YOLOv7实时物体检测器中的新设计与技术

YOLOv7: New Designs and Techniques for Real-time Object Detection. This paper introduces several trainable detection methods that significantly improve detection accuracy without increasing inference cost, and addresses two new challenges in the development of object detection methods. In addition, techniques for effective use of parameters and computation are proposed, resulting in a 40% reduction in parameters and 50% reduction in computation for real-time object detection with faster inference speeds and higher detection accuracy.