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Align Deep Features for Oriented Object Detection,
Jiaming Han*, Jian Ding*, Jie Li, Gui-Song Xia†,
arXiv preprint (arXiv:2008.09397) / TGRS (IEEE Xplore).
The repo is based on mmdetection, S2ANet branch pytorch1.9, and UCAS-AOD-benchmark thanks to their work.
Two versions are provided here: Original version and v20210104. We recommend to use v20210104 (i.e. the master branch).
As there is a need for me to run S2ANet on UCAS_AOD. However, there is no present work to do this. This repo is both a tutorial and an extension to original project S2ANet. Besides, I used UCAS-AOD-benchmark to prepare for dataset.
The main problems this repo solved are:
- custom dataset training(UCAS_AOD as an example)
- change the backbone to ResNeXt101x64_4d to gain more performance.(this pretrain model is provided in the link below, after downloading, move it to torch pretrain cache dir)
- a tutorial for begineers in remote-sensing
- provide some pretrained models with baidu Netdisk
- align the accuracy provided in UCAS-AOD-benchmark (The Reason might be training params for I only have RTX3060 12G)
class | ap |
---|---|
car | 80.75557185 |
airplane | 90.64514424 |
pretrained model file can be downloaded here. code: 0lsj |
files to be added :
- DOTA_devkit/ucas_aod_evaluation.py
- mmdet/datasets/UCAS_AOD.py
- tools/test.py
- configs/ucasaod/*
The first one is used when evaluating.
The second one is for loading custom dataset(like this directory in UCAS_AOD_Benchmark).
The third is adding params for evaluating.
The fourth is config file for training.
- 1.the processed dataset anno filed(.txt) have 14 cols, and they are
$class,x_1,y_1,x_2,y_2,x_3,y_3,x_4,y_4,theta,x,y, width,height$ . And theta is angle not arc(see here).
@article{han2021align,
author={J. {Han} and J. {Ding} and J. {Li} and G. -S. {Xia}},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Align Deep Features for Oriented Object Detection},
year={2021},
pages={1-11},
doi={10.1109/TGRS.2021.3062048}}
@inproceedings{xia2018dota,
title={DOTA: A large-scale dataset for object detection in aerial images},
author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3974--3983},
year={2018}
}
@InProceedings{Ding_2019_CVPR,
author = {Ding, Jian and Xue, Nan and Long, Yang and Xia, Gui-Song and Lu, Qikai},
title = {Learning RoI Transformer for Oriented Object Detection in Aerial Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@article{chen2019mmdetection,
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 others},
journal={arXiv preprint arXiv:1906.07155},
year={2019}
}
pytorch1.9