计算机科学
编码(集合论)
泰坦(火箭家族)
人工智能
公制(单位)
膨胀
计算机视觉
物理
工程类
程序设计语言
天文
运营管理
热力学
集合(抽象数据类型)
作者
Joseph Redmon,Ali Farhadi
出处
期刊:Cornell University - arXiv
日期:2018-01-01
被引量:13188
标识
DOI:10.48550/arxiv.1804.02767
摘要
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at https://pjreddie.com/yolo/
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