![image](https://private-user-images.githubusercontent.com/55998816/285648138-7e387afd-bc1d-4de7-98af-f2e35bb5fb1e.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CDrv0MtaWLtERyiKDije9dZIpr8h5RujUFeErmGJ0MA)
DiffusionDet is the first work of diffusion model for object detection.
DiffusionDet: Diffusion Model for Object Detection
Shoufa Chen, Peize Sun, Yibing Song, Ping Luo
arXiv 2211.09788
- (11/2022) Code is released.
Method | Box AP (1 step) | Box AP (4 step) | Download |
---|---|---|---|
COCO-Res50 | 45.5 | 46.1 | model |
COCO-Res101 | 46.6 | 46.9 | model |
COCO-SwinBase | 52.3 | 52.7 | model |
LVIS-Res50 | 30.4 | 31.8 | model |
LVIS-Res101 | 31.9 | 32.9 | model |
LVIS-SwinBase | 40.6 | 41.9 | model |
The installation instruction and usage are in Getting Started with DiffusionDet.
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.
If you use DiffusionDet in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.
@article{chen2022diffusiondet,
title={DiffusionDet: Diffusion Model for Object Detection},
author={Chen, Shoufa and Sun, Peize and Song, Yibing and Luo, Ping},
journal={arXiv preprint arXiv:2211.09788},
year={2022}
}