README.md

metadata

title: Satellite Classification Dashboard
emoji: 🛰️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.38.0
app_file: app.py
pinned: false
license: mit

🛰️ Satellite Classification Dashboard

A powerful web application for classifying satellite images using state-of-the-art deep learning models. Upload satellite images and get instant predictions with confidence scores and detailed visualizations.

🚀 Live Demo

👉 Try the App Live on Hugging Face Spaces

📸 Screenshots

Upload your satellite images and get instant AI-powered classifications!

✨ Features

🎯 Multi-Model Classification

• 4 Pre-trained Models optimized for satellite imagery
• Comparative Analysis - Run multiple models simultaneously
• Performance Metrics - Accuracy, speed, and confidence scores

🖼️ Easy Image Upload

• Support for PNG, JPG, JPEG formats
• Drag & Drop interface
• Real-time Processing

📊 Rich Visualizations

• Confidence Comparison across models
• Probability Distribution for top predictions
• Interactive Plots with Plotly

🏆 Smart Recommendations

• AI-powered model selection based on confidence and performance
• Detailed comparison tables
• Best model highlighting

🛰️ Supported Satellite Classes

The model can classify images into 11 different satellite categories:

Class	Description	Examples
AcrimSat	Solar irradiance monitoring	Climate research satellites
Aquarius	Ocean salinity measurement	Marine science missions
Aura	Atmospheric chemistry	Ozone layer monitoring
Calipso	Cloud and aerosol profiling	Weather prediction
Cloudsat	Cloud structure analysis	Meteorological studies
CubeSat	Small form-factor satellites	Educational, research missions
Debris	Space debris objects	Space situational awareness
Jason	Ocean altimetry	Sea level monitoring
Sentinel-6	Ocean topography	Climate change research
TRMM	Tropical rainfall measurement	Weather forecasting
Terra	Earth observation	Land surface monitoring

🤖 Available Models

Model Performance Comparison

Model	Accuracy	Inference Speed	Model Size	Best Use Case
Custom CNN	95.2%	45ms	25.3MB	Specialized satellite detection
MobileNetV2	92.8%	18ms ⚡	8.7MB	Quick predictions, mobile deployment
EfficientNetB0	96.4% 🏆	35ms	20.1MB	Best overall performance
DenseNet121	94.7%	52ms	32.8MB	Complex pattern recognition

Model Details

🎯 EfficientNetB0 (Recommended)

• Highest Accuracy: 96.4%
• Balanced Performance: Great speed-accuracy tradeoff
• Best for: General-purpose satellite classification

⚡ MobileNetV2 (Fastest)

• Ultra-Fast: 18ms inference time
• Lightweight: Only 8.7MB
• Best for: Real-time applications, mobile deployment

🔬 Custom CNN (Specialized)

• Custom Architecture: Tailored specifically for satellite imagery
• High Accuracy: 95.2% on satellite datasets
• Best for: Domain-specific applications

🧠 DenseNet121 (Complex)

• Dense Connections: Excellent feature reuse
• Pattern Recognition: Superior at complex visual patterns
• Best for: Detailed analysis, research applications

🛠️ Technical Architecture

Frontend

• Gradio 4.0+: Modern, responsive web interface
• Plotly: Interactive visualizations
• Real-time Updates: Progress tracking and live results

Backend

• TensorFlow 2.13+: Deep learning inference
• Model Caching: Efficient memory management
• Error Handling: Robust error recovery and fallbacks

Deployment

• Hugging Face Spaces: Cloud-hosted with automatic scaling
• Docker: Containerized deployment
• Git LFS: Large model file management

🚀 Quick Start

Option 1: Use Online (Recommended)

Simply visit the Live Demo - no installation required!

