@@ -8,25 +8,25 @@
 
 <div>
     <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>
-    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
-    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
-    <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Ultralytics Docker Pulls"></a>
+    <a href="https://ultralytics.com/discord"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
     <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>
     <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>
     <br>
-    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
-    <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 In Colab"></a>
-    <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+    <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>
+    <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>
+    <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
 </div>
 <br>
 
-[Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
+[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您充分利用 YOLOv8。请浏览 YOLOv8 的<a href="https://docs.ultralytics.com/">文档</a>了解详情，如需支持、提问或讨论，请在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提出问题，成为 Ultralytics <a href="https://ultralytics.com/discord">Discord</a>、<a href="https://reddit.com/r/ultralytics">Reddit</a> 和 <a href="https://community.ultralytics.com">论坛</a> 的一员！
+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://ultralytics.com/discord">Discord</a>、<a href="https://reddit.com/r/ultralytics">Reddit</a> 和 <a href="https://community.ultralytics.com">论坛</a> 的成员！
 
-如需申请企业许可，请在 [Ultralytics Licensing](https://www.ultralytics.com/license) 处填写表格
+想申请企业许可证，请完成 [Ultralytics Licensing](https://www.ultralytics.com/license) 上的表单。
 
-<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" alt="YOLOv8 performance plots"></a>
+<img width="100%" src="https://github.com/user-attachments/assets/a311a4ed-bbf2-43b5-8012-5f183a28a845" alt="YOLO11 performance plots"></a>
 
 <div align="center">
   <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>
@@ -45,16 +45,14 @@
 </div>
 </div>
 
-以下是提供的内容的中文翻译：
-
 ## <div align="center">文档</div>
 
-请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com/) 上有关训练、验证、预测和部署的完整文档。
+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>
 
-使用Pip在一个[**Python>=3.8**](https://www.python.org/)环境中安装`ultralytics`包，此环境还需包含[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。这也会安装所有必要的[依赖项](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml)。
+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/)
 
@@ -62,168 +60,154 @@
 pip install ultralytics
 ```
 
-如需使用包括[Conda](https://anaconda.org/conda-forge/ultralytics)，[Docker](https://hub.docker.com/r/ultralytics/ultralytics)和Git在内的其他安装方法，请参考[快速入门指南](https://docs.ultralytics.com/quickstart/)。
+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>Usage</summary>
+<summary>使用</summary>
 
 ### CLI
 
-YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令：
+YOLO 可以直接在命令行接口（CLI）中使用 `yolo` 命令：
 
 ```bash
-yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
-`yolo` 可用于各种任务和模式，并接受其他参数，例如 `imgsz=640`。查看 YOLOv8 [CLI 文档](https://docs.ultralytics.com/usage/cli/)以获取示例。
+`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
 
-YOLOv8 也可以在 Python 环境中直接使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)：
+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("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 # 训练模型
 train_results = model.train(
-    data="coco8.yaml",  # 数据配置文件的路径
-    epochs=100,  # 训练的轮数
-    imgsz=640,  # 训练图像大小
-    device="cpu",  # 运行的设备，例如 device=0 或 device=0,1,2,3 或 device=cpu
+    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
 )
 
-# 在验证集上评估模型性能
+# 评估模型在验证集上的性能
 metrics = model.val()
 
