https://github.com/leeyeehoo/csrnet
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/leeyeehoo/csrnet
crowdcounting cvpr2018
Last synced: 5 months ago
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CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
- Host: GitHub
- URL: https://github.com/leeyeehoo/csrnet
- Owner: leeyeehoo
- Created: 2018-04-17T20:16:05.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-07-19T00:52:14.000Z (almost 8 years ago)
- Last Synced: 2025-01-20T19:51:54.827Z (over 1 year ago)
- Topics: crowdcounting, cvpr2018
- Homepage:
- Size: 17.6 KB
- Stars: 35
- Watchers: 7
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# CSRNet (Try our [Pytorch Version](https://github.com/leeyeehoo/CSRNet-pytorch/tree/master)!)
This is the repo for [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://arxiv.org/abs/1802.10062) 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](https://drive.google.com/open?id=16dhJn7k4FWVwByRsQAEpl9lwjuV03jVI)
## 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](https://drive.google.com/open?id=1odZ3B_ZDSepPcVFO_TfGUIrpF2DF7SwY)
2) ShanghaiTech_Part_B: [Google Drive](https://drive.google.com/open?id=1NOpn0ztlye85vrHR2TMwOI2Qu_S8zANj)
## 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](https://github.com/leeyeehoo/CSRNet-pytorch/tree/master)
## 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}
}
```