Ultralytics YOLO26

Overview

Ultralytics YOLO26 is the latest evolution in the YOLO series of real-time object detectors, engineered from the ground up for edge and low-power devices. It introduces a streamlined design that removes unnecessary complexity while integrating targeted innovations to deliver faster, lighter, and more accessible deployment.

The architecture of YOLO26 is guided by three core principles:

• Simplicity: YOLO26 is a native end-to-end model, producing predictions directly without the need for non-maximum suppression (NMS). By eliminating this post-processing step, inference becomes faster, lighter, and easier to deploy in real-world systems. This breakthrough approach was first pioneered in YOLOv10 by Ao Wang at Tsinghua University and has been further advanced in YOLO26.
• Deployment Efficiency: The end-to-end design cuts out an entire stage of the pipeline, dramatically simplifying integration, reducing latency, and making deployment more robust across diverse environments.
• Training Innovation: YOLO26 introduces the MuSGD optimizer, a hybrid of SGD and Muon — inspired by Moonshot AI's Kimi K2 breakthroughs in LLM training. This optimizer brings enhanced stability and faster convergence, transferring optimization advances from language models into computer vision.

Together, these innovations deliver a model family that achieves higher accuracy on small objects, provides seamless deployment, and runs up to 43% faster on CPUs — making YOLO26 one of the most practical and deployable YOLO models to date for resource-constrained environments.

Key Features

• DFL Removal
  The Distribution Focal Loss (DFL) module, while effective, often complicated export and limited hardware compatibility. YOLO26 removes DFL entirely, simplifying inference and broadening support for edge and low-power devices.

• End-to-End NMS-Free Inference
  Unlike traditional detectors that rely on NMS as a separate post-processing step, YOLO26 is natively end-to-end. Predictions are generated directly, reducing latency and making integration into production systems faster, lighter, and more reliable.

• ProgLoss + STAL
  Improved loss functions increase detection accuracy, with notable improvements in small-object recognition, a critical requirement for IoT, robotics, aerial imagery, and other edge applications.

• MuSGD Optimizer
  A new hybrid optimizer that combines SGD with Muon. Inspired by Moonshot AI's Kimi K2, MuSGD introduces advanced optimization methods from LLM training into computer vision, enabling more stable training and faster convergence.

• Up to 43% Faster CPU Inference
  Specifically optimized for edge computing, YOLO26 delivers significantly faster CPU inference, ensuring real-time performance on devices without GPUs.

Supported Tasks and Modes

YOLO26 is designed as a multi-task model family, extending YOLO's versatility across diverse computer vision challenges:

This unified framework ensures YOLO26 is applicable across real-time detection, segmentation, classification, pose estimation, and oriented object detection — all with training, validation, inference, and export support.

Performance Metrics

Citations and Acknowledgements

If you use YOLO26 or other Ultralytics software in your work, please cite it as:

@software{yolo26_ultralytics,
  author = {Glenn Jocher and Jing Qiu},
  title = {Ultralytics YOLO26},
  version = {26.0.0},
  year = {2025},
  url = {https://github.com/ultralytics/ultralytics},
  orcid = {0000-0001-5950-6979, 0000-0003-3783-7069},
  license = {AGPL-3.0}
}

DOI pending. YOLO26 is available under AGPL-3.0 and Enterprise licenses.

FAQ

What are the key improvements in YOLO26 compared to YOLO11?

• DFL Removal: Simplifies export and expands edge compatibility
• End-to-End NMS-Free Inference: Eliminates NMS for faster, simpler deployment
• ProgLoss + STAL: Boosts accuracy, especially on small objects
• MuSGD Optimizer: Combines SGD and Muon (inspired by Moonshot's Kimi K2) for more stable, efficient training
• Up to 43% Faster CPU Inference: Major performance gains for CPU-only devices

What tasks will YOLO26 support?

YOLO26 is designed as a unified model family, providing end-to-end support for multiple computer vision tasks:

• Object Detection
• Instance Segmentation
• Image Classification
• Pose Estimation
• Oriented Object Detection (OBB)

Each size variant (n, s, m, l, x) is planned to support all tasks at release.

Why is YOLO26 optimized for edge deployment?

YOLO26 delivers state-of-the-art edge performance with:

• Up to 43% faster CPU inference
• Reduced model size and memory footprint
• Architecture simplified for compatibility (no DFL, no NMS)
• Flexible export formats including TensorRT, ONNX, CoreML, TFLite, and OpenVINO

When will YOLO26 models be available?

YOLO26 models are still in training and not yet open-sourced. Performance previews are shown here, with official downloads and releases planned in the near future. See YOLO Vision 2025 for YOLO26 talks.

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