README.md

---
title: Satellite Classification Dashboard
emoji: 🛰️
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: "1.47.0"
app_file: app.py
pinned: false
preload_from_hub:
  - Bhavi23/Custom_CNN/best_multimodal_model.keras
  - Bhavi23/MobilenetV2/multi_input_model_v1.keras
  - Bhavi23/EfficientNet_B0/efficientnet_model.keras
  - Bhavi23/DenseNet/densenet_model.keras
---

# 🛰️ 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
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
Visit the Hugging Face Space to use the application directly in your browser.

### Local Installation
1. Clone the repository
```bash
git clone https://github.com/YOUR_USERNAME/satellite-classification-dashboard.git
cd satellite-classification-dashboard
```

2. Install dependencies
```bash
pip install -r requirements.txt
```

3. Run the application
```bash
streamlit run app.py
```

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
The application is deployed on Hugging Face Spaces using:
- **Runtime**: Python 3.9
- **Framework**: Streamlit
- **Resources**: CPU-optimized for inference

### Docker Deployment
```dockerfile
FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
EXPOSE 7860

CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
```

## 🤝 Contributing
We welcome contributions! Please follow these steps:

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

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements.txt

# Run in development mode
streamlit run app.py --logger.level=debug
```

## 📄 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
Track your application usage:
- **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](https://huggingface.co/spaces/Bhavi23/satellite-classification-dashboard)