PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Image
torch >= 1.0 torchvision opencv numpy scipy, all the dependencies can be easily installed by pip or conda
This code was tested with python 3.6
1、 Dowload Dataset
2、 Pre-Process Data (resize image and split train/validation)
python preprocess_dataset.py --origin_dir <directory of original data> --data_dir <directory of processed data>
3、 Train model (validate on single GTX Titan X)
python train.py --data_dir <directory of processed data> --save_dir <directory of log and model>
4、 Test Model
python test.py --data_dir <directory of processed data> --save_dir <directory of log and model>
The result is slightly influenced by the random seed, but fixing the random seed (have to set cuda_benchmark to False) will make training time extrodinary long, so sometimes you can get a slightly worse result than the reported result, but most of time you can get a better result than the reported one. If you find this code is useful, please give us a star and cite our paper, have fun.
5、 Training on ShanghaiTech Dataset
Change dataloader to crowd_sh.py
For shanghaitech a, you should set learning rate to 1e-6, and bg_ratio to 0.1
Paper: https://arxiv.org/abs/2012.03597v3
RSOC Dataset：https://pan.baidu.com/s/19hL7O1sP_u2r9LNRsFSjdA code：nwcx
or at the website https://drive.google.com/drive/my-drive but only including building subsets. Other three can be download at https://captain-whu.github.io/DOTA/ according to our provided filenames
CARPK dataset, PUCPR+ dataset: https://lafi.github.io/LPN/
DroneCrowd dataset: https://github.com/VisDrone/DroneCrowd
UCF-QNRF dataset: https://www.crcv.ucf.edu/data/ucf-qnrf/
ShanghaiTech dataset: http://pan.baidu.com/s/1nuAYslz
UCF_CC_50 dataset: https://www.crcv.ucf.edu/data/ucf-cc-50/
References
If you find the PSGCNet useful, please cite our paper. Thank you!
@article{gao2022psgcnet,
title={PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images},
author={Gao, Guangshuai and Liu, Qingjie and Hu, Zhenghui and Li, Lu and Wen, Qi and Wang, Yunhong},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={60},
pages={1--12},
year={2022},
publisher={IEEE}
}