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Open-weight model · Object detection

YOLO11

by Ultralytics Ultralytics/YOLO11

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models 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.

Parameters—
Context—
Weights725.8 MB
Licenseagpl-3.0
AccessOpen weights
Monthly Downloads16.5k

Model Card

By Ultralytics, published under agpl-3.0, revision 8b8ac7d1fae7.

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models 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, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums! Request an Enterprise License for commercial use at Ultralytics Licensing. See below for quickstart installation and usage examples. For comprehensive guidance on…

Read Ultralytics's full model card

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Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models 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, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

Documentation

See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.

Install 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/) [![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/) 
pip install ultralytics
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). [![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)
Usage ### CLI You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command: 
# Predict using a pretrained YOLO model (e.g., YOLO11n) on an image
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
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. ### Python 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: 
from ultralytics import YOLO

# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")

# Train the model on the COCO8 dataset for 100 epochs
train_results = model.train(
    data="coco8.yaml",  # Path to dataset configuration file
    epochs=100,  # Number of training epochs
    imgsz=640,  # Image size for training
    device="cpu",  # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
)

# Evaluate the model's performance on the validation set
metrics = model.val()

# Perform object detection on an image
results = model("path/to/image.jpg")  # Predict on an image
results[0].show()  # Display results

# Export the model to ONNX format for deployment
path = model.export(format="onnx")  # Returns the path to the exported model
Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python).

Models

Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO26. The tables below showcase YOLO11 models pretrained on COCO for Detection, Segmentation, and Pose Estimation, and Classification models are pretrained on ImageNet. Tracking mode is compatible with Detection, Segmentation, and Pose models. All Models download automatically from the latest Ultralytics release on first use.



Detection (COCO) 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. | Model | size
(pixels) | mAPval
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 | - **mAPval** 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.
Reproduce with `yolo val detect data=coco.yaml device=0` - **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.
Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`
Segmentation (COCO) 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. | Model | size
(pixels) | mAPbox
50-95 | mAPmask
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 | - **mAPval** 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.
Reproduce with `yolo val segment data=coco.yaml device=0` - **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.
Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`
Classification (ImageNet) 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. | Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) at 224 | | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ | | [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 represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set.
Reproduce with `yolo val classify data=path/to/ImageNet device=0` - **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.
Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
Pose (COCO) 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. | Model | size
(pixels) | mAPpose
50-95 | mAPpose
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 | - **mAPval** 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.
Reproduce with `yolo val pose data=coco-pose.yaml device=0` - **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.
Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
Oriented Bounding Boxes (DOTAv1) 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. | Model | size
(pixels) | mAPtest
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 | - **mAPtest** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html).
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). - **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.
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 partners like Weights & Biases, Comet ML, Roboflow, and Intel OpenVINO, can optimize your AI workflow. Explore more at Ultralytics Integrations.

Contribute

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 to get started. We also welcome your feedback—share your experience by completing our Survey. A huge Thank Youto everyone who contributes!

We look forward to your contributions to help make the Ultralytics ecosystem even better!

License

Ultralytics offers two licensing options to suit different needs:

  • AGPL-3.0 License: This OSI-approved open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the LICENSE file for full details.
  • 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.

Contact

For bug reports and feature requests related to Ultralytics software, please visit GitHub Issues. For questions, discussions, and community support, join our active communities on Discord, Reddit, and the Ultralytics Community Forums. We're here to help with all things Ultralytics!


Identity and Version

Repository
Ultralytics/YOLO11
Publisher
Ultralytics
Task
Object detection
Modality
Image
Library
ultralytics
Parameters
Not stated by the source
Languages
en, zh, ja, ru, de, fr, es, pt
Revision
8b8ac7d1fae7468f85dbf89670dd66f41f485aab
First published
2024-10-16
Last updated
2026-06-26

Files and Weights

17 files, 725.8 MB in total. The weights are 15 files totalling 725.8 MB in pt.

Weights15 files · 725.8 MB
Documentation1 file · 25.9 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
yolo11l-pose.ptWeights53.2 MB 61921abe1f2e
yolo11l-seg.ptWeights56.1 MB cabe90049795
yolo11l.ptWeights51.4 MB 9ebd0e09d598
yolo11m-pose.ptWeights42.5 MB 29b17eaf3a31
yolo11m-seg.ptWeights45.4 MB eb9a06f63e22
yolo11m.ptWeights40.7 MB d5ffc1a67495
yolo11n-pose.ptWeights6.3 MB 869e83fcdffd
yolo11n-seg.ptWeights6.2 MB 55ed65c56c91
yolo11n.ptWeights5.6 MB 0ebbc80d4a76
yolo11s-pose.ptWeights20.4 MB 1060bda4a270
yolo11s-seg.ptWeights20.7 MB 1caa81c01954
yolo11s.ptWeights19.3 MB 85a76fe86dd8
yolo11x-pose.ptWeights118.5 MB 013c43543b07
yolo11x-seg.ptWeights125.1 MB 4e53a5f5fd3e
yolo11x.ptWeights114.6 MB 7bc158aa95c0
README.mdDocumentation25.9 KB —
.gitattributesRepository1.5 KB —

License and Download

License
agpl-3.0
Access
Open weights, no gate
Download size
725.8 MB
Download from Ultralytics

Released by Ultralytics through its official repository on Hugging Face. Read the license.

Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

BenchmarkConditionsResultReported byRevisionDate
coco Task object-detectionMetric [email protected]:0.95Comparison conditions not established 54.7 Ultralytics
Publisher reported		 Evaluated revision not stated —

Memory Requirements

PrecisionWeights in memory
As published725.8 MB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Built on This Model

Questions About YOLO11

Can I use YOLO11 commercially?

Yes, with conditions. YOLO11 is released under GNU Affero General Public License 3.0. The AGPL 3.0 is a strong copyleft license. Commercial use is allowed, but a modified version made available to users over a network must be released with its source code under the same license.

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