NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

Introduction

@inproceedings{ghiasi2019fpn,
  title={Nas-fpn: Learning scalable feature pyramid architecture for object detection},
  author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7036--7045},
  year={2019}
}

Results and Models

We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper.

Backbone	Lr schd	Mem (GB)	Train time (s/iter)	Inf time (fps)	box AP	Download
R-50-FPN	50e	12.8	0.513	15.3	37.0	model
R-50-NASFPN	50e	14.8	0.662	13.1	39.8	model

Note: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower.