CrowdCLIP

An officical implementation of "CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model" (Accepted by CVPR 2023).

Installation

Our experiments are tested on the following environments: Single 3090 GPU, Python: 3.8 PyTorch: 1.10 CUDA: 11.0

conda create --name crowdclip python=3.8 -y
conda activate crowdclip
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
git clone [this repo]
cd CrowdCLIP
pip install -r requirements.txt

Datasets

• Download UCF-QNRF dataset from here
  
• Download ShanghaiTech dataset from [here]
  

Prepare data

1. Download the datasets, then put them under folder datasets. The folder structure should look like this:

CrowdCLIP
├──CLIP
├──configs
├──scripts
├──datasets
    ├──ShanghaiTech
        ├──part_A_final
        ├──part_B_final
    ├──UCF-QNRF
        ├──Train
        ├──Test

2. Generate the image patches

For UCF-QNRF dataset:

python configs/base_cfgs/data_cfg/datasets/qnrf/preprocess_qnrf.py

For ShanghaiTech dataset:

python configs/base_cfgs/data_cfg/datasets/sha/preprocess_sha.py 
python configs/base_cfgs/data_cfg/datasets/shb/preprocess_shb.py 

The folder structure should look like this:

CrowdCLIP
├──CLIP
├──configs
├──scripts
├──datasets
├──processed_datasets
    ├──SHA
        ├──test_data
        ├──train_data
    ├──SHB
        ├──test_data
        ├──train_data
    ├──UCF-QNRF
        ├──test_data
        ├──train_data

3. Install CLIP

cd CrowdCLIP/CLIP
python setup.py develop

Model

Download the pretrained model from Baidu-Disk, passward:ale1; or Onedrive;

Training

We are preparing the journal version. The code will be coming soon.

Testing

Download the pretrained model and put them in CrowdCLIP/save_model
Example:

python scripts/run.py --config save_model/pretrained_qnrf/config.yaml --config configs/base_cfgs/data_cfg/datasets/qnrf/qnrf.yaml --test_only --gpu_id 0
python scripts/run.py --config save_model/pretrained_sha/config.yaml --config configs/base_cfgs/data_cfg/datasets/sha/sha.yaml --test_only --gpu_id 0
python scripts/run.py --config save_model/pretrained_shb/config.yaml --config configs/base_cfgs/data_cfg/datasets/shb/shb.yaml --test_only --gpu_id 0

Acknowledgement

Many thanks to the brilliant works (CLIP and OrdinalCLIP)!

Citation

If you find this codebase helpful, please consider to cite:

@article{Liang2023CrowdCLIP,
  title={CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model},
  author={Dingkang Liang, Jiahao Xie, Zhikang Zou, Xiaoqing Ye, Wei Xu, Xiang Bai},
  journal={CVPR},
  year={2023}
}