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Ultralytics: YOLO11

YOLO11 Overview

YOLO11 is an object detection and multi-task vision model developed by Ultralytics, released in September 2024 under the AGPL-3.0 license. It is the latest generation in the Ultralytics YOLO series and supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a single unified framework. YOLO11 introduces architectural refinements that improve accuracy while reducing parameter count compared to YOLOv8 at equivalent model sizes.

YOLO11 is available in five model sizes from Nano to Extra Large and is deployable through the Ultralytics Python package, Roboflow Inference, and export formats including ONNX, TensorRT, and CoreML. It supports fine-tuning on custom datasets through the standard Ultralytics training API.

YOLO11 Interactive Demo

YOLO11 Details & Performance

Details

Resources

GitHubColabTutorialVideo

Vision Tasks

Instance SegmentationObject Detection

Features

Real-Time Vision

Usage

Past 30 Days

Performance

Avg. Latency

Arena Rankings

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YOLO26
YOLO26 is a real-time object detection model developed by Ultralytics, released in October 2025. It introduces a native end-to-end, NMS-free architecture that eliminates the Non-Maximum Suppression post-processing step, reducing CPU latency by up to 43% for the Nano variant compared to NMS-dependent versions. The model incorporates the MuSGD optimizer and ProgLoss with STAL for improved training stability and small-object detection, and removes Distribution Focal Loss to ensure maximum compatibility with ONNX and TensorRT export targets.YOLO26 supports object detection, instance segmentation, pose estimation, and oriented bounding box detection within a unified framework, with model sizes available from Nano to Extra Large. Its NMS-free design makes it particularly well suited for deployment scenarios where post-processing overhead is a bottleneck, such as embedded systems and real-time edge inference pipelines.
YOLOv12
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YOLOv9
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YOLOE
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RT-DETR
RT-DETR (Real-Time Detection Transformer) is an object detection model developed by Baidu, released in April 2023 under the Apache 2.0 license. It is the first transformer-based real-time object detector, addressing the inference speed limitations of earlier DETR models through an efficient hybrid encoder that decouples intra-scale interaction and cross-scale fusion, enabling the model to process multi-scale features without the high computational overhead of standard transformer encoders.RT-DETR achieves 53.1% AP on COCO at 108 FPS on an NVIDIA T4 GPU for the RT-DETR-L variant, outperforming comparably sized YOLO detectors at similar speeds. It maintains end-to-end inference without non-maximum suppression, simplifying deployment pipelines. RT-DETR established the baseline for real-time transformer detection and has been extended by subsequent works including RF-DETR and RT-DETRv2.

YOLO11 License

AGPL-3.0

License terms and commercial-use guidance for YOLO11.

YOLO11 is licensed under an AGPL-3.0 license.

This means that YOLO11 is available for private and commercial use, but that you will need to follow specific conditions. For commercial use, you need to follow AGPL-3.0 conditions or purchase a license for commercial use, modifications, and distribution.

Using an AGPL-3.0 licensed model is conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available.

Read the full AGPL-3.0 license ↗

To use YOLO11 in a commercial project without the AGPL-3.0 conditions, you need a license. As a paid Roboflow customer, you automatically get access to use any YOLO11 models trained on or uploaded to our platform for commercial use. This is secured under Roboflow's sub-license agreement with Ultralytics, the creators of YOLO11.

If you are a free Roboflow customer, you can use YOLO11 in any way if using our serverless hosted API and can use YOLO11 models commercially self-hosted with a paid plan.

To learn more about model licensing with Roboflow, refer to our Licensing guide.

AGPL-3.0 closes the "SaaS loophole" in GPL-3.0: even hosting the model behind an API counts as distribution and triggers the source-disclosure requirement.

License information is provided as a guide and is not legal advice.

Frequently Asked Questions About YOLO11

YOLO11 is a pretrained computer vision model for object detection and instance segmentation. Unlike a general vision language model, it returns structured predictions for its task rather than free text.

YOLO11 comes in 5 sizes: Nano (640×640), Small (1280×1280), Medium (1280×1280), Large (1280×1280), XL (1280×1280). Smaller variants run faster on constrained hardware; larger ones trade speed for accuracy. You can switch sizes in the demo to compare them on the same image.

Yes. The demo on this page runs YOLO11 in the free Roboflow Playground: upload an image and see results in seconds. A free account unlocks unlimited runs.