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Single Domain Generalization for Crowd Counting

This is an official repository for our CVPR2024 work, "Single Domain Generalization for Crowd Counting". You can read our paper here.

Requirements

• Python 3.10.12
• PyTorch 2.0.1
• Torchvision 0.15.2
• Others specified in requirements.txt

Data Preparation

1. Download ShanghaiTech and UCF-QNRF datasets from official sites and unzip them.
2. Run the following commands to preprocess the datasets:

   python utils/preprocess_data.py --origin-dir [path_to_ShanghaiTech]/part_A --data-dir data/sta
   python utils/preprocess_data.py --origin-dir [path_to_ShanghaiTech]/part_B --data-dir data/stb
   python utils/preprocess_data.py --origin-dir [path_to_UCF-QNRF] --data-dir data/qnrf
   

3. Run the following commands to generate GT density maps:

   python dmap_gen.py --path data/sta
   python dmap_gen.py --path data/stb
   python dmap_gen.py --path data/qnrf
   

Training

Run the following command:

python main.py --task train --config configs/sta_train.yml

You may edit the .yml config file as you like.

Testing

Run the following commands after you specify the path to the model weight in the config file:

python main.py --task test --config configs/sta_test_stb.yml
python main.py --task test --config configs/sta_test_qnrf.yml

Inference

Run the following command:

python inference.py --img_path [path_to_img_file_or_directory] --model_path [path_to_model_weight] --save_path output.txt --vis_dir vis

Pretrained Weights

We provide pretrained weights in the table below:

Citation

If you find this work helpful in your research, please cite the following:

@inproceedings{pengMPCount2024,
  title = {Single Domain Generalization for Crowd Counting},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)},
  author = {Peng, Zhuoxuan and Chan, S.-H. Gary},
  year = {2024}
}