title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license
Rubiks Cube Recognition	🎲	blue	red	gradio	4.19.2	app.py	false	apache-2.0

🎲 Rubik's Cube Recognition with TensorFlow RetinaNet

This is a deep learning model that can recognize and analyze Rubik's cubes in images. The model is built using TensorFlow and RetinaNet architecture for object detection.

🚀 Features

• Real-time Detection: Upload images of Rubik's cubes for instant analysis
• Multi-class Recognition: Detect cube faces and 6 different color tiles
• Interactive Interface: Simple and intuitive Gradio web interface
• Advanced AI: Powered by RetinaNet with SpineNet-49 backbone

🎯 How to Use

1. Upload: Click "Upload Rubik's Cube Image" and select your image
2. Analyze: Click "🔍 Analyze Image" or wait for automatic processing
3. Results: View detection results and visualization with bounding boxes

🔬 Technical Details

• Framework: TensorFlow 2.15+ with Gradio interface
• Architecture: RetinaNet with SpineNet-49 backbone
• Input Size: 640×640 pixels
• Classes: 7 total (1 face + 6 color tiles)
• Colors Detected: Red, White, Blue, Orange, Green, Yellow

🌟 Model Architecture

RetinaNet-SpineNet-49

• Base Model: RetinaNet for object detection
• Backbone: SpineNet-49 for feature extraction
• Input Resolution: 640×640×3
• Output: Bounding boxes with class predictions and confidence scores

Detection Classes

1. face - Rubik's cube face
2. red_tile - Red color tile
3. white_tile - White color tile
4. blue_tile - Blue color tile
5. orange_tile - Orange color tile
6. green_tile - Green color tile
7. yellow_tile - Yellow color tile

📊 Performance Metrics

Metric	Target	Status
mAP@0.5	>0.85	In Training
Inference Speed	<100ms	Optimized
Accuracy	>90%	Evaluating

🛠️ Local Development

# Clone the repository
git clone https://huggingface.co/spaces/itsyuimorii/rubiks-cube-recognition
cd rubiks-cube-recognition

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

📁 Project Structure

rubiks-cube-recognition/
├── app.py                    # Main Gradio application
├── requirements.txt          # Python dependencies
├── README.md                # This file
├── configs/                 # Model configurations
│   └── retinanet_config.py
├── src/                     # Source code
│   ├── data/               # Data processing utilities
│   └── model/              # Model training and inference
└── images/                 # Training and test datasets
    ├── train/              # Training images and annotations
    ├── test/               # Test images and annotations
    └── valid/              # Validation images and annotations

🎮 Demo Status

⚠️ Note: This is a demo version. The complete trained model is currently being developed. The interface will show a preview of the detection capabilities.

📝 Dataset Information

• Format: COCO annotation format
• Image Size: 640×640 pixels
• Training Images: 50+ annotated cube images
• Classes: 7 object classes (face + 6 colors)
• Annotation Tool: LabelMe

🔧 Training Pipeline

# Training command
python src/model/trainer.py --config configs/retinanet_config.py

# Inference command  
python src/model/visualize.py --image path/to/cube_image.jpg

🤝 Contributing

Contributions are welcome! Areas for improvement:

• Additional training data
• Model optimization
• UI/UX enhancements
• Performance improvements

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgements

• TensorFlow Model Garden - RetinaNet implementation
• SpineNet - Backbone architecture
• Gradio - Web interface framework
• Hugging Face - Model hosting and deployment

📧 Contact

• GitHub: @itsyuimorii
• Hugging Face: @itsyuimorii

🔗 References

• RetinaNet Paper
• SpineNet Architecture
• TensorFlow Object Detection API
• LabelMe Annotation Tool

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🎲 Ready to solve your Rubik's cube detection challenges!