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<div align="center">
  <p>
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<br>


[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。

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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</div>

## <div align="center">文档</div>

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。

<details open>
<summary>安装</summary>

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。

[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)

```bash

pip install ultralytics

```

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/)。

[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)

</details>

<details open>
<summary>使用</summary>

### CLI

YOLO 可以直接在命令行接口（CLI）中使用 `yolo` 命令：

```bash

yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'

```

`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。

### Python

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/)：

```python

from ultralytics import YOLO



# 加载模型

model = YOLO("yolo11n.pt")



# 训练模型

train_results = model.train(

    data="coco8.yaml",  # 数据集 YAML 路径

    epochs=100,  # 训练轮次

    imgsz=640,  # 训练图像尺寸

    device="cpu",  # 运行设备，例如 device=0 或 device=0,1,2,3 或 device=cpu

)



# 评估模型在验证集上的性能

metrics = model.val()



# 在图像上执行对象检测

results = model("path/to/image.jpg")

results[0].show()



# 将模型导出为 ONNX 格式

path = model.export(format="onnx")  # 返回导出模型的路径

```

请参阅 YOLO [Python 文档](https://docs.ultralytics.com/usage/python/) 以获取更多示例。

</details>

## <div align="center">模型</div>

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。

<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">

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。

<details open><summary>检测 (COCO)</summary>

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。

| 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) |
| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
| [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               |
| [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              |
| [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              |
| [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              |
| [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             |

- **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`
- **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`

</details>

<details><summary>分割 (COCO)</summary>

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。

| 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) |
| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
| [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              |
| [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              |
| [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             |
| [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             |
| [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             |

- **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-seg.yaml device=0`
- **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-seg.yaml batch=1 device=0|cpu`

</details>

<details><summary>分类 (ImageNet)</summary>

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。

| 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 |
| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | ---------------- | ------------------------ |
| [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                      |
| [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                     |
| [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                     |
| [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                     |
| [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                    |

- **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`
- **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`

</details>

<details><summary>姿态 (COCO)</summary>

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）。

| 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) |
| -------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
| [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             |
| [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             |
| [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            |
| [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            |
| [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            |

- **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`
- **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`

</details>

<details><summary>OBB (DOTAv1)</summary>

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。

| 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) |
| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
| [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              |
| [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              |
| [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             |
| [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             |
| [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             |

- **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)。
- **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`

</details>

## <div align="center">集成</div>

我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，增强了数据集标记、训练、可视化和模型管理等任务的能力。了解 Ultralytics 如何与 [Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 合作，优化您的 AI 工作流程。

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|                                                           Roboflow                                                           |                                                 ClearML ⭐ NEW                                                  |                                                                       Comet ⭐ NEW                                                                        |                                          Neural Magic ⭐ NEW                                           |
| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| Label and export your custom datasets directly to YOLO11 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLO11 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |

## <div align="center">Ultralytics HUB</div>

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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## <div align="center">贡献</div>

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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## <div align="center">许可</div>

Ultralytics 提供两种许可选项以适应各种用例：

- **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。
- **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。

## <div align="center">联系</div>

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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