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examples/vision/segmentation/paddleseg/quantize/python/README.md

@@ -1,28 +1,29 @@
-# PaddleSeg 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PaddleSeg量化模型在CPU/GPU上的部署推理加速.
+English | [简体中文](README_CN.md)
+# PaddleSeg Quantitative Model Python Deployment Example
+ `infer.py` in this directory can help you quickly complete the inference acceleration of PaddleSeg 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.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.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.)
+### 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.(Note: The quantized classification model still needs the deploy.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.)
 
 
-## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署
+## Take the Quantized PP_LiteSeg_T_STDC1_cityscapes 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/segmentation/paddleseg/quantize/python
 
-#下载FastDeloy提供的PP_LiteSeg_T_STDC1_cityscapes量化模型文件和测试图片
+# Download the PP_LiteSeg_T_STDC1_cityscapes quantized model and test images provided by FastDeloy.
 wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
 tar -xvf PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
 wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
 
-# 在CPU上使用Paddle-Inference推理量化模型
+# Use Paddle-Inference inference quantization model on CPU.
 python infer.py --model PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_QAT --image cityscapes_demo.png --device cpu --backend paddle
 
 ```