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AI-TOD

Download

You need to download the following two parts (Part1: xView training set, Part2: part of AI-TOD) and use our end-to-end synthesis tool to generate the complete AI-TOD dataset. (Note the we have released the complete annotations of AI-TOD, you only need to generate images)

A Guide on Generating AI-TOD

Step 1: Download the xView training set, AI-TOD without xview, and clone the aitodtoolkit.

git clone https://github.com/SaShukla090/AI-TOD_data_generation.git

Step 2: Organize the downloaded files in the following way.

├─aitod
│  ├─annotations ## put the downloaded annotations of AI-TOD_wo_xview (.json)
│  └─images ## unzip the downloaded AI-TOD_wo_xview image sets, put them (.png) in the corresponding folder
│      ├─test ## directly put the images in it without extra folder
│      ├─train 
│      ├─trainval 
│      └─val 
├─aitod_xview ## here are six files (.txt)
├─xview
│  ├─ori
│  │   └─train_images ## unzip the downloaded xView training set images, put them (.tif) here
│  └─xView_train.geojson ## the annotation file of xView training set
└─generate_aitod.py ## end-to-end tool

Step 3: Install required packages.

  • Required environment (make the conda env using using 'data/aitod/AI-TOD/conda_env_aitod.yml' file)
conda env create -f conda_env_aitod.yml
conda activate aitod
pip install -r requirements.txt
  • mmcv install mmcv==1.0 (don't isntall mmcv 2.0 version which is default)
pip install mmcv==1.0.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.0/index.html

Step 4: Run the E2E aitodtoolkit and get AI-TOD, it might take around an hour, then the full image sets of AI-TOD can be found in the aitod folder. And you can delete other files in other folders to avoid taking up too much space.

python generate_aitod_imgs.py

Evaluation

Training, Validation and Testing sets are both publicly available now. We report the COCO style performance in the original paper, you can use the cocoapi-aitod to evaluate the model performance.

Citation

If you use this dataset in your research, please consider citing these papers.

@inproceedings{AI-TOD_2020_ICPR,
    title={Tiny Object Detection in Aerial Images},
    author={Wang, Jinwang and Yang, Wen and Guo, Haowen and Zhang, Ruixiang and Xia, Gui-Song},
    booktitle=ICPR,
    pages={3791--3798},
    year={2021},
}

@article{NWD_2021_arXiv,
  title={A Normalized Gaussian Wasserstein Distance for Tiny Object Detection},
  author={Wang, Jinwang and Xu, Chang and Yang, Wen and Yu, Lei},
  journal={arXiv preprint arXiv:2110.13389},
  year={2021}
}

Reference

xView Dataset

License

The AI-TOD dataset is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Thus AI-TOD dataset are freely available for academic purpose or individual reserach, but restricted for commercial use. Besides, the underlying codes are licensed under the MIT license.