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

@@ -1,31 +1,32 @@
-# PP-YOLOE-l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PP-YOLOE量化模型在CPU/GPU上的部署推理加速.
+English | [简体中文](README_CN.md)
+# PP-YOLOE-l Quantitative Model Python Deployment Example
+ `infer.py` in this directory can help you quickly complete the inference acceleration of PP-YOLOE 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infer_cfg.yml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 infer_cfg.yml 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-YOLOE-l模型为例, 进行部署
+## Take the Quantized PP-YOLOE-l 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/paddledetection/quantize/python
 
-#下载FastDeloy提供的ppyoloe_crn_l_300e_coco量化模型文件和测试图片
+# Download the ppyoloe_crn_l_300e_coco quantized model and test images provided by FastDeloy.
 wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco_qat.tar
 tar -xvf ppyoloe_crn_l_300e_coco_qat.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_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --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_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --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_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device gpu --backend pptrt
 ```