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Ultralytics YOLO for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Project description

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Ultralytics YOLO11 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.

YOLO11 performance plots

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Documentation

See below for a quickstart install and usage examples, and see our Docs for full documentation on training, validation, prediction and deployment.

Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

PyPI - Version Downloads PyPI - Python Version

pip install ultralytics

For alternative installation methods including Conda, Docker, and Git, please refer to the Quickstart Guide.

Conda Version Docker Image Version

Usage

CLI

YOLO may be used directly in the Command Line Interface (CLI) with a yolo command:

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 for examples.

Python

YOLO may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

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 for more examples.

Models

YOLO11 Detect, Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLO11 Classify models pretrained on the ImageNet dataset. Track mode is available for all Detect, Segment and Pose models.

Ultralytics YOLO supported tasks

All Models download automatically from the latest Ultralytics release on first use.

Detection (COCO)

See Detection Docs for usage examples with these models trained on COCO, which include 80 pre-trained classes.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLO11n 640 39.5 56.1 ± 0.8 1.5 ± 0.0 2.6 6.5
YOLO11s 640 47.0 90.0 ± 1.2 2.5 ± 0.0 9.4 21.5
YOLO11m 640 51.5 183.2 ± 2.0 4.7 ± 0.1 20.1 68.0
YOLO11l 640 53.4 238.6 ± 1.4 6.2 ± 0.1 25.3 86.9
YOLO11x 640 54.7 462.8 ± 6.7 11.3 ± 0.2 56.9 194.9
  • mAPval values are for single-model single-scale on COCO val2017 dataset.
    Reproduce by yolo val detect data=coco.yaml device=0
  • Speed averaged over COCO val images using an Amazon EC2 P4d instance.
    Reproduce by yolo val detect data=coco.yaml batch=1 device=0|cpu
Segmentation (COCO)

See Segmentation Docs for usage examples with these models trained on COCO-Seg, which include 80 pre-trained classes.

Model size
(pixels)
mAPbox
50-95
mAPmask
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLO11n-seg 640 38.9 32.0 65.9 ± 1.1 1.8 ± 0.0 2.9 10.4
YOLO11s-seg 640 46.6 37.8 117.6 ± 4.9 2.9 ± 0.0 10.1 35.5
YOLO11m-seg 640 51.5 41.5 281.6 ± 1.2 6.3 ± 0.1 22.4 123.3
YOLO11l-seg 640 53.4 42.9 344.2 ± 3.2 7.8 ± 0.2 27.6 142.2
YOLO11x-seg 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 COCO val2017 dataset.
    Reproduce by yolo val segment data=coco-seg.yaml device=0
  • Speed averaged over COCO val images using an Amazon EC2 P4d instance.
    Reproduce by yolo val segment data=coco-seg.yaml batch=1 device=0|cpu
Classification (ImageNet)

See Classification Docs for usage examples with these models trained on 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 224 70.0 89.4 5.0 ± 0.3 1.1 ± 0.0 1.6 3.3
YOLO11s-cls 224 75.4 92.7 7.9 ± 0.2 1.3 ± 0.0 5.5 12.1
YOLO11m-cls 224 77.3 93.9 17.2 ± 0.4 2.0 ± 0.0 10.4 39.3
YOLO11l-cls 224 78.3 94.3 23.2 ± 0.3 2.8 ± 0.0 12.9 49.4
YOLO11x-cls 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 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 instance.
    Reproduce by yolo val classify data=path/to/ImageNet batch=1 device=0|cpu
Pose (COCO)

See Pose Docs for usage examples with these models trained on COCO-Pose, which include 1 pre-trained class, person.

Model size
(pixels)
mAPpose
50-95
mAPpose
50
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLO11n-pose 640 50.0 81.0 52.4 ± 0.5 1.7 ± 0.0 2.9 7.6
YOLO11s-pose 640 58.9 86.3 90.5 ± 0.6 2.6 ± 0.0 9.9 23.2
YOLO11m-pose 640 64.9 89.4 187.3 ± 0.8 4.9 ± 0.1 20.9 71.7
YOLO11l-pose 640 66.1 89.9 247.7 ± 1.1 6.4 ± 0.1 26.2 90.7
YOLO11x-pose 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 COCO Keypoints val2017 dataset.
    Reproduce by yolo val pose data=coco-pose.yaml device=0
  • Speed averaged over COCO val images using an Amazon EC2 P4d instance.
    Reproduce by yolo val pose data=coco-pose.yaml batch=1 device=0|cpu
OBB (DOTAv1)

See OBB Docs for usage examples with these models trained on DOTAv1, which include 15 pre-trained classes.

Model size
(pixels)
mAPtest
50
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLO11n-obb 1024 78.4 117.6 ± 0.8 4.4 ± 0.0 2.7 17.2
YOLO11s-obb 1024 79.5 219.4 ± 4.0 5.1 ± 0.0 9.7 57.5
YOLO11m-obb 1024 80.9 562.8 ± 2.9 10.1 ± 0.4 20.9 183.5
YOLO11l-obb 1024 81.0 712.5 ± 5.0 13.5 ± 0.6 26.2 232.0
YOLO11x-obb 1024 81.3 1408.6 ± 7.7 28.6 ± 1.0 58.8 520.2
  • mAPtest values are for single-model multiscale on DOTAv1 dataset.
    Reproduce by yolo val obb data=DOTAv1.yaml device=0 split=test and submit merged results to DOTA evaluation.
  • Speed averaged over DOTAv1 val images using an Amazon EC2 P4d 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, ClearML, Comet, Neural Magic and 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 Automatically track, visualize and even remotely train YOLO11 using ClearML (open-source!) Free forever, 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

Ultralytics HUB

Experience seamless AI with Ultralytics 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. 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 to get started, and fill out our 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 open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the 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.

Contact

For Ultralytics bug reports and feature requests please visit GitHub Issues. Become a member of the Ultralytics Discord, Reddit, or Forums for asking questions, sharing projects, learning discussions, or for help with all things Ultralytics!


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