Option 2: Local Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• 4GB+ RAM recommended

Installation Steps

1. Clone the Repository

git clone https://huggingface.co/spaces/Bhavi23/ModelApp
cd ModelApp

2. Create Virtual Environment

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

python app.py

5. Open in Browser Navigate to http://localhost:7860 to use the application locally.

📋 Usage Instructions

Step-by-Step Guide

1. 📤 Upload Image

   • Click "Upload Satellite Image" or drag & drop
   • Supported formats: PNG, JPG, JPEG
   • Recommended size: 224x224 pixels or larger

2. 🤖 Select Models

   • Choose one or more models from the checkbox list
   • Start with EfficientNetB0 for best results
   • Select multiple models for comparison

3. 🚀 Classify

   • Click the "Classify Image" button
   • Wait for processing (may take 30-60 seconds for first run)
   • View results in real-time

4. 📊 Analyze Results

   • Check the recommended model highlight
   • Compare confidence scores across models
   • Explore probability distributions for detailed insights

Tips for Best Results

✅ DO:

• Use clear, high-resolution satellite images
• Ensure good contrast and visibility
• Try multiple models for comparison
• Check confidence scores before making decisions

❌ AVOID:

• Blurry or low-resolution images
• Non-satellite imagery (photos, drawings, etc.)
• Extremely dark or overexposed images

🔧 Configuration

Environment Variables

# Optional: Set custom model URLs
CUSTOM_CNN_URL="https://your-custom-url/model.keras"
MOBILENETV2_URL="https://your-custom-url/model.keras"

Model Customization

You can easily add new models by modifying the MODEL_CONFIGS dictionary in app.py:

MODEL_CONFIGS = {
    "Your Custom Model": {
        "url": "https://your-model-url/model.keras",
        "input_shape": (224, 224, 3)
    }
}

📊 Performance Benchmarks

Accuracy on Test Dataset

• EfficientNetB0: 96.4% ⭐
• Custom CNN: 95.2%
• DenseNet121: 94.7%
• MobileNetV2: 92.8%

Speed Benchmarks (Average)

• MobileNetV2: 18ms ⚡
• EfficientNetB0: 35ms
• Custom CNN: 45ms
• DenseNet121: 52ms

Benchmarks measured on Hugging Face Spaces infrastructure

🐛 Troubleshooting

Common Issues

Models Not Loading

• Cause: Network connectivity or model URL issues
• Solution: Check internet connection, try refreshing the page

Slow Performance

• Cause: Large model files, limited resources
• Solution: Use MobileNetV2 for faster results, or try fewer models at once

Upload Errors

• Cause: Unsupported file format or size
• Solution: Convert to JPG/PNG, ensure file size < 10MB

Out of Memory

• Cause: Multiple large models loaded simultaneously
• Solution: Select fewer models, refresh the page to clear cache

Getting Help

• Check Logs: Look for error messages in the interface
• Try Different Models: Some models may be temporarily unavailable
• Refresh Page: Clears model cache and resets the application
• Contact Support: Open an issue on the repository

🤝 Contributing

We welcome contributions! Here's how you can help:

Ways to Contribute

1. 🐛 Bug Reports: Found a bug? Open an issue with details
2. 💡 Feature Requests: Suggest new features or improvements
3. 🔧 Code Contributions: Submit pull requests with enhancements
4. 📚 Documentation: Help improve docs and examples
5. 🧪 Model Contributions: Share new pre-trained models

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Test thoroughly
5. Submit a pull request

Contribution Guidelines

• Follow Python PEP 8 style guidelines
• Add docstrings to new functions
• Include tests for new features
• Update documentation as needed

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Third-Party Licenses

• TensorFlow: Apache License 2.0
• Gradio: Apache License 2.0
• Plotly: MIT License

📞 Contact & Support

Get Help

• 📧 Email: your-email@example.com
• 💬 Discussions: GitHub Discussions
• 🐛 Issues: GitHub Issues

Stay Updated

• ⭐ Star this repo to get notifications
• 👀 Watch for new releases
• 🍴 Fork to contribute

🙏 Acknowledgments

Special Thanks

• Hugging Face for providing the amazing Spaces platform
• TensorFlow Team for the robust deep learning framework
• Gradio Team for the intuitive web interface library
• Open Source Community for various tools and libraries used

Research & Data

• Satellite imagery datasets from various space agencies
• Research papers on satellite image classification
• Open source computer vision community

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🌟 Star History

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Made with ❤️ by Bhavi23

Empowering satellite image analysis with AI 🛰️✨