Update README.md

README.md

@@ -1,3 +1,4 @@
+---
 title: Satellite Classification Dashboard
 emoji: 🛰️
 colorFrom: blue
@@ -11,129 +12,165 @@ preload_from_hub:
   - Bhavi23/MobilenetV2/multi_input_model_v1.keras
   - Bhavi23/EfficientNet_B0/efficientnet_model.keras
   - Bhavi23/DenseNet/densenet_model.keras
+---
+
+# 🛰️ Satellite Classification Dashboard
 
-🛰️ Satellite Classification Dashboard
 A comprehensive deep learning application for classifying satellite images using multiple state-of-the-art neural network models. This interactive dashboard allows you to compare different models, analyze their performance, and classify satellite images in real-time.
 
-🌟 Features
-🏠 Dashboard Overview
-Interactive web interface built with Streamlit
-Multi-page navigation system
-Real-time image classification
-Comprehensive model comparison
-Performance analytics and visualizations
-🤖 Multiple Model Support
-Custom CNN: Tailored architecture for satellite imagery (95.2% accuracy)
-MobileNetV2: Lightweight model optimized for speed (92.8% accuracy, 18ms inference)
-EfficientNetB0: Best accuracy-efficiency balance (96.4% accuracy)
-DenseNet121: Complex pattern recognition capabilities (94.7% accuracy)
-🎯 Classification Categories
+## 🌟 Features
+
+### 🏠 Dashboard Overview
+- Interactive web interface built with Streamlit
+- Multi-page navigation system
+- Real-time image classification
+- Comprehensive model comparison
+- Performance analytics and visualizations
+
+### 🤖 Multiple Model Support
+- **Custom CNN**: Tailored architecture for satellite imagery (95.2% accuracy)
+- **MobileNetV2**: Lightweight model optimized for speed (92.8% accuracy, 18ms inference)
+- **EfficientNetB0**: Best accuracy-efficiency balance (96.4% accuracy)
+- **DenseNet121**: Complex pattern recognition capabilities (94.7% accuracy)
+
+### 🎯 Classification Categories
 The system can classify 11 different types of satellites and space objects:
 
-AcrimSat
-Aquarius
-Aura
-Calipso
-Cloudsat
-CubeSat
-Debris
-Jason
-Sentinel-6
-TRMM
-Terra
-🚀 Quick Start
-Try the Live Demo
+- AcrimSat
+- Aquarius
+- Aura
+- Calipso
+- Cloudsat
+- CubeSat
+- Debris
+- Jason
+- Sentinel-6
+- TRMM
+- Terra
+
+## 🚀 Quick Start
+
+### Try the Live Demo
 Visit the Hugging Face Space to use the application directly in your browser.
 
