DiffusionInst is the first work of diffusion model for instance segmentation. We hope our work could serve as a simple yet effective baseline, which could inspire designing more efficient diffusion frameworks for challenging discriminative tasks.
DiffusionInst: Diffusion Model for Instance Segmentation
Zhangxuan Gu, Haoxing Chen, Zhuoer Xu, Jun Lan, Changhua Meng, Weiqiang Wang arXiv 2212.02773
The installation instruction and usage are in Getting Started with DiffusionInst.
We now provide trained models for ResNet-50 and ResNet-101.
https://pan.baidu.com/s/1KEdjNY3CSXWp0VFwkhRKYg, pwd: jhbv.
Method | Mask AP (1 step) | Mask AP (4 step) |
---|---|---|
COCO-val-Res50 | 37.3 | 37.5 |
COCO-val-Res101 | 41.0 | 41.1 |
COCO-val-Swin-B | 46.6 | 46.8 |
COCO-val-Swin-L | 47.8 | 47.8 |
LVIS-Res50 | 22.3 | - |
LVIS-Res101 | 27.0 | - |
LVIS-Swin-B | 36.0 | - |
COCO-testdev-Res50 | 37.1 | - |
COCO-testdev-Res101 | 41.5 | - |
COCO-testdev-Swin-B | 47.6 | - |
COCO-testdev-Swin-L | 48.3 | - |
If you use DiffusionInst in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.
@article{DiffusionInst,
title={DiffusionInst: Diffusion Model for Instance Segmentation},
author={Gu, Zhangxuan and Chen, Haoxing and Xu, Zhuoer and Lan, Jun and Meng, Changhua and Wang, Weiqiang},
journal={arXiv preprint arXiv:2212.02773},
year={2022}
}
Many thanks to the nice work of DiffusionDet @ShoufaChen. Our codes and configs follow DiffusionDet.
Please feel free to contact us if you have any problems.
Email: haoxingchen@smail.nju.edu.cn or guzhangxuan.gzx@antgroup.com