-# 对图像进行目标检测
+# 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")  # 返回导出的模型路径
+path = model.export(format="onnx")  # 返回导出模型的路径
 ```
 
-查看 YOLOv8 [Python 文档](https://docs.ultralytics.com/usage/python/)以获取更多示例。
+请参阅 YOLO [Python 文档](https://docs.ultralytics.com/usage/python/) 以获取更多示例。
 
 </details>
 
-### 笔记本
-
-Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://www.youtube.com/ultralytics?sub_confirmation=1) 教程，使学习和实现高级 YOLOv8 功能变得简单。
-
-| 文档                                                                                                              | 笔记本                                                                                                                                                                                                                       |                                                                                                     YouTube                                              
-| ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------
-| <a href="https://docs.ultralytics.com/modes/">YOLOv8 训练、验证、预测和导出模式</a>                               | <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="在 Colab 中打开"></a>                  | <a href="https://youtu.be/j8uQc0qB91s"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-soci
-| <a href="https://docs.ultralytics.com/hub/quickstart/">Ultralytics HUB 快速开始</a>                               | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>                       | <a href="https://youtu.be/lveF9iCMIzc"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtub
-| <a href="https://docs.ultralytics.com/modes/track/">YOLOv8 视频中的多对象跟踪</a>                                 | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>           | <a href="https://youtu.be/hHyHmOtmEgs"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-y
-| <a href="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 视频中的对象计数</a>                        | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>           | <a href="https://youtu.be/Ag2e-5_NpS0"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-yo
-| <a href="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 视频中的热图</a>                                   | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>                  | <a href="https://youtu.be/4ezde5-nZZw"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-yout
-| <a href="https://docs.ultralytics.com/datasets/explorer/">Ultralytics 数据集浏览器，集成 SQL 和 OpenAI 🚀 New</a> | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a> | <a href="https://youtu.be/3VryynorQeo"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-socia
-
 ## <div align="center">模型</div>
 
-在[COCO](https://docs.ultralytics.com/datasets/detect/coco/)数据集上预训练的YOLOv8 [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/)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify/)模型。所有的检测，分割和姿态模型都支
+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、
 
 <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)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
+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>
 
-查看[检测文档](https://docs.ultralytics.com/tasks/detect/)以获取这些在[COCO](https://docs.ultralytics.com/datasets/detect/coco/)上训练的模型的使用示例，其中包括80个预训练类别。
+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>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
-| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
-| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640                 | 37.3                 | 80.4                          | 0.99                               | 3.2              | 8.7               |
-| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640                 | 44.9                 | 128.4                         | 1.20                               | 11.2             | 28.6              |
-| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640                 | 50.2                 | 234.7                         | 1.83                               | 25.9             | 78.9              |
-| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640                 | 52.9                 | 375.2                         | 2.39                               | 43.7             | 165.2             |
-| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640                 | 53.9                 | 479.1                         | 3.53                               | 68.2             | 257.8             |
+| 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>Tesla T4 TensorRT<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                 | 56.12 ± 0.82 ms               | 1.55 ± 0.01 ms                         | 2.6              | 6.5               |
+| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640                 | 47.0                 | 90.01 ± 1.17 ms               | 2.46 ± 0.00 ms                         | 9.4              | 21.5              |
+| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640                 | 51.5                 | 183.20 ± 2.04 ms              | 4.70 ± 0.06 ms                         | 20.1             | 68.0              |
+| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640                 | 53.4                 | 238.64 ± 1.39 ms              | 6.16 ± 0.08 ms                         | 25.3             | 86.9              |
+| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640                 | 54.7                 | 462.78 ± 6.66 ms              | 11.31 ± 0.24 ms                        | 56.9             | 194.9             |
 
-- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上的结果。 <br>通过 `yolo val detect data=coco.yaml device=0` 复现
-- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现
+- **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>
 
-查看[分割文档](https://docs.ultralytics.com/tasks/segment/)以获取这些在[COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/)上训练的模型的使用示例，其中包括80个预训练类别。
+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>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
-| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
-| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                 | 36.7                 | 30.5                  | 96.1                          | 1.21                               | 3.4              | 12.6              |
-| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                 | 44.6                 | 36.8                  | 155.7                         | 1.47                               | 11.8             | 42.6              |
-| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                 | 49.9                 | 40.8                  | 317.0                         | 2.18                               | 27.3             | 110.2             |
-| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                 | 52.3                 | 42.6                  | 572.4                         | 2.79                               | 46.0             | 220.5             |
-| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                 | 53.4                 | 43.4                  | 712.1                         | 4.02                               | 71.8             | 344.1             |
+| 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>Tesla T4 TensorRT<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                  | 65.90 ± 1.14 ms               | 1.84 ± 0.00 ms                         | 2.9              | 10.4              |
+| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640                 | 46.6                 | 37.8                  | 117.56 ± 4.89 ms              | 2.94 ± 0.01 ms                         | 10.1             | 35.5              |
+| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640                 | 51.5                 | 41.5                  | 281.63 ± 1.16 ms              | 6.31 ± 0.09 ms                         | 22.4             | 123.3             |
+| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                 | 53.4                 | 42.9                  | 344.16 ± 3.17 ms              | 7.78 ± 0.16 ms                         | 27.6             | 142.2             |
+| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                 | 54.7                 | 43.8                  | 664.50 ± 3.24 ms              | 15.75 ± 0.67 ms                        | 62.1             | 319.0             |
 
-- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上的结果。 <br>通过 `yolo val segment data=coco-seg.yaml device=0` 复现
-- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现
+- **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>姿态 (COCO)</summary>
 
-查看[姿态文档](https://docs.ultralytics.com/tasks/pose/)以获取这些在[COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/)上训练的模型的使用示例，其中包括1个预训练类别，即人。
+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>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
-| ---------------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
-| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt)       | 640                 | 50.4                  | 80.1               | 131.8                         | 1.18                               | 3.3              | 9.2               |
-| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt)       | 640                 | 60.0                  | 86.2               | 233.2                         | 1.42                               | 11.6             | 30.2              |
-| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt)       | 640                 | 65.0                  | 88.8               | 456.3                         | 2.00                               | 26.4             | 81.0              |
-| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt)       | 640                 | 67.6                  | 90.0               | 784.5                         | 2.59                               | 44.4             | 168.6             |
-| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt)       | 640                 | 69.2                  | 90.2               | 1607.1                        | 3.73                               | 69.4             | 263.2             |
-| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280                | 71.6                  | 91.2               | 4088.7                        | 10.04                              | 99.1             | 1066.4            |
+| 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>Tesla T4 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | -------------------------------------- | ---------------- | ----------------- |
+| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640                 | 50.0                  | 81.0               | 52.40 ± 0.51 ms               | 1.72 ± 0.01 ms                         | 2.9              | 7.6               |
+| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640                 | 58.9                  | 86.3               | 90.54 ± 0.59 ms               | 2.57 ± 0.00 ms                         | 9.9              | 23.2              |
+| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640                 | 64.9                  | 89.4               | 187.28 ± 0.77 ms              | 4.94 ± 0.05 ms                         | 20.9             | 71.7              |
+| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640                 | 66.1                  | 89.9               | 247.69 ± 1.10 ms              | 6.42 ± 0.13 ms                         | 26.2             | 90.7              |
+| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640                 | 69.5                  | 91.1               | 487.97 ± 13.91 ms             | 12.06 ± 0.20 ms                        | 58.8             | 203.3             |
 
-- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org/) 数据集上的结果。 <br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
-- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现
+- **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>旋转检测 (DOTAv1)</summary>
+<details><summary>OBB (DOTAv1)</summary>
 
-查看[旋转检测文档](https://docs.ultralytics.com/tasks/obb/)以获取这些在[DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/)上训练的模型的使用示例，其中包括15个预训练类别。
+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>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
-| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
-| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-obb.pt) | 1024                | 78.0               | 204.77                        | 3.57                               | 3.1              | 23.3              |
-| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-obb.pt) | 1024                | 79.5               | 424.88                        | 4.07                               | 11.4             | 76.3              |
-| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-obb.pt) | 1024                | 80.5               | 763.48                        | 7.61                               | 26.4             | 208.6             |
-| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024                | 80.7               | 1278.42                       | 11.83                              | 44.5             | 433.8             |
-| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024                | 81.36              | 1759.10                       | 13.23                              | 69.5             | 676.7             |
