This repo contains the supported code and configuration files to reproduce object detection results of Disentangle Your Dense Object Detector. It is based on mmdetection.
| Model | Backbone | Lr Schd | box mAP | AP50 | AP75 | APs | APm | APl |
|---|---|---|---|---|---|---|---|---|
| ATSS(IoU) | ResNet50 | 1x | 39.4 | 56.6 | 42.6 | 23.9 | 42.5 | 49.6 |
| DDOD | ResNet50 | 1x | 41.6 | 59.9 | 45.2 | 23.9 | 44.9 | 54.4 |
| DDOD-FCOS | ResNet50 | 1x | 41.6 | 59.9 | 45.3 | 24.0 | 44.6 | 54.8 |
Please refer to get_started.md for installation and dataset preparation.
# multi-gpu testing
tools/dist_test.sh coco_cfg/ddod_r50_1x.py <DET_CHECKPOINT_FILE> 8 --eval bbox
To train a detector with pre-trained models, run:
# multi-gpu training
tools/dist_train.sh coco_cfg/ddod_r50_1x.py 8
@misc{chen2021disentangle,
title={Disentangle Your Dense Object Detector},
author={Zehui Chen and Chenhongyi Yang and Qiaofei Li and Feng Zhao and Zhengjun Zha and Feng Wu},
year={2021},
eprint={2107.02963},
archivePrefix={arXiv},
primaryClass={cs.CV}
}