code_test/configs/seesaw_loss

• Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)
• • Introduction
  • Results and models of Seasaw Loss on LVIS v1 dataset

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