I'm now busy at my senior thesis... So I will continue my works after May 20th

CSRNet

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 decide to make a pytorch version for the csrnet:)

References

If you find the CSRNet useful, please cite our paper. Thank you!

@article{li2018csrnet,
  title={CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes},
  author={Li, Yuhong and Zhang, Xiaofan and Chen, Deming},
  journal={arXiv preprint arXiv:1802.10062},
  year={2018}
}