Awesome
Kernel-based Density Map Generation for Dense Object Counting
Data preparation
The dataset can be constructed followed by Bayesian Loss.
Pretrained model
The pretrained model can be downloaded from GoogleDrive.
Test
python test.py --net vgg19 --data-dir PATH_TO_DATASET --save-dir PATH_TO_CHECKPOINT
python test.py --net csrnet --data-dir PATH_TO_DATASET --save-dir PATH_TO_CHECKPOINT --resize True
Train
python train.py --net vgg19 --data-dir PATH_TO_DATASET --save-dir PATH_TO_CHECKPOINT
Citation
If you use our code or models in your research, please cite with:
@inproceedings{wan2019adaptive,
title={Adaptive density map generation for crowd counting},
author={Wan, Jia and Chan, Antoni},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={1130--1139},
year={2019}
}
@article{wan2020kernel,
title={Kernel-based Density Map Generation for Dense Object Counting},
author={Wan, Jia and Wang, Qingzhong and Chan, Antoni B},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2020},
publisher={IEEE}
}