This is the official code for the NWD. The expanded method is accepted by the ISPRS J P & RS in 2022.
- Linux
- Python 3.7 (Python 2 is not supported)
- PyTorch 1.5 or higher
- CUDA 10.1 or higher
- NCCL 2
- GCC(G++) 5.4 or higher
- mmcv-nwd==1.3.5
- cocoapi-aitod==12.0.3
We have tested the following versions of OS and softwares:
- OS: Ubuntu 16.04
- GPU: TITAN X
- CUDA: 10.1
- GCC(G++): 5.5.0
- PyTorch: 1.5.0+cu101
- TorchVision: 0.6.0+cu101
- MMCV: 1.3.5
- MMDetection: 2.13.0
a. Create a conda virtual environment and activate it.
conda create -n nwd python=3.7 -y
conda activate nwdb. Install PyTorch stable or nightly and torchvision following the official instructions, e.g.,
pip install torch==1.5.0+cu101 torchvision==0.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.htmlc. Install MMCV-NWD
git clone https://github.com/jwwangchn/mmcv-nwd.git
cd mmcv-nwd
MMCV_WITH_OPS=1 pip install -e . # package mmcv-full will be installed after this step
cd ../d. Install COCOAPI-AITOD for Evaluating on AI-TOD dataset
pip install "git+https://github.com/jwwangchn/cocoapi-aitod.git#subdirectory=aitodpycocotools"e. Install NWD
git clone https://github.com/jwwangchn/NWD.git
# optional
pip install -r requirements.txt
python setup.py develop
# or "pip install -v -e ."Please refer to AI-TOD for AI-TOD dataset.
It is recommended to symlink the dataset root to $NWD/data.
If your folder structure is different, you may need to change the corresponding paths in config files (configs/base/datasets/aitod_detection.py).
NWD
├── mmdet
├── tools
├── configs
├── data
│ ├── AI-TOD
│ │ ├── annotations
│ │ │ │─── aitod_training_v1.json
│ │ │ │─── aitod_validation_v1.json
│ │ ├── trainval
│ │ │ │─── ***.png
│ │ │ │─── ***.png
│ │ ├── test
│ │ │ │─── ***.png
│ │ │ │─── ***.pngThe NWD's config files are in configs/nwd.
Please see MMDetection full tutorials with existing dataset for beginners.
The basic usage is as follows (e.g. train Faster R-CNN with NWD). Note that the lr=0.01 in config file needs to be lr=0.01/4 for training on single GPU.
python tools/train.py configs/nwd/faster_rcnn_r50_aitod_rpn_nwd.pyThe basic usage is as follows (e.g. train Faster R-CNN with NWD).
bash ./tools/dist_train.sh configs/nwd/faster_rcnn_r50_aitod_rpn_nwd.py 4The benchmark and trained models will be publicly available soon.
@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}
}@article{NWD_RKA_2022_ISPRSJ,
title={Detecting Tiny Objects in Aerial Images: A Normalized Wasserstein Distance and A New Benchmark},
author={Xu, Chang and Wang, Jinwang and and Yang, Wen and Yu, Huai and Yu, Lei and Xia, Gui-Song},
journal={ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS J P & RS)},
year={2022}
}