GoKawiilYOLO26 is an end-to-end object detection and multi-task model family supporting detection, instance segmentation , pose estimation, oriented object detection, and image classification across five size variants from Nano to Extra Large. Released in January 2026, it removes Non-Maximum Suppression for lower latency and drops the Distribution Focal Loss module for better compatibility with edge and low-power hardware. This post covers the architecture, COCO benchmark results, download links, and comparisons to models including RF-DETR , LW-DETR, and D-FINE.
YOLO models are a family of real-time computer vision models designed to handle a wide range of tasks, including object detection, segmentation, pose estimation, classification, and oriented object detection.
Leveraging popular architectures, these models offer exceptional speed and accuracy, making them well-suited for applications across edge devices, cloud APIs, and more.
In this blog, we’ll examine YOLO26, released January 2026, revealing its key improvements, important features, and how it compares to other leading computer vision models.
💡 Roboflow supports YOLO26 for labeling, training, and deployment, learn more
What Is YOLO26?
YOLO26 is a multi-task model family designed to handle a broad range of computer vision tasks, including object detection, instance segmentation, image classification, pose estimation, and oriented object detection. The lineup features multiple size variants Nano (N), Small (S), Medium (M), Large (L), and Extra Large (X) to cater to different performance and deployment needs.
Compared to previous YOLO generations, YOLO26 is optimized for edge deployment, featuring faster CPU inference, a more compact model design, and a simplified architecture for improved compatibility across diverse hardware environments. Notable improvements include decreased latency by removing NMS and results staying consistent in fp16 and fp32, making it possible to run the model in an optimized, low-latency way and get the same high accuracy you saw during training.
⚡ RF-DETR Neural Architecture Search (NAS) is faster and more accurate than YOLO26. Read the blog post here
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