# MPCount

**Repository Path**: Mr_wang_xs/MPCount

## Basic Information

- **Project Name**: MPCount
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-15
- **Last Updated**: 2024-04-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://arxiv.org/pdf/2403.09124.pdf).

## Requirements
* Python 3.10.12
* PyTorch 2.0.1
* Torchvision 0.15.2
* Others specified in [requirements.txt](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:
| Source | Performance                                   | Weights                                                                                                                                          |
| ------ | --------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| A      | B: 11.4MAE, 19.7MSE<br>Q: 115.7MAE, 199.8MSE  | [OneDrive](https://hkustconnect-my.sharepoint.com/:u:/g/personal/zpengac_connect_ust_hk/EaWnUPugulxIiP4gK2F_bqcBJwJhru0aWa8JH6l8Dbk5DQ?e=2B0kJP) |
| B      | A: 99.6MAE, 182.9MSE<br>Q: 165.6MAE, 290.4MSE | [OneDrive](https://hkustconnect-my.sharepoint.com/:u:/g/personal/zpengac_connect_ust_hk/EZp54KXswPVFnXHP2dhIGRABUZYrH4ZXaxBr5y9M7io2Bg?e=DnGP6v) |
| Q      | A: 65.5MAE, 110.1MSE<br>B: 12.3MAE, 24.1MSE   | [OneDrive](https://hkustconnect-my.sharepoint.com/:u:/g/personal/zpengac_connect_ust_hk/EQYuWSlkgMtNqWUbL5lmv1YBOkmeXF3sa0hNFQ5QGcSpvQ?e=Hy1Pf6) |

## 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}
}
```