Update README.md

README.md

@@ -1,3 +1,84 @@
 ---
+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
+
+- **Developed by:** rootstrap
+- **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}
+}