| --- |
| library_name: monai |
| tags: |
| - crowd-counting |
| - cnn |
| - detection |
| license: mit |
| metrics: |
| - mae |
| pipeline_tag: object-detection |
| datasets: |
| - ShanghaiTechDataset |
| --- |
| --- |
|
|
| ### Model Description |
| A machine learning model for crowd counting |
|
|
| - **Model type:** image-classifier |
| - **License:** mit |
|
|
| ## Crowd Counting Model |
| The aim is to build a model that can estimate the amount of people in a crowd from an image- |
|
|
| The model was built using **CSRNet** a crowd counting neural network designed by Yuhong Li, Xiaofan Zhang and Deming Chen ([https://github.com/leeyeehoo/CSRNet-pytorch](https://github.com/leeyeehoo/CSRNet-pytorch)) |
|
|
| ### Model Sources |
|
|
| - **Repository:** [https://github.com/leeyeehoo/CSRNet-pytorch](https://github.com/leeyeehoo/CSRNet-pytorch) |
|
|
| ## Uses |
|
|
| This model was created in the spirit of creating a model capable of counting the amount of people in a crowd using images. |
|
|
| ### Direct Use |
|
|
| ```bash |
| model = CSRNet() |
| checkpoint = torch.load("weights.pth") |
| model.load_state_dict(checkpoint) |
| model.predict() |
| |
| ``` |
|
|
| ## Bias, Risks, and Limitations |
|
|
| Although the model can be very accurate its not exact, it has a 2%-6% error in the prediction. |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| The model was trained using the ShanghaiTech Dataset, specifically the Shanghai B Dataset. |
|
|
| ### Training Procedure |
|
|
| The info on training procedure can be found in this repository [https://github.com/leeyeehoo/CSRNet-pytorch](https://github.com/leeyeehoo/CSRNet-pytorch) |
|
|
| ## Evaluation and Results |
|
|
| The model reached a MAE of 10.6 |
|
|
| ## Citation |
|
|
| ### Model creation and training |
|
|
| @inproceedings{li2018csrnet, |
| title={CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes}, |
| author={Li, Yuhong and Zhang, Xiaofan and Chen, Deming}, |
| booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, |
| pages={1091--1100}, |
| year={2018} |
| } |
|
|
| ### Dataset |
|
|
| @inproceedings{zhang2016single, |
| title={Single-image crowd counting via multi-column convolutional neural network}, |
| author={Zhang, Yingying and Zhou, Desen and Chen, Siqin and Gao, Shenghua and Ma, Yi}, |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, |
| pages={589--597}, |
| year={2016} |
| } |
