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