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.
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