Official implementation of Learning Point-guided Localization for Detection in Remote Sensing Images
In this repository, we release the OPLD code in Pytorch.
- OPLD architecture:
- OPLD output on DOTA:
- 4 x TITAN X GPU
- pytorch1.1
- python3.6.8
Install OPLD following INSTALL.md.
| Model | LR | mAP50 | FPS | DOWNLOAD |
|---|---|---|---|---|
| R-101-FPN_MS | 1x | 76.43 | 5.2 | GoogleDrive, BaiduNetDisk (4pt9) |
To train a model with 4 GPUs run:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 tools/train_net.py --cfg cfgs/DOTA/e2e_OPLD_R-50-FPN_1x.yaml
python tools/test_net.py --cfg ckpts/DOTA/e2e_OPLD_R-50-FPN_1x/e2e_OPLD_R-50-FPN_1x.yaml --gpu_id 0,1,2,3
python tools/test_net.py --cfg ckpts/DOTA/e2e_OPLD_R-50-FPN_1x/e2e_OPLD_R-50-FPN_1x.yaml --gpu_id 0
If you use DOTA dataset and find this repo useful, please consider cite.
@inproceedings{xia2018dota,
title={DOTA: A large-scale dataset for object detection in aerial images},
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},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3974--3983},
year={2018}
}
@ARTICLE{9252176,
title={Learning Point-guided Localization for Detection in Remote Sensing Images},
author={Q. {Song} and F. {Yang} and L. {Yang} and C. {Liu} and M. {Hu} and L. {Xia}},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2020},
}
OPLD is released under the MIT license.