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DiffusionInst: Diffusion Model for Instance Segmentation

PWC PWC

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

Todo list:

  • Release source code.
  • Hyper-paramters tuning.
  • Add Swin-Large backbone.
  • Release trained models.
  • Adding directly filter denoising.

Getting Started

The installation instruction and usage are in Getting Started with DiffusionInst.

Trained Models

We now provide trained models for ResNet-50 and ResNet-101.

https://pan.baidu.com/s/1KEdjNY3CSXWp0VFwkhRKYg, pwd: jhbv.

Model Performance

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 -

Citing DiffusionInst

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}
}

Acknowledgement

Many thanks to the nice work of DiffusionDet @ShoufaChen. Our codes and configs follow DiffusionDet.

Contacts

Please feel free to contact us if you have any problems.

Email: haoxingchen@smail.nju.edu.cn or guzhangxuan.gzx@antgroup.com