What is YOLOv10 | Architecture Enhancements made in YOLOv10 | YOLOv10 Paper Explained

YOLOv10 is the new state of the art real time object detection model that outperforms all the other object detection in terms of Average Precision (AP), parameters efficiency and inference speed. YOLOv10 adopts a consistent dual assignment strategy that eliminates the need of Non Maximum Suppression (NMS) during inference and significantly reduces the inference latency while maintaining a competitive performance. Earlier YOLO model rely on NMS for post processing during inference, which leads to inefficiencies and results in increased inference latency.
YOLOv10 also adopts efficiency-driven design strategy which involves optimizing various components of the model to reduce the computational overhead and enhance performance.
Paper: arxiv.org/pdf/2405.14458
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