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Paper · 2405.14458 · NeurIPS · 2024

YOLOv10: Real-Time End-to-End Object Detection

Kai Chen, Jungong Han, Guiguang Ding, Hui Chen, Ao Wang, Lihao Liu, Zijia Lin

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
ibaiGorordo/ONNX-YOLOv10-Object-Detection pwc_unofficial 3 of 3
FunctionStatusWhere it lives
draw_box Ran ibaiGorordo/ONNX-YOLOv10-Object-Detection/yolov10/utils.py
code served (permissive licence) · get_code("b8d4eb0708db9812")
draw_detections Ran ibaiGorordo/ONNX-YOLOv10-Object-Detection/yolov10/utils.py
code served (permissive licence) · get_code("c9dab4a605d6462a")
draw_text Ran ibaiGorordo/ONNX-YOLOv10-Object-Detection/yolov10/utils.py
code served (permissive licence) · get_code("8bd590382607dfcb")

Repositories linked to this paper

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Abstract

example, our YOLOv10-S is 1.8× faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8× smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46% less latency and 25% fewer parameters for the same performance. Code and models are available at https://github.com/THU-MIG/yolov10.

For agents

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get_code_for_paper("2405.14458")
have("2405.14458")

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