docs(README): update image paths and add resources section

README.md

@@ -29,7 +29,7 @@ sdk_version: 5.44.1
 # 🔬 Interactive Demo: Hybrid Transformer for Multi-Focus Image Fusion
 
 <div align="center">
-  <img src="https://github.com/DivitMittal/HybridTransformer-MFIF/raw/main/assets/logo.png" alt="HybridTransformer MFIF Logo" width="400"/>
+  <img src="./assets/logo.png" alt="HybridTransformer MFIF Logo" width="400"/>
 
   [![Model](https://img.shields.io/badge/🤗%20Model-HybridTransformer--MFIF-yellow)](https://huggingface.co/divitmittal/HybridTransformer-MFIF)
   [![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/DivitMittal/HybridTransformer-MFIF)
@@ -44,6 +44,16 @@ sdk_version: 5.44.1
 
 > 💡 **New to multi-focus fusion?** It's like having a camera that can focus on everything at once! Perfect for photography, microscopy, and document scanning.
 
+## 🔗 Project Resources
+
+| Resource | Purpose | Best For | Link |
+|----------|---------|----------|------|
+| 🚀 **This Demo** | Interactive testing | Quick experimentation | *You're here!* |
+| 🤗 **Model Hub** | Pre-trained weights | Integration & deployment | [Download Model](https://huggingface.co/divitmittal/HybridTransformer-MFIF) |
+| 📁 **GitHub Repository** | Source code & docs | Development & research | [View Code](https://github.com/DivitMittal/HybridTransformer-MFIF) |
+| 📊 **Kaggle Notebook** | Training pipeline | Learning & custom training | [Launch Notebook](https://www.kaggle.com/code/divitmittal/hybrid-transformer-mfif) |
+| 📦 **Training Dataset** | Lytro Multi-Focus data | Research & benchmarking | [Download Dataset](https://www.kaggle.com/datasets/divitmittal/lytro-multi-focal-images) |
+
 ## 🚀 How to Use This Demo
 
 ### Quick Start (30 seconds)
@@ -84,7 +94,7 @@ Our **FocalCrossViTHybrid** model represents a breakthrough in AI-powered image
 ### 🏗️ Architecture Deep Dive
 
 <div align="center">
-  <img src="https://github.com/DivitMittal/HybridTransformer-MFIF/raw/main/assets/model_architecture.png" alt="FocalCrossViTHybrid Architecture" width="700"/>
+  <img src="./assets/model_architecture.png" alt="FocalCrossViTHybrid Architecture" width="700"/>
   <p><em>Complete architecture diagram showing the hybrid transformer pipeline</em></p>
 </div>
 
@@ -142,16 +152,6 @@ Our model was meticulously trained on the **Lytro Multi-Focus Dataset** using st
 - **Historical Preservation**: Create high-quality digital archives
 - **Technical Documentation**: Clear images of complex 3D objects
 
-## 🔗 Complete Project Ecosystem
-
-| Resource | Purpose | Best For | Link |
-|----------|---------|----------|------|
-| 🚀 **This Demo** | Interactive testing | Quick experimentation | *You're here!* |
-| 🤗 **Model Hub** | Pre-trained weights | Integration & deployment | [Download Model](https://huggingface.co/divitmittal/HybridTransformer-MFIF) |
-| 📁 **GitHub Repository** | Source code & docs | Development & research | [View Code](https://github.com/DivitMittal/HybridTransformer-MFIF) |
-| 📊 **Kaggle Notebook** | Training pipeline | Learning & custom training | [Launch Notebook](https://www.kaggle.com/code/divitmittal/hybrid-transformer-mfif) |
-| 📦 **Training Dataset** | Lytro Multi-Focus data | Research & benchmarking | [Download Dataset](https://www.kaggle.com/datasets/divitmittal/lytro-multi-focal-images) |
-
 
 ## 🛠️ Run This Demo Locally
 
@@ -236,89 +236,27 @@ docker run -p 7860:7860 hybrid-transformer-demo
 2. **Product Photography**: Ensure all product features are in sharp focus
 3. **Art Documentation**: Capture textured surfaces with varying depths
 
