Update README.md

README.md

@@ -1,3 +1,96 @@
----
-license: cc-by-2.0
----
+Here's a sample README file for the Refined-RadImageNet model, formatted according to Hugging Face's model card guidelines:
+
+```markdown
+---
+language: en
+license: apache-2.0
+---
+
+# Refined-RadImageNet Model Card
+
+## Model Description
+
+**Refined-RadImageNet** is a collection of pre-trained convolutional neural networks (CNNs) specifically designed for medical imaging applications. These models are trained solely on the RadImageNet dataset, which comprises over 1.35 million annotated CT, MRI, and ultrasound images across various pathologies. 
+
+## Model Details
+
+- **Developed by:** [ConvergingMachine](https://github.com/converging-machine)
+- **Model type:** Convolutional Neural Networks (ResNet variants)
+- **License:** Apache-2.0
+- **Finetuned from model:** RadImageNet
+
+## Performance Comparison of ResNet Models
+
+This table compares the performance of ResNet models pretrained on 2D RadImageNet using regular and Two2Three convolution techniques across various metrics.
+
+| Model      | Precision (macro) | Recall (macro) | F1 Score (macro) | Balanced Accuracy | Average Accuracy |
+|------------|-------------------|----------------|------------------|-------------------|------------------|
+| ResNet10t  | 0.4720            | 0.3848         | 0.3998           | 0.3848            | 0.7981           |
+| ResNet18   | 0.5150            | 0.4383         | 0.4545           | 0.4383            | 0.8177           |
+| ResNet50   | 0.5563            | 0.4934         | 0.5097           | 0.4934            | 0.8352           |
+
+
+
+## Usage
+
+To utilize the Refined-RadImageNet models in your project, follow these steps:
+
+1. **Install Git LFS (Large File Storage):**
+
+   ```bash
+   git lfs install
+   ```
+
+2. **Clone the Repository:**
+
+   ```bash
+   git clone https://huggingface.co/ogrenenmakine/Refined-RadImagenet source/
+   ```
+
+   This command clones the repository into a directory named `source`. 
+
+3. **Load the Model:**
+
+   The trained models are implemented using the `timm` library. Here's how to load a model:
+
+   ```python
+   import timm
+   model = timm.create_model('resnet10t', num_classes=165)
+   ```
+
+   Replace `'resnet10t'` with `'resnet18'` or `'resnet50'` as needed. 
+
+## Dataset
+
+The RadImageNet dataset includes medical images of 3 modalities, 11 anatomies, and 165 pathologic labels. It is available upon request at [www.radimagenet.com](https://www.radimagenet.com/). 
+
+## Citation
+
+If you use this code or the Refined-RadImageNet models in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{yavuz2025policy,
+  title={Policy Gradient-Driven Noise Mask},
+  author={Yavuz, Mehmet Can and Yang, Yang},
+  booktitle={International Conference on Pattern Recognition},
+  pages={414--431},
+  year={2025},
+  organization={Springer}
+}
+```
+
+## License
+
+This project is licensed under the Apache-2.0 License. 
+
+## Acknowledgements
+
+Special thanks to the contributors of the [Refined-RadImageNet GitHub repository](https://github.com/converging-machine/Refined-RadImagenet) and the developers of the [RadImageNet dataset](https://github.com/BMEII-AI/RadImageNet).
+
+```
+
+This README provides an overview of the Refined-RadImageNet models, including their performance metrics, usage instructions, dataset information, citation details, and acknowledgments. It follows the structure recommended by Hugging Face's model card guidelines.
+
+---
+license: cc-by-2.0
+---