安装
Windows 一键安装
准备 Python 3.10 及以上版本后，克隆项目并运行安装脚本：
git clone https://github.com/only-one-over/yolo26_app.git
cd yolo26_app
install.bat
脚本会创建虚拟环境、安装 PyTorch 与应用依赖，并检查 PyQt6、OpenCV、Ultralytics 和可选 TensorRT 环境。
手动安装
git clone https://github.com/only-one-over/yolo26_app.git
cd yolo26_app
python -m venv venv
Windows：
venv\Scripts\activate
pip install -e .
Linux 或 macOS：
source venv/bin/activate
pip install -e .
GPU、SAM2、Grounding DINO、RealSense 与 TensorRT 的安装方式见环境与模型。
构建与安装 wheel
发布或验证安装产物时，使用标准 wheel：
python -m pip install build
python -m build
python -m pip install --force-reinstall dist/yolo26_app-*.whl
构建命令会同时生成 wheel 与源码分发包。wheel 已包含界面 SVG 图标和 YAML 模板。
启动
python main.py
Windows 使用一键安装脚本后，也可以直接执行：
venv\Scripts\python.exe main.py
安装 wheel 后，Windows 可以直接运行图形启动命令：
yolo26-app
PyPI 安装
已发布的 Python 包可在新虚拟环境中安装：
python -m pip install yolo26-app
yolo26-app
PyPI 包不包含模型权重、CUDA/TensorRT、SAM2、Grounding DINO 或 Windows 便携运行时；这些组件按需安装。普通 Windows 用户可继续从 GitHub Release 下载 CPU 或 CUDA 便携包。
基本使用
- 通过“文件 -> 新建项目”创建工作区。
- 在“标注”页导入图片、视频或素材目录，并添加类别。
- 使用矩形框、多边形、关键点或 OBB 工具完成标注。
- 点击“导出数据集”，生成 YOLO 格式的数据集。
- 在“训练”页选择 data.yaml，设置模型与训练参数后开始训练。
- 在“测试”页加载 best.pt，对图片、视频或相机执行推理，必要时导出 ONNX、TensorRT 等模型。
标注会自动保存；重新打开项目或异常退出后可恢复。更详细的标注、数据集和训练说明见标注、数据集与训练。
文档
| 主题 | 说明 |
|---|---|
| 标注、数据集与训练 | 素材导入、标注工具、辅助标注、数据集导出和训练 |
| 推理与模型导出 | 图片、视频、相机推理与部署模型导出 |
| 环境与模型 | GPU、可选依赖、模型权重和 TensorRT |
| 开发指南 | 架构、测试和扩展方式 |
| 诊断与排障 | 日志、诊断报告和常见异常 |
许可证
本项目采用 MIT License。项目依赖 Ultralytics YOLO，请同时遵守其许可证要求。
1 maintainer
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