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<div align="center"> |
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<p> |
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<a href="https://www.ultralytics.com/events/yolovision" target="_blank"> |
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision banner"></a> |
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</p> |
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[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar) <br> |
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<div> |
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<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a> |
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<a href="https://pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a> |
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a> |
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<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> |
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<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> |
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<a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a> |
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<br> |
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<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics on Gradient"></a> |
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<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Ultralytics In Colab"></a> |
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<a href="https://www.kaggle.com/models/ultralytics/yolo11"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a> |
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<a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a> |
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</div> |
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<br> |
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[Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics) 是一个尖端的、最先进（SOTA）的模型，基于之前 YOLO 版本的成功，并引入了新功能和改进以进一步提升性能和灵活性。YOLO11 被设计得快速、准确且易于使用，是进行广泛对象检测和跟踪、实例分割、图像分类和姿态估计任务的理想选择。 |
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我们希望这里的资源能帮助你充分利用 YOLO。请浏览 Ultralytics <a href="https://docs.ultralytics.com/">文档</a> 以获取详细信息，在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提出问题或讨论，成为 Ultralytics <a href="https://discord.com/invite/ultralytics">Discord</a>、<a href="https://reddit.com/r/ultralytics">Reddit</a> 和 <a href="https://community.ultralytics.com/">论坛</a> 的成员！ |
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想申请企业许可证，请完成 [Ultralytics Licensing](https://www.ultralytics.com/license) 上的表单。 |
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<a href="https://docs.ultralytics.com/models/yolo11/" target="_blank"> |
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="YOLO11 performance plots"> |
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</a> |
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<div align="center"> |
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<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space"> |
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<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space"> |
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<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space"> |
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<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space"> |
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<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space"> |
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<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space"> |
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<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a> |
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</div> |
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</div> |
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## <div align="center">文档</div> |
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请参阅下方的快速开始安装和使用示例，并查看我们的 [文档](https://docs.ultralytics.com/) 以获取有关训练、验证、预测和部署的完整文档。 |
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<details open> |
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<summary>安装</summary> |
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在 [**Python>=3.8**](https://www.python.org/) 环境中使用 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 通过 pip 安装包含所有[依赖项](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) 的 ultralytics 包。 |
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[](https://pypi.org/project/ultralytics/) [](https://www.pepy.tech/projects/ultralytics) [](https://pypi.org/project/ultralytics/) |
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```bash |
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pip install ultralytics |
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``` |
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有关其他安装方法，包括 [Conda](https://anaconda.org/conda-forge/ultralytics)、[Docker](https://hub.docker.com/r/ultralytics/ultralytics) 和 Git，请参阅 [快速开始指南](https://docs.ultralytics.com/quickstart/)。 |
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[](https://anaconda.org/conda-forge/ultralytics) [](https://hub.docker.com/r/ultralytics/ultralytics) [](https://hub.docker.com/r/ultralytics/ultralytics) |
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</details> |
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<details open> |
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<summary>使用</summary> |
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### CLI |
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YOLO 可以直接在命令行接口（CLI）中使用 `yolo` 命令： |
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```bash |
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yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg' |
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``` |
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`yolo` 可以用于各种任务和模式，并接受额外参数，例如 `imgsz=640`。请参阅 YOLO [CLI 文档](https://docs.ultralytics.com/usage/cli/) 以获取示例。 |
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### Python |
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YOLO 也可以直接在 Python 环境中使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)： |
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```python |
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from ultralytics import YOLO |
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# 加载模型 |
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model = YOLO("yolo11n.pt") |
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# 训练模型 |
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train_results = model.train( |
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data="coco8.yaml", # 数据集 YAML 路径 |
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epochs=100, # 训练轮次 |
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imgsz=640, # 训练图像尺寸 |
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device="cpu", # 运行设备，例如 device=0 或 device=0,1,2,3 或 device=cpu |
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) |
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# 评估模型在验证集上的性能 |
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metrics = model.val() |
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# 在图像上执行对象检测 |
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results = model("path/to/image.jpg") |
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results[0].show() |
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# 将模型导出为 ONNX 格式 |
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path = model.export(format="onnx") # 返回导出模型的路径 |
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``` |
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请参阅 YOLO [Python 文档](https://docs.ultralytics.com/usage/python/) 以获取更多示例。 |
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</details> |
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## <div align="center">模型</div> |
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YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/) 和 [姿态](https://docs.ultralytics.com/tasks/pose/) 模型在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上进行预训练，这些模型可在此处获得，此外还有在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的 YOLO11 [分类](https://docs.ultralytics.com/tasks/classify/) 模型。所有检测、分割和姿态模型均支持 [跟踪](https://docs.ultralytics.com/modes/track/) 模式。 |
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<a href="https://docs.ultralytics.com/tasks/" target="_blank"> |
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<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks"> |
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</a> |
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所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时自动从最新的 Ultralytics [发布](https://github.com/ultralytics/assets/releases)下载。 |
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<details open><summary>检测 (COCO)</summary> |
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请参阅 [检测文档](https://docs.ultralytics.com/tasks/detect/) 以获取使用这些在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上训练的模型的示例，其中包含 80 个预训练类别。 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) | |
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| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- | |
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| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 | |
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| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 | |
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| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 | |
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| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 | |
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| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 | |
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- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val detect data=coco.yaml device=0` |
