| # AutoAssign: Differentiable Label Assignment for Dense Object Detection | |
| ## Introduction | |
| <!-- [ALGORITHM] --> | |
| ``` | |
| @article{zhu2020autoassign, | |
| title={AutoAssign: Differentiable Label Assignment for Dense Object Detection}, | |
| author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian}, | |
| journal={arXiv preprint arXiv:2007.03496}, | |
| year={2020} | |
| } | |
| ``` | |
| ## Results and Models | |
| | Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | | |
| |:---------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| | |
| | R-50 | pytorch | 1x | 4.08 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.log.json) | | |
| **Note**: | |
| 1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation. | |
| 2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch. | |