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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

• 1. For the software and hardware requirements, please refer to FastDeploy Environment Requirements.
• 2. For the installation of FastDeploy Python whl package, please refer to FastDeploy Python Installation.

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 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