YOLO26n is the nano-sized variant of the YOLO26 model family, designed for ultra-fast, low-latency object detection on edge and resource-constrained devices.
As the smallest model in the YOLO26 lineup, YOLO26n prioritizes speed, simplicity, and deployability, while maintaining strong detection accuracy. It adopts an end-to-end, NMS-free architecture, which simplifies inference pipelines and reduces post-processing overhead.
YOLO26n is pretrained on the COCO dataset and serves as a lightweight baseline for real-time object detection workloads.
Nano model size optimized for edge and low-power environments
End-to-end detection with NMS-free inference
Real-time capable for latency-sensitive applications
Pretrained weights available out of the box
Ultralytics workflow support for training, validation, inference, and export
Use Cases
Edge and embedded computer vision
Mobile object detection
Smart cameras and IoT applications
Robotics and autonomous systems
Rapid prototyping with minimal compute resources
Inputs and Outputs
Input:
Images or video streams, automatically preprocessed by the Ultralytics framework
Output:
Bounding boxes
Class labels
Confidence scores
License
This repo is licensed under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license, which allows use, sharing, and modification only for non-commercial purposes with proper attribution. All NPU-related models, runtimes, and code in this project are protected under this non-commercial license and cannot be used in any commercial or revenue-generating applications. Commercial licensing or enterprise usage requires a separate agreement. For inquiries, please contact dev@nexa.ai
