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Jerry Jiarui XU ae3b3c3ffc | 3 years ago | |
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README.md | 3 years ago | |
retinanet_r50_fpn_crop640_50e_coco.py | 3 years ago | |
retinanet_r50_nasfpn_crop640_50e_coco.py | 3 years ago |
@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}
}
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.
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