Spatial Uncertainty-Aware-Semi-Supervised-Crowd-Counting
ICCV2021 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'
Dependencies: requirements.txt
Training code: under prepared, you can check https://github.com/yulequan/UA-MT, as some of our model's structure are built based on it. Thanks Dr. Yu Lequan for such a wonderful project.
------------SHA-----------
--Download chekpoint_best.pth and put into ./checkpoints/SHA
https://drive.google.com/file/d/1UgCasGAr0SqX8OIVL-vw4EEvHoCg1yHk/view?usp=sharing
Prepare the Test data , then put them into ./Data_Crowd_Counting/ShanghaiTech_Crowd_Counting_Dataset/part_A_final/test_data
--Run the test_SHA.py
The unlabeled data index of SHA train data are in unlabeled_images_index.txt
--------------JHU-----------------
--Download chekpoint_best.pth and put into ./checkpoints/JHU
https://drive.google.com/file/d/1aWX2s64dSDRkj-oMxqe3tepYzDhW5rNL/view?usp=sharing
--Prepare the Test data , then put them into ./Data_Crowd_Counting/JHU/test
--Run the test_JHU.py
The unlabeled data index of JHU train data are in unlabeled_images_index_JHU.txt
Citation
If you find our work useful or our work gives you any insights, please cite:
@InProceedings{Meng_2021_ICCV,
author = {Meng, Yanda and Zhang, Hongrun and Zhao, Yitian and Yang, Xiaoyun and Qian, Xuesheng and Huang, Xiaowei and Zheng, Yalin},
title = {Spatial Uncertainty-Aware Semi-Supervised Crowd Counting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {15549-15559}
}