# Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

arXiv: https://arxiv.org/abs/2606.03748 · HTML: https://mlsanity.com/paper/2606.03748/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 90 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 30 of 71)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)

## Built on by (most cited first, 30 of 57)

- [ShutterMuse: Capture-Time Photography Guidance with MLLMs](https://mlsanity.com/paper/2606.25763/) (2026)
- [Toward Optimal Adenovirus Detection Using YOLO26](https://mlsanity.com/paper/2607.17799/) (2026)
- [Measuring Gender Representation in Animated Films](https://mlsanity.com/paper/2608.21429/) (2026)
- [A Smart Classroom Behavior Analysis Framework with a New Highly Congested Classroom Dataset](https://mlsanity.com/paper/2606.21568/) (2026)
- [Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality](https://mlsanity.com/paper/2607.10575/) (2026)
- [Edge-Aware Thermal Infrared UAV Swarm Tracking](https://mlsanity.com/paper/2607.12544/) (2026)
- [Cotton-SF YOLO: Learning Structural and Frequency Cues for Early Cotton Square Detection in Complex Field Environments](https://mlsanity.com/paper/2607.14445/) (2026)
- [Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels](https://mlsanity.com/paper/2607.14934/) (2026)
- [Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR](https://mlsanity.com/paper/2607.19040/) (2026)
- [Delineate Anything v2: A Global Foundation Model for Field Delineation](https://mlsanity.com/paper/2607.19069/) (2026)
- [Small-Pollinator Detection in Cluttered Field Video](https://mlsanity.com/paper/2607.22913/) (2026)
- [MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments](https://mlsanity.com/paper/2608.00257/) (2026)
- [RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI](https://mlsanity.com/paper/2608.00508/) (2026)
- [AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles](https://mlsanity.com/paper/2608.06801/) (2026)
- [SkySeaLand: A Wide-Format Satellite Transportation Benchmark with an Ultra-Lightweight Detection Baseline](https://mlsanity.com/paper/2608.07382/) (2026)
- [Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery](https://mlsanity.com/paper/2608.10801/) (2026)
- [CapProbe: Evaluating Detailed Image Captions via Full-Scene Dense Question Answering](https://mlsanity.com/paper/2608.11074/) (2026)
- [AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage](https://mlsanity.com/paper/2608.13343/) (2026)
- [YOLO26-RD: An End-to-End Road Damage Detection Network With Learnable Contrast Enhancement and Edge-Guided Downsampling](https://mlsanity.com/paper/2608.15713/) (2026)
- [A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection](https://mlsanity.com/paper/2608.20608/) (2026)
- [A2DINOv3: Rethinking Multi-Modal Object Detection via Socialized Collaboration](https://mlsanity.com/paper/2608.21099/) (2026)
- [Optimizing YOLO27, YOLO26, YOLO11, and YOLOv8 for Fine-Grained Small-Object Detection and Segmentation in Complex Orchard Environments](https://mlsanity.com/paper/2608.23636/) (2026)
- [TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding](https://mlsanity.com/paper/2608.25935/) (2026)
- [Imag-Eval: a language-grounded framework for interpretable Text-to-Image instruction following evaluation](https://mlsanity.com/paper/2608.29210/) (2026)
- [RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detection](https://mlsanity.com/paper/2608.30709/) (2026)
- [DramaChain Bench: An End-to-End Benchmark for Short-Drama Generation](https://mlsanity.com/paper/2609.00646/) (2026)
- [Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations](https://mlsanity.com/paper/2609.01257/) (2026)
- [Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction](https://mlsanity.com/paper/2609.02251/) (2026)
- [Scalable Detection of Fossil Palynomorphs in Multifocal Digital Microscopy Images](https://mlsanity.com/paper/2609.05323/) (2026)
- [NEO-BENCH: A New Multi-Source Benchmark for Generalizable Astronomical Streak Detection](https://mlsanity.com/paper/2609.06774/) (2026)

## Abstract (arXiv)

Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed for this reason, yet most YOLO detectors still rely on non-maximum suppression at inference, carry heavy detection heads due to Distribution Focal Loss, require long training schedules, and can leave the smallest objects without positive label assignments. We present Ultralytics YOLO26, a unified real-time vision model family that addresses these limitations through coordinated architecture and training advances. YOLO26 uses a dual-head design for native NMS-free end-to-end inference and removes DFL entirely, yielding a lighter head with unconstrained regression range. Its training pipeline combines MuSGD, a hybrid Muon-SGD optimizer adapted from large language model training; Progressive Loss, which shifts supervision toward the inference-time head; and STAL, a label assignment strategy that guarantees positive coverage for small objects. Beyond detection, YOLO26 introduces task-specific head and loss designs for instance segmentation, pose estimation, and oriented detection, producing consistent gains across tasks and scales. The family spans five scales (n/s/m/l/x) and supports detection, instance segmentation, pose estimation, classification, and oriented detection in a single pipeline, with an open-vocabulary extension, YOLOE-26, for text-, visual-, and prompt-free inference. Across all scales, YOLO26 achieves 40.9-57.5 mAP on COCO at 1.7-11.8 ms T4 TensorRT latency, advancing the accuracy-latency Pareto front over prior real-time detectors, while YOLOE-26x reaches 40.6 AP on LVIS minival under text prompting. Code and models are available at https://github.com/ultralytics/ultralytics.