+| 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>Tesla T4 TensorRT<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 ms              | 4.43 ± 0.01 ms                         | 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 ms              | 5.13 ± 0.02 ms                         | 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 ms              | 10.07 ± 0.38 ms                        | 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 ms              | 13.46 ± 0.55 ms                        | 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 ms             | 28.59 ± 0.96 ms                        | 58.8             | 520.2             |
 
-- **mAP<sup>val</sup>** 值是基于单模型多尺度在 [DOTAv1](https://captain-whu.github.io/DOTA/index.html) 数据集上的结果。 <br>通过 `yolo val obb data=DOTAv1.yaml device=0 split=test` 复现
-- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` 复现
+- **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>
 
 <details><summary>分类 (ImageNet)</summary>
 
-查看[分类文档](https://docs.ultralytics.com/tasks/classify/)以获取这些在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/)上训练的模型的使用示例，其中包括1000个预训练类别。
+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>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
-| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | ---------------- | ------------------------ |
-| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224                 | 69.0             | 88.3             | 12.9                          | 0.31                               | 2.7              | 4.3                      |
-| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224                 | 73.8             | 91.7             | 23.4                          | 0.35                               | 6.4              | 13.5                     |
-| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224                 | 76.8             | 93.5             | 85.4                          | 0.62                               | 17.0             | 42.7                     |
-| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224                 | 78.3             | 94.2             | 163.0                         | 0.87                               | 37.5             | 99.7                     |
-| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224                 | 79.0             | 94.6             | 232.0                         | 1.01                               | 57.4             | 154.8                    |
+| 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>Tesla T4 TensorRT<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             | 5.03 ± 0.32 ms                | 1.10 ± 0.01 ms                         | 1.6              | 3.3                      |
+| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224                 | 75.4             | 92.7             | 7.89 ± 0.18 ms                | 1.34 ± 0.01 ms                         | 5.5              | 12.1                     |
+| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224                 | 77.3             | 93.9             | 17.17 ± 0.40 ms               | 1.95 ± 0.00 ms                         | 10.4             | 39.3                     |
+| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224                 | 78.3             | 94.3             | 23.17 ± 0.29 ms               | 2.76 ± 0.00 ms                         | 12.9             | 49.4                     |
+| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224                 | 79.5             | 94.9             | 41.41 ± 0.94 ms               | 3.82 ± 0.00 ms                         | 28.4             | 110.4                    |
 
-- **acc** 值是模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。 <br>通过 `yolo val classify data=path/to/ImageNet device=0` 复现
-- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 ImageNet val 图像进行平均计算的。 <br>通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
+- **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>
 
 ## <div align="center">集成</div>
 
-我们与领先的AI平台的关键整合扩展了Ultralytics产品的功能，增强了数据集标签化、训练、可视化和模型管理等任务。探索Ultralytics如何与[Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic以及[OpenVINO](https://docs.ultralytics.com/integrations/openvino/)合作，优化您的AI工作流程。
+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。
 
 <br>
 <a href="https://ultralytics.com/hub" target="_blank">
@@ -245,36 +229,36 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
 </div>
 
-|                                                 Roboflow                                                  |                                  ClearML ⭐ NEW                                  |                                                     Comet ⭐ NEW                                                     |                                        Neural Magic ⭐ NEW                                        |
-| :-------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
-| 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://clear.ml/)（开源！）自动跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 
+|                                                           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-neuralmagi
 
 ## <div align="center">Ultralytics HUB</div>
 
-体验 [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://www.ultralytics.com/app-install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！
+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！
 
 <a href="https://ultralytics.com/hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
 
 ## <div align="center">贡献</div>
 
-我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing/)以开始使用，并填写我们的[调查问卷](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏
+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 🙏！
 
 <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
 
 <a href="https://github.com/ultralytics/ultralytics/graphs/contributors">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
 
-## <div align="center">许可证</div>
+## <div align="center">许可</div>
 
-Ultralytics 提供两种许可证选项以适应各种使用场景：
+Ultralytics 提供两种许可选项以适应各种用例：
 
-- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/license)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。
-- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 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中，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license)与我们联系。
+- **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">联系方式</div>
+## <div align="center">联系</div>
 
-有关 Ultralytics 错误报告和功能请求，请访问 [GitHub 问题](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 用于提出问题、共享项目、学习讨论或寻求有关 Ultralytics 的所有帮助！
+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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