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YOLO26s

Model Description

YOLO26s is the small-sized variant of the YOLO26 model family, offering a strong balance between inference speed and detection accuracy.

Positioned above the nano model, YOLO26s provides improved representational capacity while remaining efficient enough for real-time applications on edge, desktop, and entry-level server hardware. It uses an end-to-end, NMS-free architecture, simplifying deployment and reducing inference overhead.

YOLO26s is pretrained on the COCO dataset and is well-suited for production object detection pipelines that require both responsiveness and reliability.

Quickstart

  1. Install NexaSDK and create a free account at sdk.nexa.ai
  2. Activate your device with your access token:
    nexa config set license '<access_token>'
    
  3. Run the model on Qualcomm NPU in one line:
    nexa infer NexaAI/yolo26s-npu
    

Features

  • Small model size with balanced speed and accuracy
  • End-to-end detection with NMS-free inference
  • Real-time capable across a wide range of hardware
  • Pretrained weights available for immediate use
  • Ultralytics ecosystem support for training, validation, inference, and export

Use Cases

  • Real-time video analytics
  • Edge and desktop object detection
  • Smart retail and surveillance systems
  • Robotics and automation
  • Production-ready computer vision applications

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 [email protected]

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