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examples/vision/detection/yolov5/quantize/cpp/README.md
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# YOLOv5量化模型 C++部署示例
English | [简体中文](README_CN.md)
# YOLOv5 Quantitative Model C++ Deployment Example
本目录下提供的`infer.cc`,可以帮助用户快速完成YOLOv5s量化模型在CPU/GPU上的部署推理加速.
`infer.cc` in this directory can help you quickly complete the inference acceleration of YOLOv5s 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.
### 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.
## 以量化后的YOLOv5s模型为例, 进行部署
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
## Take the Quantized YOLOv5s Model as an example for Deployment
Run the following commands in this directory to compile and deploy the quantized model. FastDeploy version 0.7.0 or higher is required (x.x.x>=0.7.0).
```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
# Download pre-compiled FastDeploy libraries. You can choose the appropriate version from `pre-compiled FastDeploy libraries` mentioned above.
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yolov5s量化模型文件和测试图片
# Download the yolov5s quantized model and test images provided by FastDeloy.
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
tar -xvf yolov5s_quant.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.
./infer_demo yolov5s_quant 000000014439.jpg 0
# 在GPU上使用TensorRT推理量化模型
# Use TensorRT inference quantization model on GPU.
./infer_demo yolov5s_quant 000000014439.jpg 1
# 在GPU上使用Paddle-TensorRT推理量化模型
# Use Paddle-TensorRT inference quantization model on GPU.
./infer_demo yolov5s_quant 000000014439.jpg 2
```

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examples/vision/detection/yolov5/quantize/cpp/README_CN.md Normal file
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[English](README.md) | 简体中文
# YOLOv5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YOLOv5s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.
## 以量化后的YOLOv5s模型为例, 进行部署
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
```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yolov5s量化模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
tar -xvf yolov5s_quant.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
./infer_demo yolov5s_quant 000000014439.jpg 0
# 在GPU上使用TensorRT推理量化模型
./infer_demo yolov5s_quant 000000014439.jpg 1
# 在GPU上使用Paddle-TensorRT推理量化模型
./infer_demo yolov5s_quant 000000014439.jpg 2
```