ultralytics 8.3.1

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

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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.

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.

pip install ultralytics

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

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.

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	56.12 ± 0.82 ms	1.55 ± 0.01 ms	2.6	6.5
YOLO11s	640	47.0	90.01 ± 1.17 ms	2.46 ± 0.00 ms	9.4	21.5
YOLO11m	640	51.5	183.20 ± 2.04 ms	4.70 ± 0.06 ms	20.1	68.0
YOLO11l	640	53.4	238.64 ± 1.39 ms	6.16 ± 0.08 ms	25.3	86.9
YOLO11x	640	54.7	462.78 ± 6.66 ms	11.31 ± 0.24 ms	56.9	194.9

• mAP^val 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	65.90 ± 1.14 ms	1.84 ± 0.00 ms	2.9	10.4
YOLO11s-seg	640	46.6	37.8	117.56 ± 4.89 ms	2.94 ± 0.01 ms	10.1	35.5
YOLO11m-seg	640	51.5	41.5	281.63 ± 1.16 ms	6.31 ± 0.09 ms	22.4	123.3
YOLO11l-seg	640	53.4	42.9	344.16 ± 3.17 ms	7.78 ± 0.16 ms	27.6	142.2
YOLO11x-seg	640	54.7	43.8	664.50 ± 3.24 ms	15.75 ± 0.67 ms	62.1	319.0

• mAP^val 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

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	52.40 ± 0.51 ms	1.72 ± 0.01 ms	2.9	7.6
YOLO11s-pose	640	58.9	86.3	90.54 ± 0.59 ms	2.57 ± 0.00 ms	9.9	23.2
YOLO11m-pose	640	64.9	89.4	187.28 ± 0.77 ms	4.94 ± 0.05 ms	20.9	71.7
YOLO11l-pose	640	66.1	89.9	247.69 ± 1.10 ms	6.42 ± 0.13 ms	26.2	90.7
YOLO11x-pose	640	69.5	91.1	487.97 ± 13.91 ms	12.06 ± 0.20 ms	58.8	203.3

• mAP^val 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	117.56 ± 0.80 ms	4.43 ± 0.01 ms	2.7	17.2
YOLO11s-obb	1024	79.5	219.41 ± 4.00 ms	5.13 ± 0.02 ms	9.7	57.5
YOLO11m-obb	1024	80.9	562.81 ± 2.87 ms	10.07 ± 0.38 ms	20.9	183.5
YOLO11l-obb	1024	81.0	712.49 ± 4.98 ms	13.46 ± 0.55 ms	26.2	232.0
YOLO11x-obb	1024	81.3	1408.63 ± 7.67 ms	28.59 ± 0.96 ms	58.8	520.2

• mAP^test 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

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	5.03 ± 0.32 ms	1.10 ± 0.01 ms	1.6	3.3
YOLO11s-cls	224	75.4	92.7	7.89 ± 0.18 ms	1.34 ± 0.01 ms	5.5	12.1
YOLO11m-cls	224	77.3	93.9	17.17 ± 0.40 ms	1.95 ± 0.00 ms	10.4	39.3
YOLO11l-cls	224	78.3	94.3	23.17 ± 0.29 ms	2.76 ± 0.00 ms	12.9	49.4
YOLO11x-cls	224	79.5	94.9	41.41 ± 0.94 ms	3.82 ± 0.00 ms	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

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.

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!

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!

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!