@InProceedings{Xia_2018_CVPR,
author = {Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
title = {DOTA: A Large-Scale Dataset for Object Detection in Aerial Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
The dota dataset can be downloaded from here.
The data structure is as follows:
mmrotate
├── mmrotate
├── tools
├── configs
├── data
│ ├── DOTA
│ │ ├── train
│ │ ├── val
│ │ ├── test
Please crop the original images into 1024×1024 patches with an overlap of 200 by run
python tools/data/dota/split/img_split.py --base-json \
tools/data/dota/split/split_configs/ss_trainval.json
python tools/data/dota/split/img_split.py --base-json \
tools/data/dota/split/split_configs/ss_test.json
If you want to get a multiple scale dataset, you can run the following command.
python tools/data/dota/split/img_split.py --base-json \
tools/data/dota/split/split_configs/ms_trainval.json
python tools/data/dota/split/img_split.py --base-json \
tools/data/dota/split/split_configs/ms_test.json
Please change the img_dirs
and ann_dirs
in json. (Forked from BboxToolkit, which is faster then DOTA_Devkit.)
Please change data_root
in configs/_base_/datasets/dotav1.py
to split DOTA dataset.