Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
YOLOv3: An Incremental Improvement
5.881
Zitationen
2
Autoren
2018
Jahr
Abstract
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/
Ähnliche Arbeiten
Deep Residual Learning for Image Recognition
2016 · 215.868 Zit.
ImageNet: A large-scale hierarchical image database
2009 · 60.394 Zit.
Distinctive Image Features from Scale-Invariant Keypoints
2004 · 54.667 Zit.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
2016 · 52.596 Zit.
Going deeper with convolutions
2015 · 46.236 Zit.