Awesome
S-DCNet
This is the repository for S-DCNet, presented in our paper in the ICCV 2019:
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
Haipeng Xiong<sup>1</sup>, Hao Lu<sup>2</sup>, Chengxin Liu<sup>1</sup>, Liang Liu<sup>1</sup>, Zhiguo Cao<sup>1</sup>, Chunhua Shen<sup>2</sup>
<sup>1</sup>Huazhong University of Science and Technology, China
<sup>2</sup>The University of Adelaide, Australia
News !!!
An extended version of S-DCNet, i.e., SS-DCNet is now available !!!
Contributions
- Reformulating the counting problem: We propose S-DCNet, which transforms open-set counting into a closed-set problem via Spatial Divide-and-Conquer;
- Simple and effective: S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part_B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC.
Environment
Please install required packages according to requirements.txt
.
Data
Testing data for ShanghaiTech dataset have been preprocessed. You can download the processed dataset from:
Baidu Yun (314M) with code: ou3b
Model
Pretrained weights can be downloaded from:
Baidu Yun (210MB) with code: 1tcb
A Quick Demo
-
Download the code, data and model.
-
Organize them into one folder. The final path structure looks like this:
-->The whole project
-->Test_Data
-->SH_partA_Density_map
-->SH_partB_Density_map
-->model
-->SHA
-->SHB
-->Network
-->class_func.py
-->merge_func.py
-->SDCNet.py
-->SHAB_main.py
-->main_process.py
-->Val.py
-->load_data_V2.py
-->IOtools.py
-
Run the following code to reproduce our results. The MAE will be SHA: 57.575, SHB: 6.633. Have fun:)
python SHAB_main.py
References
If you find this work or code useful for your research, please cite:
@inproceedings{xhp2019SDCNet,
title={From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer},
author={Xiong, Haipeng and Lu, Hao and Liu, Chengxin and Liang, Liu and Cao, Zhiguo and Shen, Chunhua},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2019},
pages = {8362-8371}
}