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# 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) | | |