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CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
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README.md

CSRNet (Try our Pytorch Version!)

This is the 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

Models (Only for tests)

This is the model for test. The results should be similar to the results shown in the paper(slightly better or worse).

  1. ShanghaiTech_Part_A: Google Drive

  2. ShanghaiTech_Part_B: Google Drive

Prerequisites

  1. A good CAFFE

We understand that it's tedious and difficult to config a custom input layer (even installing CAFFE on your own PC), thus we make a pytorch version for the csrnet: CSRNet Pytorch Version

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}
}
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