| # NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | |
| ## Introduction | |
| [ALGORITHM] | |
| ```latex | |
| @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](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) | | |
| | R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) | | |
| **Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower. | |