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[Doc] Update English version of some documents (#1084)
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# YOLOv6量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv6量化模型在CPU/GPU上的部署推理加速.
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English | [简体中文](README_CN.md)
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# YOLOv6 Quantification Model Python Deployment Example
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This directory provides examples that `infer.py` fast finishes the deployment of YOLOv6 quantification models on CPU/GPU.
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## 部署准备
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### FastDeploy环境准备
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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## Prepare the deployment
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### FastDeploy Environment Preparation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 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.
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### Prepare the quantification model
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- 1. Users can directly deploy quantized models provided by FastDeploy.
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- 2. ii. Or users can use the [One-click auto-compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to automatically conduct quantification model for deployment.
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## 以量化后的YOLOv6s模型为例, 进行部署
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## Example: quantized YOLOv6 model
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```bash
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#下载部署示例代码
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/slim/yolov6/python
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#下载FastDeloy提供的yolov6s量化模型文件和测试图片
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# Download yolov6 quantification model files and test images provided by FastDeploy
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_qat_model_new.tar
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tar -xvf yolov6s_qat_model.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用ONNX Runtime推理量化模型
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# Use ONNX Runtime quantification model on CPU
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device cpu --backend ort
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# 在GPU上使用TensorRT推理量化模型
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# Use TensorRT quantification model on GPU
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend trt
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# 在GPU上使用Paddle-TensorRT推理量化模型
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# Use Paddle-TensorRT quantification model on GPU
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend pptrt
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```
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[English](README.md) | 简体中文
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# YOLOv6量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv6量化模型在CPU/GPU上的部署推理加速.
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## 部署准备
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### FastDeploy环境准备
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 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.
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## 以量化后的YOLOv6s模型为例, 进行部署
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/slim/yolov6/python
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#下载FastDeloy提供的yolov6s量化模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_qat_model_new.tar
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tar -xvf yolov6s_qat_model.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用ONNX Runtime推理量化模型
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device cpu --backend ort
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# 在GPU上使用TensorRT推理量化模型
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend trt
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# 在GPU上使用Paddle-TensorRT推理量化模型
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend pptrt
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```
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