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Ultralytics
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YOLO11

Object Detection
ultralytics
yolo
instance-segmentation
image-classification
pose-estimation
obb
tracking
yolo11
Eval Results (legacy)
Model card Files
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@@ -13,68 +13,80 @@ language:
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  - vi
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  - ar
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  library_name: ultralytics
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- pipeline_tag: object-detection
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- tags:
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- - ultralytics
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- - yolo
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- - object-detection
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- - instance-segmentation
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- - image-classification
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- - pose-estimation
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- - obb
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- - tracking
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- - yolo11
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  model-index:
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  - name: ultralytics/yolo11
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  results:
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  - task:
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  type: object-detection
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  dataset:
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- name: coco
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  type: merve/coco
 
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  split: validation
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  metrics:
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- - type: mAP
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- value: 54.7
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- name: mAP@0.5:0.95
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  ---
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- [![Ultralytics YOLO banner](https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png)](https://platform.ultralytics.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=platform_launch&utm_content=banner&utm_term=ultralytics_github)
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-
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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)
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-
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- [![Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml) [![Ultralytics Downloads](https://img.shields.io/pepy/dt/ultralytics?color=blue)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics) [![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/) [![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://www.reddit.com/r/ultralytics/)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- [![Run on Gradient](https://img.shields.io/badge/Run_on-Gradient-blue?logo=paperspace&logoColor=white)](https://console.paperspace.com/github/ultralytics/ultralytics) [![Open In Colab](https://img.shields.io/badge/Open_in-Colab-blue?logo=googlecolab&logoColor=white)](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) [![Open In Kaggle](https://img.shields.io/badge/Open_in-Kaggle-blue?logo=kaggle&logoColor=white)](https://www.kaggle.com/models/ultralytics/yolo11) [![Launch Binder](https://img.shields.io/badge/Launch-Binder-blue?logo=jupyter&logoColor=white)](https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb)
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- [Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at [object detection](https://docs.ultralytics.com/tasks/detect), [tracking](https://docs.ultralytics.com/modes/track), [instance segmentation](https://docs.ultralytics.com/tasks/segment), [semantic segmentation](https://docs.ultralytics.com/tasks/semantic), [image classification](https://docs.ultralytics.com/tasks/classify), and [pose estimation](https://docs.ultralytics.com/tasks/pose) tasks.
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- Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!
53
 
54
- Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).
55
 
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- <a href="https://platform.ultralytics.com/ultralytics/yolo11" target="_blank">
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- <img width="100%" src="https://github.com/user-attachments/assets/a311a4ed-bbf2-43b5-8012-5f183a28a845" alt="YOLO11 performance plots">
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- </a>
 
 
 
 
 
 
 
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- ## 📄 Documentation
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- See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full [Ultralytics Docs](https://docs.ultralytics.com/).
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  <details open>
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  <summary>Install</summary>
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- Install the `ultralytics` package, including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
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- [![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Ultralytics Downloads](https://img.shields.io/pepy/dt/ultralytics?color=blue)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](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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- For alternative installation methods, including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and building from source via Git, please consult the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
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- [![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) [![Ultralytics Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
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  </details>
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@@ -83,61 +95,58 @@ For alternative installation methods, including [Conda](https://anaconda.org/con
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  ### CLI
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- You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:
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  ```bash
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- # Predict using a pretrained YOLO model (e.g., YOLO11n) on an image
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  yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
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  ```
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- The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli) for more examples.
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  ### Python
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- Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https://docs.ultralytics.com/usage/cfg) as the CLI:
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  ```python
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  from ultralytics import YOLO
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- # Load a pretrained YOLO11n model
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  model = YOLO("yolo11n.pt")
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- # Train the model on the COCO8 dataset for 100 epochs
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  train_results = model.train(
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- data="coco8.yaml", # Path to dataset configuration file
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- epochs=100, # Number of training epochs
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- imgsz=640, # Image size for training
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- device="cpu", # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
111
  )
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113
- # Evaluate the model's performance on the validation set
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  metrics = model.val()
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116
  # Perform object detection on an image
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- results = model("path/to/image.jpg") # Predict on an image
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- results[0].show() # Display results
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- # Export the model to ONNX format for deployment
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- path = model.export(format="onnx") # Returns the path to the exported model
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  ```
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- Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python).
125
 
