YOLOv10: Real-Time End-to-End Object Detection
Paper: arxiv.org/pdf/...
YOLOv10, developed by researchers at Tsinghua University introduces a novel approach to real-time object detection. This version addresses deficiencies in both post-processing and model architecture found in earlier YOLO versions. By removing non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments show its superior accuracy-latency trade-offs across multiple model scales.
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