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English | 简体中文
YOLOv5s量化模型 Python部署示例
infer.py 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
# Download sample deployment code.
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/detection/yolov5/quantize/python
# 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.
python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend ort
# Use TensorRT inference quantization model on GPU.
python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt
# Use Paddle-TensorRT inference quantization model on GPU.
python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend pptrt