-Local Installation
-Clone the repository
+### Local Installation
+1. Clone the repository
+```bash
 git clone https://github.com/YOUR_USERNAME/satellite-classification-dashboard.git
 cd satellite-classification-dashboard
-Install dependencies
+```
+
+2. Install dependencies
+```bash
 pip install -r requirements.txt
-Run the application
+```
+
+3. Run the application
+```bash
 streamlit run app.py
-Open your browser and navigate to http://localhost:7860
-📦 Dependencies
-streamlit==1.47.0
-tensorflow-cpu==2.15.0
-numpy==1.26.4
-pandas==2.2.2
-plotly==5.22.0
-Pillow==10.4.0
-requests==2.32.3
-protobuf<4.0.0
-🎮 How to Use
-1. Home Page
-Overview of the system capabilities
-Class distribution visualization
-Quick navigation to other features
-2. Model Comparison
-Side-by-side performance metrics
-Interactive charts comparing accuracy, speed, and model size
-Model selection recommendations
-3. Image Classification
-Upload satellite images (PNG, JPG, JPEG)
-Select one or multiple models for prediction
-View confidence scores and detailed probabilities
-Get model recommendations based on performance
-4. Performance Analytics
-Detailed performance breakdowns
-Efficiency metrics analysis
-Comprehensive model comparison tables
-5. About Models
-Technical specifications for each model
-Strengths and use cases
-Model architecture details
-📊 Model Performance
-Model	Accuracy	Precision	Recall	F1-Score	Inference Time	Model Size
-EfficientNetB0	96.4%	96.1%	96.2%	96.1%	35ms	20.1MB
-Custom CNN	95.2%	94.8%	95.1%	94.9%	45ms	25.3MB
-DenseNet121	94.7%	94.2%	94.5%	94.3%	52ms	32.8MB
-MobileNetV2	92.8%	92.1%	92.5%	92.3%	18ms	8.7MB
-🎯 Model Selection Guide
-🏆 Best Overall Accuracy: EfficientNetB0 (96.4%)
-⚡ Fastest Inference: MobileNetV2 (18ms)
-💾 Most Lightweight: MobileNetV2 (8.7MB)
-🎯 Best Balance: EfficientNetB0 (High accuracy + efficiency)
-📱 Mobile/Edge Deployment: MobileNetV2
-🔬 Research/High Accuracy: EfficientNetB0 or DenseNet121
-🏗️ Architecture
-Model Sources
+```
+
+4. Open your browser and navigate to http://localhost:7860
+
+## 📦 Dependencies
+- streamlit==1.47.0
+- tensorflow-cpu==2.15.0
+- numpy==1.26.4
+- pandas==2.2.2
+- plotly==5.22.0
+- Pillow==10.4.0
+- requests==2.32.3
+- protobuf<4.0.0
+
+## 🎮 How to Use
+
+### 1. Home Page
+- Overview of the system capabilities
+- Class distribution visualization
+- Quick navigation to other features
+
+### 2. Model Comparison
+- Side-by-side performance metrics
+- Interactive charts comparing accuracy, speed, and model size
+- Model selection recommendations
+
+### 3. Image Classification
+- Upload satellite images (PNG, JPG, JPEG)
+- Select one or multiple models for prediction
+- View confidence scores and detailed probabilities
+- Get model recommendations based on performance
+
+### 4. Performance Analytics
+- Detailed performance breakdowns
+- Efficiency metrics analysis
+- Comprehensive model comparison tables
+
+### 5. About Models
+- Technical specifications for each model
+- Strengths and use cases
+- Model architecture details
+
+## 📊 Model Performance
+
+| Model | Accuracy | Precision | Recall | F1-Score | Inference Time | Model Size |
+|-------|----------|-----------|---------|----------|----------------|------------|
+| EfficientNetB0 | 96.4% | 96.1% | 96.2% | 96.1% | 35ms | 20.1MB |
+| Custom CNN | 95.2% | 94.8% | 95.1% | 94.9% | 45ms | 25.3MB |
+| DenseNet121 | 94.7% | 94.2% | 94.5% | 94.3% | 52ms | 32.8MB |
+| MobileNetV2 | 92.8% | 92.1% | 92.5% | 92.3% | 18ms | 8.7MB |
+
+## 🎯 Model Selection Guide
+- 🏆 **Best Overall Accuracy**: EfficientNetB0 (96.4%)
+- ⚡ **Fastest Inference**: MobileNetV2 (18ms)
+- 💾 **Most Lightweight**: MobileNetV2 (8.7MB)
+- 🎯 **Best Balance**: EfficientNetB0 (High accuracy + efficiency)
+- 📱 **Mobile/Edge Deployment**: MobileNetV2
+- 🔬 **Research/High Accuracy**: EfficientNetB0 or DenseNet121
+
+## 🏗️ Architecture
+
+### Model Sources
 All models are hosted on Hugging Face Model Hub:
-
-Custom CNN: Bhavi23/Custom_CNN
-MobileNetV2: Bhavi23/MobilenetV2
-EfficientNetB0: Bhavi23/EfficientNet_B0
-DenseNet121: Bhavi23/DenseNet
-Data Pipeline
-Image Upload: Supports PNG, JPG, JPEG formats
-Preprocessing: Resize to 224x224, normalize to [0,1]
-Model Inference: Multi-model prediction with timing
-Post-processing: Confidence scoring and recommendations
-🔧 Technical Details
-Input Requirements
-Image Format: PNG, JPG, JPEG
-Input Size: 224x224x3 (RGB)
-Preprocessing: Automatic resizing and normalization
-Output Format
-Class Prediction: One of 11 satellite categories
-Confidence Score: Percentage confidence (0-100%)
-Inference Time: Milliseconds for prediction
-Probability Distribution: Full softmax output for all classes
-📈 Performance Optimization
-Model Caching: Models are cached using @st.cache_resource
-Lazy Loading: Models loaded only when needed
-Efficient Preprocessing: Optimized image pipeline
-Memory Management: Automatic cleanup of model objects
-🚢 Deployment
-Hugging Face Spaces
+- Custom CNN: Bhavi23/Custom_CNN
+- MobileNetV2: Bhavi23/MobilenetV2
+- EfficientNetB0: Bhavi23/EfficientNet_B0
+- DenseNet121: Bhavi23/DenseNet
+
+### Data Pipeline
+- **Image Upload**: Supports PNG, JPG, JPEG formats
+- **Preprocessing**: Resize to 224x224, normalize to [0,1]
+- **Model Inference**: Multi-model prediction with timing
+- **Post-processing**: Confidence scoring and recommendations
+
+## 🔧 Technical Details
+
+### Input Requirements
+- **Image Format**: PNG, JPG, JPEG
+- **Input Size**: 224x224x3 (RGB)
+- **Preprocessing**: Automatic resizing and normalization
+
+### Output Format
+- **Class Prediction**: One of 11 satellite categories
+- **Confidence Score**: Percentage confidence (0-100%)
+- **Inference Time**: Milliseconds for prediction
+- **Probability Distribution**: Full softmax output for all classes
+
+## 📈 Performance Optimization
+- **Model Caching**: Models are cached using @st.cache_resource
+- **Lazy Loading**: Models loaded only when needed
+- **Efficient Preprocessing**: Optimized image pipeline
+- **Memory Management**: Automatic cleanup of model objects
+
+## 🚢 Deployment
+
+### Hugging Face Spaces
 The application is deployed on Hugging Face Spaces using:
+- **Runtime**: Python 3.9
+- **Framework**: Streamlit
+- **Resources**: CPU-optimized for inference
 
