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diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -10,4 +10,79 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# LUNGAI: Lung Cancer Detection Model
+
+## Project Overview
+LungAI is a deep learning project aimed at detecting and classifying lung cancer from CT scan images. The model can differentiate between cancerous and non-cancerous lung tissue, as well as classify specific types of lung cancer.
+
+4x hackathon award winner - out of 1,500 total competitors.
+
+[![GitHub](https://img.shields.io/badge/-GitHub-181717?style=for-the-badge&logo=github)](https://github.com/DorsaRoh/LungAI)
+[![HuggingFace](https://img.shields.io/badge/-HuggingFace-FFFF00?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/dorsar/lung-cancer-detection)
+
+
+## Model Performance
+- 98% accuracy in distinguishing between cancerous and non-cancerous cases
+- 83% accuracy in differentiating between four specific types of lung conditions:
+  - Adenocarcinoma: 82% F1-score
+  - Large Cell Carcinoma: 85% F1-score
+  - Normal (non-cancerous): 98% F1-score
+  - Squamous Cell Carcinoma: 76% F1-score
+
+<i>This project represents the newest version, now using PyTorch.</i>
+
+## Repository Structure
+- `Architecture/`: Contains the core model scripts
+  - `architecture.py`: Defines the model architecture
+  - `preprocess.py`: Data preprocessing utilities
+  - `test.py`: Script for testing the model
+- `Model/`: Stores trained model files
+  - `lung_cancer_detection_model.onnx`: ONNX format of the trained model
+  - `lung_cancer_detection_model.pth`: PyTorch weights of the trained model
+- `Data/`: (Not included in repository) Directory for storing the dataset
+- `Processed_Data/`: (Not included in repository) Directory for preprocessed data
+- `assets/`: Additional project assets
+- `requirements.txt`: List of Python dependencies
+
+
+## Setup and Usage
+
+### Step 1: Install Dependencies
+
+First, ensure you have Python installed. Then, install the required Python libraries using the following command:
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 2: Train the Model (Optional)
+
+Run the training script to train the model. 
+**It will be saved as `.pth` and `.onnx` files**
+
+```bash
+python Architecture/architecture.py
+```
+
+### Step 3: Run the Model
+
+Run the model by running the following file:
+
+```bash
+python Architecture/run.py
+```
+
+### Notes
+
+- Make sure your dataset is structured correctly under the Processed_Data directory with subdirectories for training, validation, and testing sets.
+- The model training script expects the dataset to be in the Processed_Data directory. Ensure that the data transformations and directory paths are correctly set up in architecture.py.
+
+### Contributing
+
+If you would like to contribute to this project, please fork the repository and submit a pull request. We welcome improvements, bug fixes, and new features.
+
+## Connect with Me
+
+[![GitHub](https://img.shields.io/badge/-GitHub-181717?style=for-the-badge&logo=github)](https://github.com/DorsaRoh)
+[![Twitter](https://img.shields.io/badge/-Twitter-1DA1F2?style=for-the-badge&logo=twitter)](https://twitter.com/Dorsa_Rohani)
+[![LinkedIn](https://img.shields.io/badge/-LinkedIn-0077B5?style=for-the-badge&logo=linkedin)](https://www.linkedin.com/in/dorsarohani/)
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