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| # DM-Count | |
| Official Pytorch implementation of the paper Distribution Matching for Crowd Counting (NeurIPS, spotlight). | |
| [Arxiv](https://arxiv.org/pdf/2009.13077.pdf) | [NeurIPS Processings](https://proceedings.neurips.cc/paper/2020/hash/118bd558033a1016fcc82560c65cca5f-Abstract.html) | |
| We propose to use Distribution Matching for crowd COUNTing (DM-Count). In DM-Count, we use Optimal Transport (OT) to measure the similarity between the normalized predicted density map and the normalized ground truth density map. To stabilize OT computation, we include a Total Variation loss in our model. We show that the generalization error bound of DM-Count is tighter than that of the Gaussian smoothed methods. Empirically, our method outperforms the state-of-the-art methods by a large margin on four challenging crowd counting datasets: UCF-QNRF, NWPU, ShanghaiTech, and UCF-CC50. | |
| ## Prerequisites | |
| Python 3.x | |
| Pytorch >= 1.2 | |
| For other libraries, check requirements.txt. | |
| ## Getting Started | |
| 1. Dataset download | |
| + QNRF can be downloaded [here](https://www.crcv.ucf.edu/data/ucf-qnrf/) | |
| + NWPU can be downloaded [here](https://www.crowdbenchmark.com/nwpucrowd.html) | |
| + Shanghai Tech Part A and Part B can be downloaded [here](https://www.kaggle.com/tthien/shanghaitech) | |
| 2. Data preprocess | |
| Due to large sizes of images in QNRF and NWPU datasets, we preprocess these two datasets. | |
| ``` | |
| python preprocess_dataset.py --dataset <dataset name: qnrf or nwpu> --input-dataset-path <original data directory> --output-dataset-path <processed data directory> | |
| ``` | |
| 3. Training | |
| ``` | |
| python train.py --dataset <dataset name: qnrf, sha, shb or nwpu> --data-dir <path to dataset> --device <gpu device id> | |
| ``` | |
| 4. Test | |
| ``` | |
| python test.py --model-path <path of the model to be evaluated> --data-path <directory for the dataset> --dataset <dataset name: qnrf, sha, shb or nwpu> | |
| ``` | |
| ## Pretrained models | |
| Pretrained models on UCF-QNRF, NWPU, Shanghaitech part A and B can be found [Google Drive](https://drive.google.com/drive/folders/10U7F4iW_aPICM5-qJq21SXLLkzlum9tX?usp=sharing). You could download them and put them in in pretrained_models folder. | |
| ## Other resources | |
| + Web Demo | |
| A web interface to can be found [here](https://gradio.app/g/dm-count). | |
|  | |
| Feel free to upload a image and try out the demo on a web browser. It is developed by [Ali Abdalla](twitter.com/si3luwa) from [Gradio](https://github.com/gradio-app/gradio). Gradio is an open source library, which helps to create interfaces to make models more accessible. Thanks Ali and Gradio! | |
| To launch a Gradio interface, run | |
| ``` | |
| python demo.py | |
| ``` | |
| + Kaggle Notebook | |
| A [Kaggle Notebook](https://www.kaggle.com/selmanzleyen/dmcount-shb) is developed by [Selman Ozleyen](https://github.com/SelmanOzleyen/DM-Count). Thanks Selman! | |
| ## References | |
| If you find this work or code useful, please cite: | |
| ``` | |
| @inproceedings{wang2020DMCount, | |
| title={Distribution Matching for Crowd Counting}, | |
| author={Boyu Wang and Huidong Liu and Dimitris Samaras and Minh Hoai}, | |
| booktitle={Advances in Neural Information Processing Systems}, | |
| year={2020}, | |
| } | |
| ``` | |