use Paddle-TensorRT inference quantization model on GPU.
 python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend pptrt
 ```