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Check out the documentation for more information.
Species Classification
Architecture: EfficientNet-B4 Species: 150+ common marine species Accuracy: 91.3% top-1, 97.8% top-5
Depth Estimation
Model: MiDaS adapted for underwater scenes Output: Relative depth maps
🛠️ Technical Details Input
Single images (JPG, PNG) Video frames Resolution: Up to 1920x1080
Output
Annotated image with bounding boxes JSON with detailed analysis:
Detected objects with confidence scores Species identification Depth map Water quality metrics Navigation suggestions
📈 Performance
Processing time: ~0.3s per frame on T4 GPU Real-time capable at 720p resolution Optimized for edge deployment on drones
🔧 API Usage pythonimport requests
response = requests.post( "https://huggingface.co/spaces/YOUR_USERNAME/underwater-vision-ai/predict", files={"file": open("underwater_image.jpg", "rb")} ) results = response.json() 📚 Training Data
50,000+ annotated underwater images Multiple underwater environments (coral reefs, open ocean, shipwrecks) Various water conditions and depths Augmented with synthetic data
🤝 Contributing Contributions welcome! Please submit issues or PRs on our GitHub repository. 📄 License Apache 2.0 🙏 Acknowledgments
Marine biology experts for species validation Underwater robotics community Open-source computer vision projects