DiffusionInst: Diffusion Model for Instance Segmentation

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: [email protected] or [email protected]