YOLO-Master: Adaptive Computation for Real-time Object Detection
Analysis
This paper introduces YOLO-Master, a novel YOLO-like framework that improves real-time object detection by dynamically allocating computational resources based on scene complexity. The use of an Efficient Sparse Mixture-of-Experts (ES-MoE) block and a dynamic routing network allows for more efficient processing, especially in challenging scenes, while maintaining real-time performance. The results demonstrate improved accuracy and speed compared to existing YOLO-based models.
Key Takeaways
- •Proposes YOLO-Master, a novel YOLO-like framework for real-time object detection.
- •Employs an Efficient Sparse Mixture-of-Experts (ES-MoE) block for adaptive computation.
- •Achieves improved accuracy and speed, especially in challenging scenes.
- •Outperforms existing YOLO-based models on benchmarks like MS COCO.
Reference
“YOLO-Master achieves 42.4% AP with 1.62ms latency, outperforming YOLOv13-N by +0.8% mAP and 17.8% faster inference.”