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README.md

@@ -1,3 +1,101 @@
----
-license: mit
----
+---
+license: mit
+tags:
+- medical
+- radiology
+- image-classification
+- pytorch
+- radimagenet
+library_name: pytorch
+---
+
+# RadImageNet Pre-trained Models
+
+This repository contains pre-trained models from RadImageNet, a large-scale radiologic image dataset designed to facilitate transfer learning for medical imaging applications.
+
+## Model Description
+
+RadImageNet models are convolutional neural networks pre-trained on a diverse collection of radiologic images spanning multiple modalities and anatomical regions. These models serve as powerful feature extractors for downstream medical imaging tasks.
+
+### Available Models
+
+- **ResNet50.pt**: ResNet-50 architecture pre-trained on RadImageNet
+- **DenseNet121.pt**: DenseNet-121 architecture pre-trained on RadImageNet
+- **InceptionV3.pt**: Inception-V3 architecture pre-trained on RadImageNet
+
+## Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download and load a model
+model_path = hf_hub_download(repo_id="Lab-Rasool/RadImageNet", filename="ResNet50.pt")
+model = torch.load(model_path, map_location="cuda" if torch.cuda.is_available() else "cpu")
+model.eval()
+
+# Use for inference
+# ... your inference code here ...
+```
+
+## Preprocessing
+
+Images should be preprocessed using standard ImageNet normalization:
+
+```python
+from torchvision import transforms
+
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+```
+
+## Citation
+
+If you use these models in your research, please cite the RadImageNet paper:
+
+```bibtex
+@article{mei2022radimagenet,
+  title={RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning},
+  author={Mei, Xueyan and Liu, Zelong and Robson, Philip M and Marinelli, Brett and Huang, Mingqian and Doshi, Amish and Jacobi, Adam and Cao, Chendi and Link, Katherine E and Yang, Thomas and others},
+  journal={Radiology: Artificial Intelligence},
+  volume={4},
+  number={5},
+  pages={e210315},
+  year={2022},
+  publisher={Radiological Society of North America}
+}
+```
+
+## License
+
+MIT License
+
+Copyright (c) 2021 BMEII-AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+## Additional Information
+
+- **Original Repository**: [BMEII-AI/RadImageNet](https://github.com/BMEII-AI/RadImageNet)
+- **Paper**: [RadImageNet: An Open Radiologic Deep Learning Research Dataset](https://pubs.rsna.org/doi/10.1148/ryai.210315)
+- **Dataset**: The RadImageNet dataset contains 1.35 million annotated radiologic images