1. 官方Github仓库主仓库https://github.com/ultralytics/ultralytics模型权重托管仓库https://github.com/ultralytics/assets/releases2. 全部.pt预训练权重直链v8.3.0版本YOLOv8YOLO11# yolov8l 检测 https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.pt # yolov8l-seg 实例分割 https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-seg.pt # yolo11n-cls 图像分类 https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt # yolo11n-pose 姿态估计 https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt # yolov8m-obb 旋转框检测 https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-obb.pt3. 一键导出ONNX完整代码复制运行第一步安装依赖pipinstallultralytics第二步导出脚本fromultralyticsimportYOLO# 逐个加载pt并导出onnxmodel_list[yolov8l.pt,yolov8l-seg.pt,yolo11n-cls.pt,yolo11n-pose.pt,yolov8m-obb.pt]fornameinmodel_list:modelYOLO(name)# formatonnx 导出默认640分辨率out_pathmodel.export(formatonnx)print(f导出完成:{out_path})运行后当前目录就会生成你需要的5个onnx文件yolov8l.onnx、yolov8l-seg.onnx、yolo11n-cls.onnx、yolo11n-pose.onnx、yolov8m-obb.onnx4. 服务器上一键下载导出全套直接粘贴到Linux服务器执行# 下载权重wgethttps://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.ptwgethttps://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-seg.ptwgethttps://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.ptwgethttps://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.ptwgethttps://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-obb.pt# 安装依赖pipinstallultralytics# python导出onnxpython-c from ultralytics import YOLO models [yolov8l.pt,yolov8l-seg.pt,yolo11n-cls.pt,yolo11n-pose.pt,yolov8m-obb.pt] for m in models: p YOLO(m).export(formatonnx) print(Exported:, p) 5. 国内下载慢解决方案如果github下载超时可以使用加速地址格式在原链接前面加https://mirror.ghproxy.com/示例https://mirror.ghproxy.com/https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.pt补充说明不要网上随便找别人打包好的onnx输入尺寸、后处理节点各不相同容易推理报错官方pt导出最标准如果你需要固定onnx参数例如简化op、固定输入shape、启用dynamic我可以给你加上对应导出参数