Skip to content

Latest commit

 

History

History
41 lines (32 loc) · 1.25 KB

README.md

File metadata and controls

41 lines (32 loc) · 1.25 KB
Raw

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}
}