This is a fork of the Rotated-RetinaNet implemetation for Google Colab usage. This code was changed to primary support the DOTAv1.5 dataset and a newer Python version.

• Python 3.11.12
• torch 2.6.0+cu124
• cuda_12.5.r12.5/compiler.34385749_0
• gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0

The codes build R-RetinaNet for arbitrary-oriented object detection. It supports the following datasets: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC2007.

Follow the demo.ipynb or follow the next steps one by one.

Build the Cython and CUDA modules:

cd $ROOT/utils
sh make.sh

Install Dota-devkit:

sudo apt-get install swig
swig -c++ -python polyiou.i
python setup.py build_ext --inplace

python demo.py --weight None

1. Prepare dataset and move it into the $ROOT directory. First, make sure your initial data are in the following structure.

$ROOT/DOTA
├── train
│   ├──images
│   └── labelTxt
├── val
│   ├── images
│   └── labelTxt
└── test
    └── images

Then split the dataset with:

cd $ROOT/datasets/DOTA_devkit

python ImgSplit_multi_process.py \
    --raw_root_dir $ROOT/DOTA/train \
    --split_root_dir $ROOT/DOTA/trainsplit

!python ImgSplit_multi_process.py \
    --raw_root_dir $ROOT/DOTA/val \
    --split_root_dir $ROOT/DOTA/valsplit

2. Generate imageset files:

cd $ROOT/datasets
python generate_imageset.py

3. Adjust the hyperparameters in hyp.py file.

4. Start training:

python train.py

start evaluation:

python eval.py

Note that :

• the script needs to be executed only once.
• the imageset file used is generated from generate_imageset.py.

• https://github.com/ming71/Rotated-RetinaNet
• https://captain-whu.github.io/DOTA/dataset.html
• https://github.com/CAPTAIN-WHU/DOTA_devkit
• https://github.com/lehduong/torch-warmup-lr