https://github.com/dk-liang/crowdclip
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model [CVPR 2023]
https://github.com/dk-liang/crowdclip
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CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model [CVPR 2023]
- Host: GitHub
- URL: https://github.com/dk-liang/crowdclip
- Owner: dk-liang
- Created: 2023-04-09T13:00:09.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-28T13:49:40.000Z (almost 2 years ago)
- Last Synced: 2023-07-28T14:56:54.417Z (almost 2 years ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 4.52 MB
- Stars: 29
- Watchers: 8
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://www.crcv.ucf.edu/data/ucf-qnrf/)
- 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:
```bash
python configs/base_cfgs/data_cfg/datasets/qnrf/preprocess_qnrf.py
```
For ShanghaiTech dataset:
```bash
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](https://pan.baidu.com/s/19B4r-iXHSypj4GU7v2EoEA), passward:ale1; or [Onedrive](https://1drv.ms/f/s!Ak_WZsh5Fl0lhmAvvpa7EFq1uSSS?e=brRQ00);
# 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](https://github.com/openai/CLIP) and [OrdinalCLIP](https://github.com/xk-huang/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}
}
```