YOLOv12-N

Model Description

YOLOv12-N is a variant of the 12th-generation YOLO (You Only Look Once) real-time object detector.
It builds on prior YOLO models with improved backbone/neck architectures, updated training strategies, and optimizations for both high-performance GPUs and edge devices.

Features

Real-time object detection optimized for low-latency inference.
High accuracy across diverse categories and challenging environments.
Lightweight variants suitable for mobile and embedded deployment.
Scalable: runs from smartphones to multi-GPU servers.
Extensible: fine-tuning supported for domain-specific datasets.

Use Cases

Autonomous driving and ADAS (Advanced Driver Assistance Systems)
Surveillance and security monitoring
Industrial automation and defect detection
Retail analytics and inventory monitoring
Sports analytics and event detection

Inputs and Outputs

Input:

RGB images or video frames (any resolution; auto-resized during preprocessing).

Output:

Bounding boxes (x, y, w, h)
Class labels
Confidence scores

How to use

⚠️ Hardware requirement: the model currently runs only on Qualcomm NPUs (e.g., Snapdragon-powered AIPC).
Apple NPU support is planned next.

1) Install Nexa-SDK

Download and follow the steps under "Deploy Section" Nexa's model page: Download Windows arm64 SDK
(Other platforms coming soon)

2) Get an access token

Create a token in the Model Hub, then log in:

nexa config set license '<access_token>'

3) Run the model

Running:

nexa infer NexaAI/yolov12-npu

License

Licensed under AGPL-3.0 (same as Ultralytics YOLO).

References

Original repo: https://github.com/sunsmarterjie/yolov12
Ultralytics YOLO family: https://github.com/ultralytics/ultralytics

NexaAI
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yolov12-npu