| # Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | |
| ## Introduction | |
| [ALGORITHM] | |
| ``` | |
| @article{DynamicRCNN, | |
| author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen}, | |
| title = {Dynamic {R-CNN}: Towards High Quality Object Detection via Dynamic Training}, | |
| journal = {arXiv preprint arXiv:2004.06002}, | |
| year = {2020} | |
| } | |
| ``` | |
| ## Results and Models | |
| | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | |
| |:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| | |
| | R-50 | pytorch | 1x | 3.8 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | | |