CSRNet-pytorch
This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes in CVPR 2018, which delivered a state-of-the-art, straightforward and end-to-end architecture for crowd counting tasks.
Datasets
ShanghaiTech Dataset: Google Drive
Prerequisites
We strongly recommend Anaconda as the environment.
Python: 2.7
PyTorch: 0.4.0
CUDA: 9.2
Ground Truth
Please follow the make_dataset.ipynb to generate the ground truth.
Training process
References
If you find the CSRNet useful, please cite our paper. Thank you!
@inproceedings{li2018csrnet,
title={CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes},
author={Li, Yuhong and Zhang, Xiaofan and Chen, Deming},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={1091--1100},
year={2018}
}
Please cite the Shanghai datasets and other works if you use them.
@inproceedings{zhang2016single,
title={Single-image crowd counting via multi-column convolutional neural network},
author={Zhang, Yingying and Zhou, Desen and Chen, Siqin and Gao, Shenghua and Ma, Yi},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={589--597},
year={2016}
}