[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/classification/paddleclas/quantize/cpp/README_CN.md

@@ -0,0 +1,37 @@
+[English](README.md) | 简体中文
+# PaddleClas 量化模型 C++部署示例
+本目录下提供的`infer.cc`,可以帮助用户快速完成PaddleClas量化模型在CPU/GPU上的部署推理加速.
+
+## 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)
+
+### 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.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
+
+## 以量化后的ResNet50_Vd模型为例, 进行部署，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
+在本目录执行如下命令即可完成编译,以及量化模型部署.
+```bash
+mkdir build
+cd build
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+#下载FastDeloy提供的ResNet50_Vd量化模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
+tar -xvf resnet50_vd_ptq.tar
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# 在CPU上使用ONNX Runtime推理量化模型
+./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 0
+# 在GPU上使用TensorRT推理量化模型
+./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 1
+# 在GPU上使用Paddle-TensorRT推理量化模型
+./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 2
+```