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val detect data=coco.yaml batch=1 device=0|cpu` |
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</details> |
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<details><summary>分割 (COCO)</summary> |
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请参阅 [分割文档](https://docs.ultralytics.com/tasks/segment/) 以获取使用这些在 [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/) 数据集上训练的模型的示例，其中包含 80 个预训练类别。 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) | |
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| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- | |
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| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4 | |
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| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5 | |
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| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3 | |
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| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2 | |
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| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0 | |
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- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val segment data=coco.yaml device=0` |
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val segment data=coco.yaml batch=1 device=0|cpu` |
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</details> |
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<details><summary>分类 (ImageNet)</summary> |
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请参阅 [分类文档](https://docs.ultralytics.com/tasks/classify/) 以获取使用这些在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上训练的模型的示例，其中包含 1000 个预训练类别。 |
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| 模型 | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 | |
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| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | ---------------- | ------------------------ | |
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| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 3.3 | |
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| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 12.1 | |
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| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 39.3 | |
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| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 12.9 | 49.4 | |
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| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 28.4 | 110.4 | |
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- **acc** 值为在 [ImageNet](https://www.image-net.org/) 数据集验证集上的模型准确率。 <br>复制命令 `yolo val classify data=path/to/ImageNet device=0` |
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 ImageNet 验证图像上平均。 <br>复制命令 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` |
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</details> |
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<details><summary>姿态 (COCO)</summary> |
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请参阅 [姿态文档](https://docs.ultralytics.com/tasks/pose/) 以获取使用这些在 [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/) 数据集上训练的模型的示例，其中包含 1 个预训练类别（人）。 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) | |
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| -------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- | |
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| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 17.2 | |
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| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.5 | |
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| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 183.5 | |
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| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.2 | 232.0 | |
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| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 520.2 | |
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- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val pose data=coco-pose.yaml device=0` |
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` |
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</details> |
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<details><summary>OBB (DOTAv1)</summary> |
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请参阅 [OBB 文档](https://docs.ultralytics.com/tasks/obb/) 以获取使用这些在 [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/) 数据集上训练的模型的示例，其中包含 15 个预训练类别。 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>test<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) | |
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| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- | |
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| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.56 ± 0.80 | 4.43 ± 0.01 | 2.7 | 17.2 | |
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| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.41 ± 4.00 | 5.13 ± 0.02 | 9.7 | 57.5 | |
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| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.81 ± 2.87 | 10.07 ± 0.38 | 20.9 | 183.5 | |
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| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.49 ± 4.98 | 13.46 ± 0.55 | 26.2 | 232.0 | |
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| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.63 ± 7.67 | 28.59 ± 0.96 | 58.8 | 520.2 | |
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- **mAP<sup>test</sup>** 值针对单模型多尺度在 [DOTAv1](https://captain-whu.github.io/DOTA/index.html) 数据集上进行。 <br>复制命令 `yolo val obb data=DOTAv1.yaml device=0 split=test` 并提交合并结果到 [DOTA 评估](https://captain-whu.github.io/DOTA/evaluation.html)。 |
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 DOTAv1 验证图像上平均。 <br>复制命令 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` |
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</details> |
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## <div align="center">集成</div> |
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我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，提升了数据集标注、训练、可视化和模型管理等任务。探索 Ultralytics 如何通过与 [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/)、[Comet](https://bit.ly/yolov8-readme-comet)、[Roboflow](https://roboflow.com/?ref=ultralytics) 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 的合作，优化您的 AI 工作流程。 |
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<a href="https://www.ultralytics.com/hub" target="_blank"> |
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a> |
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<div align="center"> |
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<a href="https://www.ultralytics.com/hub"> |
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space"> |
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<a href="https://docs.wandb.ai/guides/integrations/ultralytics/"> |
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="W&B logo"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space"> |
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<a href="https://bit.ly/yolov8-readme-comet"> |
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space"> |
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<a href="https://bit.ly/yolov5-neuralmagic"> |
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a> |
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</div> |
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| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic | |
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| :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | |
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| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://www.ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 | |
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## <div align="center">Ultralytics HUB</div> |
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体验无缝 AI 使用 [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐，一个集数据可视化、YOLO11 🚀 模型训练和部署于一体的解决方案，无需编写代码。利用我们最先进的平台和用户友好的 [Ultralytics 应用](https://www.ultralytics.com/app-install)，将图像转换为可操作见解，并轻松实现您的 AI 愿景。免费开始您的旅程！ |
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<a href="https://www.ultralytics.com/hub" target="_blank"> |
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a> |
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## <div align="center">贡献</div> |
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我们欢迎您的意见！没有社区的帮助，Ultralytics YOLO 就不可能实现。请参阅我们的 [贡献指南](https://docs.ultralytics.com/help/contributing/) 开始，并填写我们的 [调查问卷](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们提供您体验的反馈。感谢所有贡献者 🙏！ |
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<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 --> |
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<a href="https://github.com/ultralytics/ultralytics/graphs/contributors"> |
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a> |
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## <div align="center">许可</div> |
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Ultralytics 提供两种许可选项以适应各种用例： |
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- **AGPL-3.0 许可**：这是一个 [OSI 批准](https://opensource.org/license) 的开源许可，适合学生和爱好者，促进开放协作和知识共享。有关详细信息，请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。 |
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- **企业许可**：专为商业使用设计，此许可允许将 Ultralytics 软件和 AI 模型无缝集成到商业产品和服务中，无需满足 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license) 联系我们。 |
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## <div align="center">联系</div> |
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如需 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)。成为 Ultralytics [Discord](https://discord.com/invite/ultralytics)、[Reddit](https://www.reddit.com/r/ultralytics/) 或 [论坛](https://community.ultralytics.com/) 的成员，提出问题、分享项目、探讨学习讨论，或寻求所有 Ultralytics 相关的帮助！ |
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