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YOLOv5 Quantitative Model C++ Deployment Example
infer.cc in this directory can help you quickly complete the inference acceleration of YOLOv5s quantization model deployment on CPU/GPU.
Deployment Preparations
FastDeploy Environment Preparations
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- For the software and hardware requirements, please refer to FastDeploy Environment Requirements.
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- For the installation of FastDeploy Python whl package, please refer to FastDeploy Python Installation.
Quantized Model Preparations
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- You can directly use the quantized model provided by FastDeploy for deployment.
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- You can use one-click automatical compression tool provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.
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).
mkdir build
cd build
# 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
# 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
# Use ONNX Runtime inference quantization model on CPU.
./infer_demo yolov5s_quant 000000014439.jpg 0
# Use TensorRT inference quantization model on GPU.
./infer_demo yolov5s_quant 000000014439.jpg 1
# Use Paddle-TensorRT inference quantization model on GPU.
./infer_demo yolov5s_quant 000000014439.jpg 2