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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](../../../../../../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).

### 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.


## Take the Quantized YOLOv5s Model as an example for Deployment
```bash
# 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
```