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YOLO26 App

基于 Ultralytics YOLO 的桌面端标注、训练和推理应用,支持 YOLO26 与 YOLOv8。

Python PyQt6 License

English · 完整文档 · 环境与模型 · 常见问题

安装

Windows 一键安装

要求 Python 3.10-3.12。克隆项目后运行统一安装引导器:

git clone https://github.com/only-one-over/yolo26_app.git
cd yolo26_app
install.bat --profile auto

auto 仅在 nvidia-smi 可用时选择 CUDA 12.1 配置,否则选择 CPU 配置;不会根据 CUDA 字符串安装未经测试的 PyTorch 版本。安装器会创建 venv、使用锁定依赖、执行 pip check,并在 Windows 上创建“YOLO26 App”桌面快捷方式。

可用选项:

install.bat --profile cpu
install.bat --profile cuda --with-sam --with-dino --with-realsense
install.bat --dry-run

Windows 便携包

普通用户也可以从 GitHub Releases 下载 CPU 或 CUDA 便携包。完整解压后启动对应的 EXE;便携包默认不会自动创建快捷方式,可右键 EXE 选择“发送到 -> 桌面快捷方式”。不要单独移动 EXE 文件。

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\python.exe -m pip install -r requirements-lock-cpu.txt
venv\Scripts\python.exe -m pip install --no-deps .

Linux 或 macOS:

source venv/bin/activate
python -m pip install -r requirements-lock-cpu.txt
python -m pip install --no-deps .

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 venv .venv

Windows:

.venv\Scripts\python.exe -m pip install "yolo26-app[onnx]"
.venv\Scripts\yolo26-app.exe

Linux 或 macOS:

.venv/bin/python -m pip install "yolo26-app[onnx]"
.venv/bin/yolo26-app

GPU ONNX 推理可将 [onnx] 改为 [onnx-gpu];SAM2、Grounding DINO、RealSense 和 TensorRT 也可按需追加对应 extra。PyPI 包不包含模型权重或 Windows 便携运行时。

基本使用

  1. 通过“文件 -> 新建项目”创建工作区。
  2. 在“标注”页导入图片、视频或素材目录,并添加类别。
  3. 使用矩形框、多边形、关键点或 OBB 工具完成标注。
  4. 点击“导出数据集”,生成 YOLO 格式的数据集。
  5. 在“训练”页选择 data.yaml,设置模型与训练参数后开始训练。
  6. 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。

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