[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)

Open Ultralytics In Kaggle

[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. We hope that the resources here will help you get the most out of YOLO. Please browse the Ultralytics Docs for details, raise an issue on GitHub for support, questions, or discussions, become a member of the Ultralytics Discord, Reddit and Forums! To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license). YOLO11 performance plots

YOLO11 performance plots

##

Documentation

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.

Install

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

Usage

### CLI YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` command: ```bash yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg' ``` `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. ### Python 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: ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n.pt") # Train the model train_results = model.train( data="coco8.yaml", # path to dataset YAML epochs=100, # number of training epochs imgsz=640, # training image size device="cpu", # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu ) # Evaluate model performance on the validation set metrics = model.val() # Perform object detection on an image results = model("path/to/image.jpg") results[0].show() # Export the model to ONNX format path = model.export(format="onnx") # return path to exported model ``` See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.

##

Models

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.

Ultralytics YOLO supported tasks

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.

Detection (COCO)

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. | Model | size
^{(pixels) | mAP^{val
50-95 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |

- **mAP^val** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`}}}}}}

Segmentation (COCO)

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. | Model | size
^{(pixels) | mAP^{box
50-95 | mAP^{mask
50-95 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4 |
| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5 |
| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3 |
| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2 |
| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0 |

- **mAP^val** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`}}}}}}}

Classification (ImageNet)

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. | Model | size
^{(pixels) | acc
^{top1 | acc
^{top5 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 3.3 |
| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 12.1 |
| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 39.3 |
| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 12.9 | 49.4 |
| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 28.4 | 110.4 |

- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`}}}}}}}

Pose (COCO)

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. | Model | size
^{(pixels) | mAP^{pose
50-95 | mAP^{pose
50 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.6 |
| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.2 |
| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.7 |
| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.2 | 90.7 |
| [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 |

- **mAP^val** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset.
Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`}}}}}}}

OBB (DOTAv1)

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. | Model | size
^{(pixels) | mAP^{test
50 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 17.2 |
| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.5 |
| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 183.5 |
| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.2 | 232.0 |
| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 520.2 |

- **mAP^test** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset.
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).
- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`}}}}}}

##

Integrations

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.

Ultralytics active learning integrations

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

Ultralytics HUB

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!

Ultralytics HUB preview image

##

Contribute

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!

Ultralytics open-source contributors

##

License

Ultralytics offers two licensing options to accommodate diverse use cases: - **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. - **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). ##

Contact

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!