[Paper] AI-TOD is a dataset for tiny object detection in aerial images.
[Dataset] Please download the xView trainig set and AI-TOD_wo_xview to construct the complete AI-TOD dataset!
AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others.
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)
- xView training set. [Website]
- Part of AI-TOD. [GoogleDrive]
- E2E aitodtoolkit. [Folder]
Step 1: Download the xView training set, AI-TOD without xview, and clone the aitodtoolkit.
git clone https://github.com/jwwangchn/AI-TOD.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_imgs.py ## end-to-end tool
Step 3: Install required packages.
- Required environment
- Python 3.7
- mmcv
- Install wwtool
git clone https://github.com/jwwangchn/wwtool.git
cd wwtool
python setup.py develop
- Install other required packages
cd ..
cd aitodtoolkit
pip install -r requirements.txt
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
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.
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}
}
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.