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]