• An officical implementation of AutoScale regression-based method, you can find localization-based method from here.
• AutoScale leverages a simple yet effective Learning to Scale (L2S) module to cope with significant scale variations in both localization and regression.
  

AutoScale_regression
|-- data            # generate target
|-- model           # model path 
|-- README.md       # README
|-- centerloss.py           
|-- config.py          
|-- dataset.py       
|-- find_contours.py           
|-- fpn.py         
|-- image.py
|-- make_npydata.py
|-- rate_model.py
|-- val.py          

python >=3.6
pytorch >=1.0
opencv-python >=4.0
scipy >=1.4.0
h5py >=2.10
pillow >=7.0.0

• Download ShanghaiTech dataset from Baidu-Disk, passward:cjnx ; or Google-Drive
• Download UCF-QNRF Dataset from Google-Drive
• Download JHU-CROWD ++ dataset from here
• Download NWPU-CROWD dataset from Baidu-Disk, passward:9z97; or Google-Drive

cd data
Edit "distance_generate_xx.py" to change the path to your original dataset folder.
python density_generate_xx.py

“xx” means the dataset name, including sh, jhu, qnrf, and nwpu.

Download the pretrained model from Baidu-Disk, passward:9qfc ; or Google-Drive

• git clone https://github.com/dk-liang/AutoScale_regression.git
cd AutoScale
  

• Download Dataset and Model

• Generate target

• Generate images list

  Edit "make_npydata.py" to change the path to your original dataset folder.
  Run python make_npydata.py .

• Test
  python val.py --test_dataset qnrf --pre ./model/QNRF/model_best.pth --gpu_id 0
python val.py --test_dataset jhu --pre ./model/JHU/model_best.pth --gpu_id 0
python val.py --test_dataset nwpu --pre ./model/NWPU/model_best.pth --gpu_id 0
python val.py --test_dataset ShanghaiA --pre ./model/ShanghaiA/model_best.pth --gpu_id 0
python val.py --test_dataset ShanghaiB --pre ./model/ShanghaiB/model_best.pth --gpu_id 0
  More config information is provided in config.py

coming soon.

If you are interested in AutoScale, please cite our work:

@article{xu2019autoscale,
  title={AutoScale: Learning to Scale for Crowd Counting},
  author={Xu, Chenfeng and Liang, Dingkang and Xu, Yongchao and Bai, Song and Zhan, Wei and Tomizuka, Masayoshi and Bai, Xiang},
  journal={arXiv preprint arXiv:1912.09632},
  year={2019}
}

and

@inproceedings{xu2019learn,
  title={Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting},
  author={Xu, Chenfeng and Qiu, Kai and Fu, Jianlong and Bai, Song and Xu, Yongchao and Bai, Xiang},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={8382--8390},
  year={2019}
}