Code for Unsupervised crowd counting via cross-domain feature adaptation.

Google Drive

Baidu Cloud : t4qc

We are good in the environment:

python 3.6

CUDA 9.2

Pytorch 1.2.0

numpy 1.19.2

matplotlib 3.3.4

We provide the test code for our model. The result_gcc_qnrf.pth model is adapted from the GCC dataset to the UCF_QNRF dataset. We randomly select an image from the UCF_QNRF dataset and place it in the image folder. And you can either choose the other images for a test.

We are good to run:

python test.py --model CDFA --model_state ./model/result_gcc_qnrf.pth --out ./out/out.png

Please see the paper for more details about network.

@ARTICLE{9788041,
  author={Ding, Guanchen and Yang, Daiqin and Wang, Tao and Wang, Sihan and Zhang, Yunfei},
  journal={IEEE Transactions on Multimedia}, 
  title={Crowd counting via unsupervised cross-domain feature adaptation}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMM.2022.3180222}}

Thanks to these repositories

• C-3 Framework
• EDSC-pytorch

If you have any question, please feel free to contact me. (gcding@whu.edu.cn)