-## 📈 Live Demo Performance
-
-### ⚡ Speed & Efficiency
-- **Processing Time**: ~2-3 seconds per image pair (with GPU)
-- **CPU Fallback**: ~8-12 seconds (when GPU unavailable)
-- **Memory Usage**: <2GB RAM for standard operation
-- **Concurrent Users**: Supports multiple simultaneous users
-- **Auto-scaling**: Handles traffic spikes gracefully
-
-### 🎯 Quality Assurance
-- **Consistent Results**: Same inputs always produce identical outputs
-- **Error Handling**: Graceful handling of invalid inputs
-- **Format Support**: JPEG, PNG, WebP, and most common formats
-- **Size Limits**: Automatic resizing for optimal processing
-- **Quality Preservation**: Maintains maximum possible image quality
-
-### 📊 Real-time Metrics (Displayed in Demo)
-- **Fusion Quality Score**: Overall fusion effectiveness (0-100)
-- **Focus Transfer Rate**: How well focus regions are preserved (%)
-- **Edge Preservation**: Sharpness retention metric
-- **Processing Time**: Actual computation time for your images
-
-## 🔬 Research & Development
-
-### 📚 Academic Value
-- **Novel Architecture**: First implementation combining Focal Transformer + CrossViT for MFIF
-- **Reproducible Research**: Complete codebase with deterministic training
-- **Benchmark Dataset**: Standard evaluation on Lytro Multi-Focus Dataset
-- **Comprehensive Metrics**: 6+ evaluation metrics for thorough assessment
-
-### 🧪 Experimental Framework
-- **Modular Design**: Easy to modify components for ablation studies
-- **Hyperparameter Tuning**: Configurable architecture and training parameters
-- **Extension Support**: Framework for adding new transformer components
-- **Comparative Analysis**: Built-in tools for method comparison
-
-### 📖 Educational Resource
-- **Step-by-step Tutorials**: From basic concepts to advanced implementation
-- **Interactive Learning**: Hands-on experience with transformer architectures
-- **Code Documentation**: Extensively commented for educational use
-- **Research Integration**: Easy to incorporate into academic projects
-
-## 🤝 Community & Support
-
-### 💬 Get Help
-- **GitHub Issues**: Report bugs or request features
-- **HuggingFace Discussions**: Community Q&A and tips
-- **Kaggle Comments**: Dataset and training discussions
-- **Email Support**: Direct contact for collaboration inquiries
-
-### 🔄 Contributing
-- **Code Contributions**: Submit PRs for improvements
-- **Dataset Expansion**: Help grow the training data
-- **Documentation**: Improve guides and tutorials
-- **Testing**: Report issues and edge cases
-
-### 🏷️ Citation
+## 🛠️ Technical Details
+
+- **Processing Time**: ~2-3 seconds per image pair (GPU) / ~8-12 seconds (CPU)
+- **Model Size**: 73M+ parameters with hybrid transformer architecture
+- **Input**: 224×224 pixel images (automatically resized)
+- **Performance**: State-of-the-art results on Lytro Multi-Focus Dataset
+
+## 📄 Citation & License
+
+**MIT License** - Free for commercial and non-commercial use.
+
 If you use this work in your research:
 ```bibtex
 @software{mittal2024hybridtransformer,
   title={HybridTransformer-MFIF: Focal Transformer and CrossViT Hybrid for Multi-Focus Image Fusion},
   author={Mittal, Divit},
   year={2024},
-  url={https://github.com/DivitMittal/HybridTransformer-MFIF},
-  note={Interactive demo available at HuggingFace Spaces}
+  url={https://github.com/DivitMittal/HybridTransformer-MFIF}
 }
 ```
 
-## 📄 License & Terms
-
-### 📜 Open Source License
-**MIT License** - Free for commercial and non-commercial use
-- ✅ **Commercial Use**: Integrate into products and services
-- ✅ **Modification**: Adapt and customize for your needs
-- ✅ **Distribution**: Share with proper attribution
-- ✅ **Private Use**: Use in proprietary projects
-
-### ⚖️ Usage Terms
-- **Attribution Required**: Credit the original work when using
-- **No Warranty**: Provided "as-is" without guarantees
-- **Ethical Use**: Please use responsibly and ethically
-- **Research Friendly**: Encouraged for academic and research purposes
-
 ---
 
 <div align="center">