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S2ANet-custom-dataset

A repo to show how to use custom dataset to train s2anet, and change backbone to resnext101

Align Deep Features for Oriented Object Detection

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).

Introduction

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)

Results for UCAS_AOD

Tutorial for custom training

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.

Something Important to be noticed

• 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).

Citation

@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