-Runtime: Python 3.9
-Framework: Streamlit
-Resources: CPU-optimized for inference
-Docker Deployment
+### Docker Deployment
+```dockerfile
 FROM python:3.9-slim
 
 WORKDIR /app
@@ -144,15 +181,19 @@ COPY . .
 EXPOSE 7860
 
 CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
-🤝 Contributing
+```
+
+## 🤝 Contributing
 We welcome contributions! Please follow these steps:
 
-Fork the repository
-Create a feature branch (git checkout -b feature/amazing-feature)
-Commit changes (git commit -m 'Add amazing feature')
-Push to branch (git push origin feature/amazing-feature)
-Open a Pull Request
-Development Setup
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit changes (`git commit -m 'Add amazing feature'`)
+4. Push to branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+### Development Setup
+```bash
 # Clone your fork
 git clone https://github.com/YOUR_USERNAME/satellite-classification-dashboard.git
 
@@ -165,33 +206,40 @@ pip install -r requirements.txt
 
 # Run in development mode
 streamlit run app.py --logger.level=debug
-📄 License
+```
+
+## 📄 License
 This project is licensed under the MIT License - see the LICENSE file for details.
 
-🙏 Acknowledgments
-Dataset: Spark 2021 dataset used for training
-Frameworks: TensorFlow, Streamlit, Plotly
-Models: Pre-trained architectures from TensorFlow/Keras
-Hosting: Hugging Face Spaces for deployment
-📞 Support
-Issues: GitHub Issues
-Discussions: GitHub Discussions
-Email: bhavithrass@gmail.com
-🔮 Future Enhancements
- Real-time Video Classification
- Batch Processing for multiple images
- Model Fine-tuning Interface
- Custom Model Upload
- Export Predictions (CSV/JSON)
- API Endpoint for programmatic access
- Mobile App version
- Advanced Visualization with satellite orbit data
-📊 Usage Statistics
+## 🙏 Acknowledgments
+- **Dataset**: Spark 2021 dataset used for training
+- **Frameworks**: TensorFlow, Streamlit, Plotly
+- **Models**: Pre-trained architectures from TensorFlow/Keras
+- **Hosting**: Hugging Face Spaces for deployment
+
+## 📞 Support
+- **Issues**: GitHub Issues
+- **Discussions**: GitHub Discussions
+- **Email**: bhavithrass@gmail.com
+
+## 🔮 Future Enhancements
+- [ ] Real-time Video Classification
+- [ ] Batch Processing for multiple images
+- [ ] Model Fine-tuning Interface
+- [ ] Custom Model Upload
+- [ ] Export Predictions (CSV/JSON)
+- [ ] API Endpoint for programmatic access
+- [ ] Mobile App version
+- [ ] Advanced Visualization with satellite orbit data
+
+## 📊 Usage Statistics
 Track your application usage:
+- **Classifications Performed**: Real-time counter
+- **Popular Models**: Usage analytics
+- **Performance Metrics**: Response time tracking
+
+---
 
-Classifications Performed: Real-time counter
-Popular Models: Usage analytics
-Performance Metrics: Response time tracking
 Built with ❤️ using Streamlit and TensorFlow
 
-For more information, visit our Hugging Face Space
+For more information, visit our [Hugging Face Space](https://huggingface.co/spaces/Bhavi23/satellite-classification-dashboard)