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Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)

Introduction

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.

Results and models of Seasaw Loss on LVIS v1 dataset

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