yolov8模型部署源码
YOLOv8使用TensorRT加速！首先是YOLOv8模型训练和导出：使用YOLOv8的训练代码和数据集进行模型训练。导出YOLOv8模型的权重文件和配置文件，以便后续在C++中加载和使用。安装TensorRT和相关依赖：下载并安装NVIDIA TensorRT，TensorRT是一个深度学习推理加速库。安装CUDA和CUDNN，确保与TensorRT版本兼容。安装OpenCV，用于图像处理和预处理。将YOLOv8模型转换为TensorRT格式：使用TensorRT提供的工具和API将YOLOv8模型从常规框架（如PyTorch或）转换为TensorRT格式。这涉及模型的序列化和优化，以便在TensorRT中进行高效的推理。
本栏目使用C++编写应用程序代码来加载TensorRT格式的YOLOv8模型并进行推理。使用TensorRT的C++ API，创建推理引擎并配置相关参数。进行图像预处理，如调整尺寸、归一化等操作。将预处后的图像输入到TensorRT引擎中进行目标检测推理。
解析和处理推理结果，包括目标框的提取、类别预测和置信度计算等。构建和编译：
使用适当的构建工具进行配置。
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