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

Hugging Face Daily Papers Papers
yolo real-time-vision object-detection instance-segmentation pose-estimation nms-free multi-task

Summary

Ultralytics YOLO26 introduces a unified real-time vision model family with NMS-free inference, improved training strategies, and multi-task capabilities for detection, segmentation, and pose estimation, achieving state-of-the-art accuracy-latency trade-offs.

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.
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Cached at: 06/03/26, 07:39 PM

Paper page - Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

Source: https://huggingface.co/papers/2606.03748

Abstract

YOLO26 addresses real-time vision challenges through a unified model family with NMS-free inference, improved training strategies, and multi-task capabilities spanning detection, segmentation, and pose estimation.

Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. TheYOLOfamily has become widely deployed for this reason, yet mostYOLOdetectors still rely onnon-maximum suppressionat inference, carry heavy detection heads due toDistribution Focal Loss, require long training schedules, and can leave the smallest objects without positive label assignments. We present UltralyticsYOLO26, 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 combinesMuSGD, ahybrid Muon-SGD optimizeradapted from large language model training;Progressive Loss, which shifts supervision toward the inference-time head; andSTAL, a label assignment strategy that guarantees positive coverage for small objects. Beyond detection,YOLO26 introduces task-specific head and loss designs forinstance segmentation,pose estimation, andoriented 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, andoriented detectionin a single pipeline, with anopen-vocabulary extension,YOLOE-26, for text-, visual-, and prompt-free inference. Across all scales,YOLO26 achieves 40.9-57.5mAPonCOCOat 1.7-11.8 ms T4TensorRT latency, advancing the accuracy-latency Pareto front over prior real-time detectors, whileYOLOE-26x reaches 40.6 AP onLVISminival under text prompting. Code and models are available at https://github.com/ultralytics/ultralytics.

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