zpdu/redesigning-multi-scale-neural-network-for-crowd-counting

[TIP 2023] Redesigning Multi-Scale Neural Network for Crowd Counting

Redesigning Multi-Scale Neural Network for Crowd Counting

This is the official implementation of 'Redesigning Multi-Scale Neural Network for Crowd Counting'. (IEEE Transactions on Image Processing) [arXiv]

Installation

Python ≥ 3.6.

To install other required packages, run:

pip install -r requirements.txt

Training

• Fill in the settings in datasets/SHHA/setting.py
• Run 'train.py'

Evaluation

• Download the processed dataset ShanghaiTech part A(Link). For the preprocessing code, please refer to this.
• Download our SHA models here.
• Modify the path to the processed dataset and pretrained model in 'setting.py' in datasets/SHHA
• Run 'test.py'

Acknowledgements

Part of codes are borrowed from C^3 Framework and SASNet. Thanks for their great work!

Citation

If you find this work useful, please cite

@article{du2022redesigning,
  title={Redesigning Multi-Scale Neural Network for Crowd Counting},
  author={Du, Zhipeng and Shi, Miaojing and Deng, Jiankang and Zafeiriou, Stefanos},
  journal={arXiv preprint arXiv:2208.02894},
  year={2022}
}

or

@article{du2023redesigning,
  title={Redesigning multi-scale neural network for crowd counting},
  author={Du, Zhipeng and Shi, Miaojing and Deng, Jiankang and Zafeiriou, Stefanos},
  journal={IEEE Transactions on Image Processing},
  year={2023},
  publisher={IEEE}
}

redesigning-multi-scale-neural-network-for-crowd-counting's Issues

Can't find requirements.txt

Hi I can't find requirments.txt in the repo. Please share.

code

Hello, can you provide the training code? (train. py) Thank you!

Regarding relative loss

Has the relative loss been implemented yet? It is currently commented out and the function is not available. Thanks!