126
  </details>
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- ## ✨ Models
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- Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3) to the latest [YOLO26](https://docs.ultralytics.com/models/yolo26). The tables below showcase YOLO11 models pretrained on [COCO](https://docs.ultralytics.com/datasets/detect/coco) for [Detection](https://docs.ultralytics.com/tasks/detect), [Segmentation](https://docs.ultralytics.com/tasks/segment), and [Pose Estimation](https://docs.ultralytics.com/tasks/pose), and [Classification](https://docs.ultralytics.com/tasks/classify) models are pretrained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet). [Tracking](https://docs.ultralytics.com/modes/track) mode is compatible with Detection, Segmentation, and Pose models. All [Models](https://docs.ultralytics.com/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
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- <a href="https://docs.ultralytics.com/tasks" target="_blank">
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- <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
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- </a>
135
- <br>
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- <br>
137
 
138
  <details open><summary>Detection (COCO)</summary>
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140
- Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect) for usage examples. These models are trained on the [COCO dataset](https://cocodataset.org/), featuring 80 object classes.
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  | Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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  | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@@ -147,14 +156,14 @@ Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect) for usag
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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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150
- - **mAP<sup>val</sup>** values refer to single-model single-scale performance on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics) for details. <br>Reproduce with `yolo val detect data=coco.yaml device=0`
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- - **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`
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153
  </details>
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155
  <details><summary>Segmentation (COCO)</summary>
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157
- Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment) for usage examples. These models are trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco), including 80 classes.
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  | Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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  | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@@ -164,16 +173,16 @@ Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment) for
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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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167
- - **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics) for details. <br>Reproduce with `yolo val segment data=coco.yaml device=0`
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- - **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`
169
 
170
  </details>
171
 
172
  <details><summary>Classification (ImageNet)</summary>
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174
- Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify) for usage examples. These models are trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet), covering 1000 classes.
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- | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 224 |
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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 |
@@ -181,14 +190,14 @@ Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify) f
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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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184
- - **acc** values represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce with `yolo val classify data=path/to/ImageNet device=0`
185
- - **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
186
 
187
  </details>
188
 
189
  <details><summary>Pose (COCO)</summary>
190
 
191
- See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose) for usage examples. These models are trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco), focusing on the 'person' class.
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193
  | Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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  | ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@@ -198,14 +207,14 @@ See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose) for usag
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  | [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.2 | 90.7 |
199
  | [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 203.3 |
200
 
201
- - **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics) for details. <br>Reproduce with `yolo val pose data=coco-pose.yaml device=0`
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- - **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
203
 
204
  </details>
205
 
206
- <details><summary>Oriented Bounding Boxes (DOTAv1)</summary>
207
 
208
- Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb) for usage examples. These models are trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2#dota-v10), including 15 classes.
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210
  | Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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  | -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@@ -215,39 +224,71 @@ Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb) for usage examples.
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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 |
216
  | [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 |
217
 
218
- - **mAP<sup>test</sup>** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html). <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https://captain-whu.github.io/DOTA/evaluation.html).
219
- - **Speed** metrics are averaged over [DOTAv1 val images](https://docs.ultralytics.com/datasets/obb/dota-v2#dota-v10) using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
220
 
221
  </details>
222
 
223
- ## 🧩 Integrations
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225
- Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases), [Comet ML](https://docs.ultralytics.com/integrations/comet), [Roboflow](https://docs.ultralytics.com/integrations/roboflow), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations).
226
 
227
- <a href="https://platform.ultralytics.com" target="_blank">
228
- <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
229
- </a>
 
 
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- ## 🤝 Contribute
 
 
 
 
 
 
 
 
 
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- We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. We also welcome your feedback—share your experience by completing our [Survey](https://www.ultralytics.com/survey?utm_source=huggingface&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes!
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- <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=1280 -->
 
 
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- [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
238
 
239
- We look forward to your contributions to help make the Ultralytics ecosystem even better!
240
 
241
- ## 📜 License
 
242
 
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- Ultralytics offers two licensing options to suit different needs:
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245
- - **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-3.0) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for full details.
246
- - **Ultralytics Enterprise License**: For development and production use, this license enables seamless integration of Ultralytics software and AI models into business products and services, including internal tools, automated workflows, and production deployments, bypassing the open-source requirements of AGPL-3.0. To get started, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
247
 
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- ## 📞 Contact
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250
- For bug reports and feature requests related to Ultralytics software, please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For questions, discussions, and community support, join our active communities on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/). We're here to help with all things Ultralytics!
 
