Awesome
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}}