Wholly-WOOD

Introduction

We develop Wholly-WOOD (Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection), a weakly-supervised OOD framework, capable of wholly leveraging various labeling forms (Points, HBoxes, RBoxes, and their combination) in a unified fashion. By only using HBox for training, our Wholly-WOOD achieves performance very close to that of the RBox-trained counterpart on remote sensing and other areas, which significantly reduces the tedious efforts on labor-intensive annotation for oriented objects.

This project is the Jittor implementation of Wholly-WOOD. The code works with Jittor 1.3.8.5. It is modified from JDet, which is an object detection benchmark mainly focus on oriented object detection. PyTorch version: Wholly-WOOD (PyTorch).

Models

This repository contains the Wholly-WOOD model and our series of work on weakly-supervised OOD (i.e. H2RBox, H2RBox-v2, and Point2RBox).

1. Wholly-WOOD

We can train/test Wholly-WOOD model by:

python tools/run_net.py --config-file=configs/whollywood/whollywood_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/whollywood_obb_r50_adamw_fpn_1x_dota.py --task=test

2. H2RBox

We can train/test H2RBox model by:

python tools/run_net.py --config-file=configs/whollywood/h2rbox_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/h2rbox_obb_r50_adamw_fpn_1x_dota.py --task=test

3. H2RBox-v2

We can train/test H2RBox-v2 model by:

python tools/run_net.py --config-file=configs/whollywood/h2rbox_v2p_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/h2rbox_v2p_obb_r50_adamw_fpn_1x_dota.py --task=test

4. Point2RBox

We can train/test Point2RBox model by:

python tools/run_net.py --config-file=configs/whollywood/point2rbox_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/point2rbox_obb_r50_adamw_fpn_1x_dota.py --task=test

Installation

Recommended environments:

• System: Linux (e.g. Ubuntu/CentOS/Arch), macOS, or Windows Subsystem of Linux (WSL)
• Python == 3.10
• Jittor == 1.3.8.5
• CPU Compiler: g++ == 11.4.0
• GPU Library: cuda == 12.3 & cudnn == 8.9.7.29

Step 1: Install requirements

git clone https://github.com/yuyi1005/whollywood-jittor
cd whollywood-jittor
python -m pip install -r requirements.txt

If you have any installation problems for Jittor, please refer to Jittor

Step 2: Install Wholly-WOOD

cd whollywood-jittor
# suggest this 
python setup.py develop
# or
python setup.py install

If you don't have permission for install, please add --user.

Datasets

The following datasets are supported in JDet, please check the corresponding document before use.

DOTA1.0/DOTA1.5/DOTA2.0 Dataset: dota.md.

FAIR Dataset: fair.md

SSDD/SSDD+: ssdd.md

You can also build your own dataset by convert your datas to DOTA format.

Visualization

You can test and visualize results on your own image sets by:

python tools/run_net.py --config-file=configs/whollywood/whollywood_obb_r50_adamw_fpn_1x_dota.py --task=vis_test

You can choose the visualization style you prefer, for more details about visualization, please refer to visualization.md.

Citation

@article{yu2025whollywood,
  title={Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection}, 
  author={Yi Yu and Xue Yang and Yansheng Li and Zhenjun Han and Feipeng Da and Junchi Yan},
  year={2025},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
}