| # Feature Pyramid Grids | |
| ## Introduction | |
| ```latex | |
| @article{chen2020feature, | |
| title={Feature pyramid grids}, | |
| author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph}, | |
| journal={arXiv preprint arXiv:2004.03580}, | |
| year={2020} | |
| } | |
| ``` | |
| ## Results and Models | |
| We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. | |
| All backbones are Resnet-50 in pytorch style. | |
| | Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | |
| |:------------:|:-----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:-------:|:--------:| | |
| | Faster R-CNN | FPG | 50e | 20.0 | - | 42.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) | | |
| | Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | | |
| | Mask R-CNN | FPG | 50e | 23.2 | - | 42.7 | 37.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) | | |
| | Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 36.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | | |
| | RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) | | |
| | RetinaNet | FPG-chn128 | 50e | | - | | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) | | |
| **Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in | |
| Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision. | |