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      "statement": "# YOLOv10: consistent dual assignment for NMS-free inference\n\nJointly trained one-to-many and one-to-one detection heads with consistent matching, discarding the former at inference, alongside component-level efficiency design.\n\n## Scientific scope\n\nInherits YOLO features and earlier one-to-one/end-to-end detection. Removing NMS does not remove all preprocessing, device-transfer or deployment costs. Different model scales are not separate achievements, and source benchmark advantages depend on export/runtime settings.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAo Wang; Hui Chen; Lihao Liu; Kai Chen; Zijia Lin; Jungong Han; Guiguang Ding\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458v1)\n",
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