YOLO26m is the medium-sized variant of the YOLO26 model family, designed to deliver higher detection accuracy while maintaining efficient real-time performance.
Compared to the smaller YOLO26n and YOLO26s models, YOLO26m offers increased model capacity, making it better suited for more complex scenes and dense object detection. It retains the end-to-end, NMS-free architecture, ensuring a streamlined and deployment-friendly inference pipeline.
YOLO26m is pretrained on the COCO dataset and is commonly used in production scenarios where accuracy is prioritized without sacrificing responsiveness.
Real-time capable on modern GPUs and high-performance systems
Pretrained weights available out of the box
Ultralytics ecosystem support for training, validation, inference, and export
Use Cases
High-quality real-time object detection
Video analytics and monitoring systems
Robotics and autonomous platforms
Industrial inspection and safety applications
Accuracy-focused production deployments
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