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- <br>
 
 
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  - ar
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  library_name: ultralytics
 
 
 
 
 
 
 
 
 
 
 
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  model-index:
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  - name: ultralytics/yolo11
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  results:
19
  - task:
20
  type: object-detection
21
  dataset:
 
22
  type: merve/coco
23
+ name: coco
24
  split: validation
25
  metrics:
26
+ - type: precision # since mAP@0.5:0.95 is not available on hf.co/metrics
27
+ value: 54.7
28
+ name: mAP@0.5:0.95
29
  ---
30
 
31
+ <div align="center">
32
+ <p>
33
+ <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
34
+ <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision banner"></a>
35
+ </p>
36
+
37
+ [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>
38
+
39
+ <div style="display: flex; justify-content: center; flex-wrap: wrap; gap: 5px;">
40
+ <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>
41
+ <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
42
+ <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>
43
+ <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>
44
+ <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>
45
+ <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>
46
+ <br>
47
+ <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>
48
+ <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>
49
+ <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>
50
+ </div>
51
+ <br>
52
 
53
+ [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
54
 
55
+ We hope that the resources here will help you get the most out of YOLO. Please browse the Ultralytics <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, questions, or discussions, become a member of the Ultralytics <a href="https://discord.com/invite/ultralytics">Discord</a>, <a href="https://reddit.com/r/ultralytics">Reddit</a> and <a href="https://community.ultralytics.com/">Forums</a>!
56
 
57
+ To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
58
 
59
+ <img width="100%" src="https://github.com/user-attachments/assets/a311a4ed-bbf2-43b5-8012-5f183a28a845" alt="YOLO11 performance plots"></a>
60
 
61
+ <div style="display: flex; justify-content: center; flex-wrap: wrap;">
62
+ <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="35%" alt="Ultralytics GitHub"></a>
63
+ <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="35%" alt="Ultralytics LinkedIn"></a>
64
+ <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="35%" alt="Ultralytics Twitter"></a>
65
+ <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="35%" alt="Ultralytics YouTube"></a>
66
+ <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="35%" alt="Ultralytics TikTok"></a>
67
+ <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="35%" alt="Ultralytics BiliBili"></a>
68
+ <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="35% alt="Ultralytics Discord"></a>
69
+ </div>
70
+ </div>
71
 
72
+ ## <div align="center">Documentation</div>
73
 
74
+ See below for a quickstart install and usage examples, and see our [Docs](https://docs.ultralytics.com/) for full documentation on training, validation, prediction and deployment.
75
 
76
  <details open>
77
  <summary>Install</summary>
78
 
79
+ Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
80
 
81
+ [![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/)
82
 
83
  ```bash
84
  pip install ultralytics
85
  ```
86
 
87
+ For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart/).
88
 
89
+ [![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)
90
 
91
  </details>
92
 
 
95
 
96
  ### CLI
97
 
98
+ YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` command:
99
 
100
  ```bash
 
101
  yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
102
  ```
103
 
104
+ `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
105
 
106
  ### Python
107
 
108
+ YOLO may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
109
 
110
  ```python
111
  from ultralytics import YOLO
112
 
113
+ # Load a model
114
  model = YOLO("yolo11n.pt")
115
 
116
+ # Train the model
117
  train_results = model.train(
118
+ data="coco8.yaml", # path to dataset YAML
119
+ epochs=100, # number of training epochs
120
+ imgsz=640, # training image size
121
+ device="cpu", # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu
122
  )
123
 
124
+ # Evaluate model performance on the validation set
125
  metrics = model.val()
126
 
127
  # Perform object detection on an image
128
+ results = model("path/to/image.jpg")
129
+ results[0].show()
130
 
131
+ # Export the model to ONNX format
132
+ path = model.export(format="onnx") # return path to exported model
133
  ```
134
 
