Update README with GitHub repo links

README.md

@@ -21,14 +21,17 @@ library_name: pytorch
 
 # MRI Brain Tumor Classification Models
 
-This repository contains trained deep learning models for MRI brain scan classification, developed using Mojo and PyTorch.
+[![GitHub](https://img.shields.io/badge/GitHub-COMMRI-blue?logo=github)](https://github.com/Meidverse/COMMRI)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+This repository contains trained deep learning models for MRI brain scan classification, developed using **Mojo 🔥** and **PyTorch**.
+
+**Full source code and training scripts:** [github.com/Meidverse/COMMRI](https://github.com/Meidverse/COMMRI)
 
 ## Models
 
 ### 1. Brain Tumor 2D CNN (`kaggle_tumor_2dcnn_best.pth`)
 
-**Task:** Multi-class brain tumor classification from MRI slices
-
 | Metric | Value |
 |--------|-------|
 | **Accuracy** | 93.95% |
@@ -36,28 +39,65 @@ This repository contains trained deep learning models for MRI brain scan classif
 | **Recall** | 0.94 |
 | **F1 Score** | 0.94 |
 
-**Classes:**
-- `glioma` - 98.1% accuracy
-- `meningioma` - 83.9% accuracy  
-- `notumor` - 98.5% accuracy
-- `pituitary` - 94.3% accuracy
-
-**Architecture:** 2D CNN with 4 convolutional blocks, 4.85M parameters
+**Per-Class Performance:**
+| Class | Accuracy |
+|-------|----------|
+| Glioma | 98.1% |
+| Meningioma | 83.9% |
+| No Tumor | 98.5% |
+| Pituitary | 94.3% |
 
 ### 2. IXI 3D Brain CNN (`ixi_3dcnn_best.pth`)
 
-**Task:** 3D brain MRI volume classification  
-**Architecture:** 3D CNN with 4 Conv3D blocks, 1.2M parameters
+3D CNN for brain volume classification from NIfTI files.
 
 ## Quick Start
 
+### Option 1: Clone from GitHub (Recommended)
+
+```bash
+git clone https://github.com/Meidverse/COMMRI.git
+cd COMMRI
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run inference
+python -c "
+import torch
+from scripts.train_tumor import TumorCNN
+
+model = TumorCNN(4)
+model.load_state_dict(torch.load('kaggle_tumor_2dcnn_best.pth'))
+model.eval()
+print('Model loaded!')
+"
+```
+
+### Option 2: Download from Hugging Face
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="Nikshey/mri-brain-classification",
+    filename="kaggle_tumor_2dcnn_best.pth"
+)
+
+# Load with PyTorch
+import torch
+model = torch.load(model_path)
+```
+
+## Inference Example
+
 ```python
 import torch
 import torch.nn as nn
 from torchvision import transforms
 from PIL import Image
 
-# Define model
 class TumorCNN(nn.Module):
     def __init__(self, num_classes=4):
         super().__init__()
@@ -82,11 +122,12 @@ class TumorCNN(nn.Module):
     def forward(self, x):
         return self.classifier(self.features(x))
 
-# Load and predict
+# Load model
 model = TumorCNN(4)
 model.load_state_dict(torch.load("kaggle_tumor_2dcnn_best.pth", map_location="cpu"))
 model.eval()
 
+# Preprocess and predict
 transform = transforms.Compose([
     transforms.Resize((224, 224)),
     transforms.ToTensor(),
@@ -95,14 +136,45 @@ transform = transforms.Compose([
 
 image = transform(Image.open("brain_mri.jpg").convert("RGB")).unsqueeze(0)
 pred = model(image).argmax(1).item()
+
 classes = ['glioma', 'meningioma', 'notumor', 'pituitary']
 print(f"Prediction: {classes[pred]}")
 ```
 
 ## Training
 
-Built with Mojo 🔥 on NVIDIA RTX 4090
+Train your own models using the scripts in the [GitHub repo](https://github.com/Meidverse/COMMRI):
+
+```bash
+# Train tumor classifier
+mojo run scripts/train_tumor.mojo
+
+# Train 3D brain model
+mojo run scripts/train_advanced.mojo
+
+# Evaluate
+mojo run scripts/evaluate_tumor.mojo
+```
+
+## Citation
+
+```bibtex
+@misc{commri2024,
+  author = {Meidverse},
+  title = {COM-MRI: Brain Tumor Classification with Mojo},
+  year = {2024},
+  publisher = {GitHub},
+  url = {https://github.com/Meidverse/COMMRI}
+}
+```
 
 ## License
 
-MIT
+MIT License - See [LICENSE](https://github.com/Meidverse/COMMRI/blob/main/LICENSE)
+
+## Acknowledgments
+
+- [Kaggle Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)
+- [IXI Dataset](https://brain-development.org/ixi-dataset/)
+- Built with [Mojo 🔥](https://www.modular.com/mojo) and PyTorch
+- Trained on NVIDIA RTX 4090