MDKNet

Virtual Classification: Modulating Domain-Specific Knowledge for Multi-domain Crowd Counting

Testing code of MDKNet is available.

Datasets Preparation

Download the datasets ShanghaiTech A, ShanghaiTech B, UCF-QNRF and NWPU. Then generate the density maps via gen_den_map.py. After that, create a folder named JSTL_large_4_dataset, and directly copy all the processed data in JSTL_large_4_dataset.

The tree of the folder should be:

`DATASET` is `SHA`, `SHB`, `QNRF_large` or `NWPU_large`.

-JSTL_large_dataset
   -den
       -test
            -Npy files with the name of DATASET_img_xxx.npy, which logs the info of density maps.
       -train
            -Npy files with the name of DATASET_img_xxx.npy, which logs the info of density maps.
   -ori
       -test_data
            -ground_truth
                 -MAT files with the name of DATASET_img_xxx.mat, which logs the original dot annotations.
            -images
                 -JPG files with the name of DATASET_img_xxx.mat, which logs the original image files.
       -train_data
            -ground_truth
                 -MAT files with the name of DATASET_img_xxx.mat, which logs the original dot annotations.
            -images
                 -JPG files with the name of DATASET_img_xxx.mat, which logs the original image files.

Download the pretrained hrnet model HRNet-W40-C from the link https://github.com/HRNet/HRNet-Image-Classification and put it directly in the root path of the repository.

Test

Download the pretrained model(mdknet.pth) via Link：https://pan.baidu.com/s/1J9mzjo5l6z3TDr0bPYi-kw, Extract Password：sqbm

or

bash download_models.sh

And put the model into folder ./output/MDKNet_models/

bash test.sh

Train

bash train.sh

Citation

If you find our work useful or our work gives you any insights, please cite:

@ARTICLE{MingyueGuoVirtualCM,
  author={Guo, Mingyue and Chen, Binghui and Yan, Zhaoyi and Wang, Yaowei and Ye, Qixiang},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting}, 
  year={2024},
  pages={1-15},
  keywords={Training;Adaptation models;Feature extraction;Modulation;Data models;Knowledge engineering;Pipelines;Crowd counting;domain-guided virtual classifier (DVC);instance-specific batch normalization (IsBN);multidomain learning},
  doi={10.1109/TNNLS.2024.3350363}}