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* Update VERSION_NUMBER * Update paddle_inference.cmake * Delete docs directory * release new docs * update version number * add vision result doc * update version * fix dead link * fix vision * fix dead link * Update README_EN.md * Update README_EN.md * Update README_EN.md * Update README_EN.md * Update README_EN.md * Update README_CN.md * Update README_EN.md * Update README_CN.md * Update README_EN.md * Update README_CN.md * Update README_EN.md * Update README_EN.md Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>
35 lines
1.5 KiB
Markdown
35 lines
1.5 KiB
Markdown
# YOLOv5量化模型 C++部署示例
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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.
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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[一键模型量化工具](../../../../../../tools/quantization/),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YOLOv5s模型为例, 进行部署
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在本目录执行如下命令即可完成编译,以及量化模型部署.
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```bash
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mkdir build
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cd build
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.3.0.tgz
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tar xvf fastdeploy-linux-x64-0.3.0.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.3.0
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make -j
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#下载FastDeloy提供的yolov5s量化模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
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tar -xvf yolov5s_quant.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Paddle-Inference推理量化模型
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./infer_demo yolov5s_quant 000000014439.jpg 0
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# 在GPU上使用TensorRT推理量化模型
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./infer_demo yolov5s_quant 000000014439.jpg 1
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```
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