# CSRNet-pytorch

**Repository Path**: zsnlife/CSRNet-pytorch

## Basic Information

- **Project Name**: CSRNet-pytorch
- **Description**: CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-05-24
- **Last Updated**: 2021-03-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CSRNet-pytorch

This is the PyTorch version 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)

## 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. It shall take some time to generate the dynamic ground truth. Note you need to generate your own json file.

## Training Process

Try `python train.py train.json val.json 0 0` to start training process.

## Validation

Follow the `val.ipynb` to try the validation. You can try to modify the notebook and see the output of each image.
## Results

ShanghaiA MAE: 66.4 [Google Drive](https://drive.google.com/open?id=1Z-atzS5Y2pOd-nEWqZRVBDMYJDreGWHH)
ShanghaiB MAE: 10.6 [Google Drive](https://drive.google.com/open?id=1zKn6YlLW3Z9ocgPbP99oz7r2nC7_TBXK)

## 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}
}
```