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FastDeploy/examples/vision/detection/yolov5/quantize/python
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Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>
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infer.py
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README.md
Bump up to version 0.3.0 (#371)
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README.md

YOLOv5s量化模型 Python部署示例

本目录下提供的infer.py,可以帮助用户快速完成YOLOv5量化模型在CPU/GPU上的部署推理加速.

部署准备

FastDeploy环境准备

量化模型准备

    1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
    1. 用户可以使用FastDeploy提供的一键模型量化工具,自行进行模型量化, 并使用产出的量化模型进行部署.

以量化后的YOLOv5s模型为例, 进行部署

#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/detection/yolov5/quantize/python

#下载FastDeloy提供的yolov5s量化模型文件和测试图片
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

# 在CPU上使用Paddle-Inference推理量化模型
python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend paddle
# 在GPU上使用TensorRT推理量化模型
python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt