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Yolov9 CoreML
YOLOv9 model converted to CoreML format, capable of running efficiently on Apple Neural Engine for object detection tasks.
Downloads 21
Release Time : 6/19/2024
Model Overview
CoreML-converted version of YOLOv9, optimized for Apple devices, suitable for real-time object detection scenarios.
Model Features
Apple Device Optimization
Designed specifically for Apple Neural Engine, runs efficiently on M1/M4 and other Apple chips.
Real-time Detection
Based on YOLOv9 architecture, supports high-frame-rate object detection.
Multi-platform Compatibility
Tested and verified on multiple Apple devices including Mac and iPad.
Model Capabilities
Image Object Detection
Real-time Object Recognition
Multi-category Recognition
Use Cases
Smart Surveillance
Real-time Security Monitoring
Used for real-time detection of suspicious objects or personnel in surveillance footage.
High-accuracy real-time detection
Mobile Applications
AR Application Object Recognition
Enables object recognition in AR scenarios on mobile devices like iPad.
Low-latency mobile detection
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