This project hosts the code for implementing the IterDet scheme for object detection, as presented in our paper:

IterDet: Iterative Scheme for Object Detection in Crowded Environments
Danila Rukhovich, Konstantin Sofiiuk, Danil Galeev, Olga Barinova, Anton Konushin
Samsung AI Center Moscow
https://arxiv.org/abs/2005.05708

This implementation is based on mmdetection framework.

Please refer to original install.md for installation. Do not forget to update the original github repository link, and install requirements.txt. For v1.2.0 release follow v1 branch.

Config files and tools for converting annotations to COCO format are provided for the following datasets:

• AdaptIS ToyV1 and ToyV2
• CrowdHuman
• WiderPerson

Please see original getting_started.md for the basic usage examples. Iterdet configs can be used for train and test scripts:

bash tools/dist_train.sh configs/iterdet/crowd_human_full_faster_rcnn_r50_fpn_2x.py 8 --validate
bash tools/dist_test.sh configs/iterdet/crowd_human_full_faster_rcnn_r50_fpn_2x.py \
    work_dirs/iterdet/crowd_human_full_faster_rcnn_r50_fpn_2x/latest.pth 8

State-of-the-art models for all datasets are trained on top of Faster RCNN based on ResNet-50. Metrics are given for 2 iterations IterDet inference.

The code is released under the MPL 2.0 License. MPL is a copyleft license that is easy to comply with. You must make the source code for any of your changes available under MPL, but you can combine the MPL software with proprietary code, as long as you keep the MPL code in separate files.

If you find this work useful for your research, please cite our paper:

@article{rukhovich2020iterdet,
  title={IterDet: Iterative Scheme for Object Detection in Crowded Environments},
  author={Danila Rukhovich, Konstantin Sofiiuk, Danil Galeev, Olga Barinova, Anton Konushin},
  journal={arXiv preprint arXiv:2005.05708},
  year={2020}
}