Optimization for Oriented Object Detection via Representation Invariance Loss

By Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Xue Yang, and Yunpeng Dong.

The repository hosts the codes for our paper Optimization for Oriented Object Detection via Representation Invariance Loss (paper link), based on mmdetection and s2anet.

Introduction

To be updated.

Installation

conda create -n ridet python=3.7 -y
source activate ridet
conda install pytorch=1.3 torchvision cudatoolkit=10.0 -c pytorch

pip install -r requirements.txt
python setup.py develop
cd mmdet/ops/orn
python setup.py build_ext --inplace

apt-get update
apt-get install swig
apt-get install zip

cd DOTA_devkit
swig -c++ -python polyiou.i
python setup.py build_ext --inplace
cd ..

Getting Started

Datasets

• DOTA
• HRSC2016
• ICDAR2015
• UCAS-AOD
• VOC2007
• MSRA-TD500

Data Preration

cd DOTA_devkit/$DATASET
python prepare_$DATASET.py

Training

Set the following configuration according to your own file directory: $GPUS, $ROOT, $CONFIG, and then start training:

sh train.sh

Testing

Set the following configuration according to your own file directory: $GPUS, $DATASET, $CHECKPOINT, $CONFIG, and then start evaluation:

sh test.sh

Demo

To output the visualization of the detections, the following configuration need to be set: $ROOT, $IMAGES, $CHECKPOINT, $CONFIG, and then start evaluation:

sh demo.sh

Models

All the trained models can be found here with fetch code q9zc.

Notes

The implementation based on mmdetection does not work well on the scene text datasets. Recommend to use my another implementation: RIDet-pytorch.

Citation

To be updated.