We provide config files to reproduce the instance segmentation performance in the CVPR 2021 paper for Seesaw Loss for Long-Tailed Instance Segmentation.
@inproceedings{wang2021seesaw,
title={Seesaw Loss for Long-Tailed Instance Segmentation},
author={Jiaqi Wang and Wenwei Zhang and Yuhang Zang and Yuhang Cao and Jiangmiao Pang and Tao Gong and Kai Chen and Ziwei Liu and Chen Change Loy and Dahua Lin},
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
year={2021}
}
Please setup LVIS dataset for MMDetection.
RFS indicates to use oversample strategy here with oversample threshold 1e-3
.
Method | Backbone | Style | Lr schd | Data Sampler | Norm Mask | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|---|
Mask R-CNN | R-50-FPN | pytorch | 2x | random | N | 25.6 | 25.0 | config | model | log |
Mask R-CNN | R-50-FPN | pytorch | 2x | random | Y | 25.6 | 25.4 | config | model | log |
Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 27.4 | 26.7 | config | model | log |
Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 27.2 | 27.3 | config | model | log |
Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | N | 27.6 | 26.4 | config | model | log |
Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | Y | 27.6 | 26.8 | config | model | log |
Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 28.9 | 27.6 | config | model | log |
Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 28.9 | 28.2 | config | model | log |
Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 33.1 | 29.2 | config | model | log |
Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 33.0 | 30.0 | config | model | log |
Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 30.0 | 29.3 | config | model | log |
Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 32.8 | 30.1 | config | model | log |
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