SASNet (AAAI2021)
Official implementation in PyTorch of SASNet as described in "To Choose or to Fuse? Scale Selection for Crowd Counting" by Qingyu Song , Changan Wang , Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Jian Wu, Jiayi Ma.
The codes is tested with PyTorch 1.5.0. It may not run with other versions.
## Visualizations for the scale-adaptive selection
The proposed adaptive selection strategy automatically learns the internal relations and the following visualizations demonstrate its effectiveness.
## Installation
* Clone this repo into a directory named SASNet_ROOT
* Install Python dependencies. We use python 3.6.8 and pytorch >= 1.5.0
```
pip install -r requirements.txt
```
* Download ShanghaiTech dataset and models from [GoogleDrive](https://drive.google.com/drive/folders/17WobgYjekLTq3QIRW3wPyNByq9NJTmZ9?usp=sharing)
## Preparation
Organizing the datas and models as following:
```
SASNet_ROOT/
|->datas/
| |->part_A_final/
| |->part_B_final/
| |->...
|->models/
| |->SHHA.pth
| |->SHHB.pth
| |->...
|->main.py
```
Generating the density maps for the data:
```
python prepare_dataset.py --data_path ./datas/part_A_final
python prepare_dataset.py --data_path ./datas/part_B_final
```
## Running
Run the following commands to launch inference:
```
python3 main.py --data_path ./datas/part_A_final --model_path ./models/SHHA.pth
python3 main.py --data_path ./datas/part_B_final --model_path ./models/SHHB.pth
```
## The network
The overall architecture of the proposed SASNet mainly consists of three components: U-shape backbone, confidence branch and density branch.
## Comparison with state-of-the-art methods
The SASNet achieved state-of-the-art performance on several challenging datasets with various densities.
## Qualitative results
The following qualitative results show impressive counting accuracy under various crowd densities.
## Citing SASNet
If you think SASNet is useful in your project, please consider citing us.
```BibTeX
@article{sasnet,
title={To Choose or to Fuse? Scale Selection for Crowd Counting},
author={Qingyu Song and Changan Wang and Yabiao Wang and Ying Tai and Chengjie Wang and Jilin Li and Jian Wu and Jiayi Ma},
journal={The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
year={2021}
}
```