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examples/vision/classification/paddleclas/a311d/cpp/README.md

@@ -1,26 +1,27 @@
-# PaddleClas A311D 开发板 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 A311D 上的部署推理加速。
+English | [简体中文](README_CN.md)
+# PaddleClas A311D Development Board C++ Deployment Example
+ `infer.cc` in this directory can help you quickly complete the inference acceleration of PaddleClas quantization model deployment on A311D.
 
-## 部署准备
-### FastDeploy 交叉编译环境准备
-1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/a311d.md#交叉编译环境搭建)  
+## Deployment Preparations
+### FastDeploy Cross-compile Environment Preparations
+1. For the software and hardware environment, and the cross-compile environment, please refer to [FastDeploy Cross-compile environment](../../../../../../docs/en/build_and_install/a311d.md#Cross-compilation-environment-construction).  
 
-### 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.)
+### Quantization 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.(Note: The quantized classification model still needs the inference_cls.yaml file in the FP32 model folder. Self-quantized model folder does not contain this yaml file, you can copy it from the FP32 model folder to the quantized model folder.)
 
-更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
+For more information, please refer to [Model Quantization](../../quantize/README.md).
 
-## 在 A311D 上部署量化后的 ResNet50_Vd 分类模型
-请按照以下步骤完成在 A311D 上部署 ResNet50_Vd 量化模型：
-1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/a311d.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
+## Deploying the Quantized ResNet50_Vd Segmentation model on A311D
+Please follow these steps to complete the deployment of the ResNet50_Vd quantization model on A311D.
+1. Cross-compile the FastDeploy library as described in [Cross-compile  FastDeploy](../../../../../../docs/en/build_and_install/a311d.md#FastDeploy-cross-compilation-library-compilation-based-on-Paddle-Lite).
 
-2. 将编译后的库拷贝到当前目录，可使用如下命令：
+2. Copy the compiled library to the current directory. You can run this line:
 ```bash
 cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/
 ```
 
-3. 在当前路径下载部署所需的模型和示例图片：
+3. Download the model and example images required for deployment in current path.
 ```bash
 cd FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/
 mkdir models && mkdir images
@@ -31,26 +32,26 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima
 cp -r ILSVRC2012_val_00000010.jpeg images
 ```
 
-4. 编译部署示例，可使入如下命令：
+4. Compile the deployment example. You can run the following lines:
 ```bash
 cd FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/
 mkdir build && cd build
 cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
 make -j8
 make install
-# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
+# After success, an install folder will be created with a running demo and libraries required for deployment.
 ```
 
-5. 基于 adb 工具部署 ResNet50 分类模型到晶晨 A311D，可使用如下命令：
+5. Deploy the ResNet50 segmentation model to A311D based on adb. You can run the following lines:
 ```bash
-# 进入 install 目录
+# Go to the install directory.
 cd FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/build/install/
-# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
+# The following line represents: bash run_with_adb.sh, demo needed to run, model path, image path, DEVICE ID.
 bash run_with_adb.sh infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg $DEVICE_ID
 ```
 
-部署成功后运行结果如下：
+The output is:
 
 <img width="640" src="https://user-images.githubusercontent.com/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.png">
 
-需要特别注意的是，在 A311D 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
+Please note that the model deployed on A311D needs to be quantized. You can refer to [Model Quantization](../../../../../../docs/en/quantize.md).