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FPG

Feature Pyramid Grids

Abstract

Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost, highlighting importance of deep pyramid representations. In addition to its general and uniform structure, over complicated structures that have been found with neural architecture search, it also compares favorably against such approaches without relying on search. We hope that FPG with its uniform and effective nature can serve as a strong component for future work in object recognition.

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 - 42.3 - config model | log
Faster R-CNN FPG-chn128 50e 11.9 - 41.2 - config model | log
Faster R-CNN FPN 50e 20.0 - 38.9 - config model | log
Mask R-CNN FPG 50e 23.2 - 43.0 38.1 config model | log
Mask R-CNN FPG-chn128 50e 15.3 - 41.7 37.1 config model | log
Mask R-CNN FPN 50e 23.2 - 49.6 35.6 config model | log
RetinaNet FPG 50e 20.8 - 40.5 - config model | log
RetinaNet FPG-chn128 50e 19.9 - 39.9 - config model | log

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.

Citation

@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}
}