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.
