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 | Config | Download |
|---|---|---|---|---|---|---|
| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | config | model | log |
| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | config | 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.