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nas_fpn

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) Inf time (fps) box AP Download
R-50-FPN 50e 12.9 22.9 37.9 model | log
R-50-NASFPN 50e 13.2 23.0 40.5 model | log

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