This project is based on the open source object detection toolbox MMDetection, please refer to Installation for installation instructions first.
The benchmark experiments work with Python 3.8, PyTorch 1.10 and mmdet 2.23.0, and corresponding configs can be found at sodad-benchmarks.
The data preparation of SODA-D is a little different from common detection datasets (e.g., COCO, VOC), where a image split processes is necessary before training and please refer to Split part for more details.
If you use our benchmark in your research, please cite this project.
@ARTICLE{SODA,
author={Cheng, Gong and Yuan, Xiang and Yao, Xiwen and Yan, Kebing and Zeng, Qinghua and Xie, Xingxing and Han, Junwei},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Towards Large-Scale Small Object Detection: Survey and Benchmarks},
year={2023},
volume={45},
number={11},
pages={13467-13488}
}@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal= {arXiv preprint arXiv:1906.07155},
year={2019}
}