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--- |
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title: Rubiks Cube Recognition |
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emoji: 🎲 |
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colorFrom: blue |
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colorTo: red |
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sdk: gradio |
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sdk_version: 4.19.2 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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--- |
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# 🎲 Rubik's Cube Recognition with TensorFlow RetinaNet |
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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. |
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## 🚀 Features |
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- **Real-time Detection**: Upload images of Rubik's cubes for instant analysis |
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- **Multi-class Recognition**: Detect cube faces and 6 different color tiles |
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- **Interactive Interface**: Simple and intuitive Gradio web interface |
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- **Advanced AI**: Powered by RetinaNet with SpineNet-49 backbone |
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## 🎯 How to Use |
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1. **Upload**: Click "Upload Rubik's Cube Image" and select your image |
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2. **Analyze**: Click "🔍 Analyze Image" or wait for automatic processing |
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3. **Results**: View detection results and visualization with bounding boxes |
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## 🔬 Technical Details |
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- **Framework**: TensorFlow 2.15+ with Gradio interface |
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- **Architecture**: RetinaNet with SpineNet-49 backbone |
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- **Input Size**: 640×640 pixels |
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- **Classes**: 7 total (1 face + 6 color tiles) |
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- **Colors Detected**: Red, White, Blue, Orange, Green, Yellow |
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## 🌟 Model Architecture |
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### RetinaNet-SpineNet-49 |
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- **Base Model**: RetinaNet for object detection |
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- **Backbone**: SpineNet-49 for feature extraction |
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- **Input Resolution**: 640×640×3 |
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- **Output**: Bounding boxes with class predictions and confidence scores |
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### Detection Classes |
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1. `face` - Rubik's cube face |
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2. `red_tile` - Red color tile |
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3. `white_tile` - White color tile |
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4. `blue_tile` - Blue color tile |
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5. `orange_tile` - Orange color tile |
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6. `green_tile` - Green color tile |
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7. `yellow_tile` - Yellow color tile |
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## 📊 Performance Metrics |
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| Metric | Target | Status | |
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|--------|--------|--------| |
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| mAP@0.5 | >0.85 | In Training | |
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| Inference Speed | <100ms | Optimized | |
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| Accuracy | >90% | Evaluating | |
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## 🛠️ Local Development |
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```bash |
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# Clone the repository |
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git clone https://huggingface.co/spaces/itsyuimorii/rubiks-cube-recognition |
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cd rubiks-cube-recognition |
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# Install dependencies |
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pip install -r requirements.txt |
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# Run the application |
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python app.py |
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``` |
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## 📁 Project Structure |
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``` |
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rubiks-cube-recognition/ |
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├── app.py # Main Gradio application |
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├── requirements.txt # Python dependencies |
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├── README.md # This file |
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├── configs/ # Model configurations |
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│ └── retinanet_config.py |
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├── src/ # Source code |
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│ ├── data/ # Data processing utilities |
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│ └── model/ # Model training and inference |
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└── images/ # Training and test datasets |
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├── train/ # Training images and annotations |
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├── test/ # Test images and annotations |
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└── valid/ # Validation images and annotations |
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``` |
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## 🎮 Demo Status |
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⚠️ **Note**: This is a demo version. The complete trained model is currently being developed. The interface will show a preview of the detection capabilities. |
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## 📝 Dataset Information |
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- **Format**: COCO annotation format |
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- **Image Size**: 640×640 pixels |
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- **Training Images**: 50+ annotated cube images |
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- **Classes**: 7 object classes (face + 6 colors) |
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- **Annotation Tool**: LabelMe |
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## 🔧 Training Pipeline |
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```python |
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# Training command |
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python src/model/trainer.py --config configs/retinanet_config.py |
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# Inference command |
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python src/model/visualize.py --image path/to/cube_image.jpg |
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``` |
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## 🤝 Contributing |
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Contributions are welcome! Areas for improvement: |
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- Additional training data |
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- Model optimization |
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- UI/UX enhancements |
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- Performance improvements |
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## 📄 License |
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This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details. |
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## 🙏 Acknowledgements |
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- **TensorFlow Model Garden** - RetinaNet implementation |
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- **SpineNet** - Backbone architecture |
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- **Gradio** - Web interface framework |
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- **Hugging Face** - Model hosting and deployment |
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## 📧 Contact |
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- **GitHub**: [@itsyuimorii](https://github.com/itsyuimorii) |
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- **Hugging Face**: [@itsyuimorii](https://huggingface.co/itsyuimorii) |
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## 🔗 References |
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- [RetinaNet Paper](https://arxiv.org/abs/1708.02002) |
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- [SpineNet Architecture](https://arxiv.org/abs/1912.05027) |
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- [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) |
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- [LabelMe Annotation Tool](https://github.com/wkentaro/labelme) |
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--- |
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*🎲 Ready to solve your Rubik's cube detection challenges!* |
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