Win10下YOLOv5模型C++部署实战:ONNX导出+TensorRT INT8量化加速

上传者: study | 上传时间: 2026-04-27 15:08:32 | 文件大小: 18.91MB | 文件类型: ZIP
一套开箱即用的YOLOv5s在Windows 10平台C++环境下的端到端部署方案,基于OpenCV和TensorRT实现高性能推理。流程覆盖Python端模型导出(export.py生成.onnx)、C++端TensorRT引擎构建、INT8精度校准(含calibrator.cpp实现)、CUDA预处理(preprocess.cu/h)与自定义YOLO层封装(yololayer.cu/h)。所有代码适配VS工程,包含完整头文件依赖(common.hpp、cuda_utils.h、my_common.h等)和典型测试图(bus.jpg、zidane.jpg等),支持直接编译运行。适用于算法工程师快速落地目标检测模型,无需从零搭建环境,重点解决Windows下TensorRT INT8量化部署中的常见兼容性问题与内存管理细节。

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