135
+ See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.
136
 
137
  </details>
138
 
139
+ ## <div align="center">Models</div>
140
 
141
+ YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
142
 
143
+ <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
144
+
145
+ All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 
 
146
 
147
  <details open><summary>Detection (COCO)</summary>
148
 
149
+ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
150
 
151
  | Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
152
  | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
 
156
  | [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 |
157
  | [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 |
158
 
159
+ - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
160
+ - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
161
 
162
  </details>
163
 
164
  <details><summary>Segmentation (COCO)</summary>
165
 
166
+ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), which include 80 pre-trained classes.
167
 
168
  | Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
169
  | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
 
173
  | [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 |
174
  | [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 |
175
 
176
+ - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
177
+ - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
178
 
179
  </details>
180
 
181
  <details><summary>Classification (ImageNet)</summary>
182
 
183
+ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
184
 
185
+ | Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
186
  | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
187
  | [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 |
188
  | [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 |
 
190
  | [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 |
191
  | [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 |
192
 
193
+ - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
194
+ - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
195
 
196
  </details>
197
 
198
  <details><summary>Pose (COCO)</summary>
199
 
200
+ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples with these models trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), which include 1 pre-trained class, person.
201
 
202
  | Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
203
  | ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
 
207
  | [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.2 | 90.7 |
208
  | [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 203.3 |
209
 
210
+ - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
211
+ - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
212
 
213
  </details>
214
 
215
+ <details><summary>OBB (DOTAv1)</summary>
216
 
217
+ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
218
 
219
  | Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
220
  | -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
 
224
  | [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 |
225
  | [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 |
226
 
227
+ - **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
228
+ - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
229
 
230
  </details>
231
 
232
+ ## <div align="center">Integrations</div>
233
 
234
+ Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
235
 
236
+ <br>
237
+ <a href="https://www.ultralytics.com/hub" target="_blank">
238
+ <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
239
+ <br>
240
+ <br>
241
 
242
+ <div style="display: flex; justify-content: center; flex-wrap: wrap; gap: 50px;">
243
+ <a href="https://roboflow.com/?ref=ultralytics">
244
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="50%" alt="Roboflow logo"></a>
245
+ <a href="https://clear.ml/">
246
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="50%" alt="ClearML logo"></a>
247
+ <a href="https://bit.ly/yolov8-readme-comet">
248
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="50%" alt="Comet ML logo"></a>
249
+ <a href="https://bit.ly/yolov5-neuralmagic">
250
+ <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="50%" alt="NeuralMagic logo"></a>
251
+ </div>
252
 
 
253
 
254
+ | Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
255
+ | :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
256
+ | 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) |
257
 
258
+ ## <div align="center">Ultralytics HUB</div>
259
 
260
+ Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLO11 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
261
 
262
+ <a href="https://www.ultralytics.com/hub" target="_blank">
263
+ <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
264
 
265
+ ## <div align="center">Contribute</div>
266
 
267
+ We love your input! Ultralytics YOLO would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
 
268
 
269
+ <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
270
 
271
+ <a href="https://github.com/ultralytics/ultralytics/graphs/contributors">
272
+ <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
273
 
274
+ ## <div align="center">License</div>
275
+
276
+ Ultralytics offers two licensing options to accommodate diverse use cases:
277
 
278
+ - **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
279
+ - **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://www.ultralytics.com/license).
280
+
281
+ ## <div align="center">Contact</div>
282
+
283
+ For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). Become a member of the Ultralytics [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), or [Forums](https://community.ultralytics.com/) for asking questions, sharing projects, learning discussions, or for help with all things Ultralytics!
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+
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+ <br>
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+ <div style="display: flex; justify-content: center; flex-wrap: wrap; gap: 5px; ">
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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="35%" alt="Ultralytics GitHub"></a>
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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="35%" alt="Ultralytics LinkedIn"></a>
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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="35%" alt="Ultralytics Twitter"></a>
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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="35%" alt="Ultralytics YouTube"></a>
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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="35%" alt="Ultralytics TikTok"></a>
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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="35%" alt="Ultralytics BiliBili"></a>
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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="35% alt="Ultralytics Discord"></a>
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+ </div>