This project utilizes TensorFlow.js and the RetinaNet-SpineNet-49 model (retinanet_spinenet_mobile_coco) to train an AI system that recognizes and interprets Rubik's Cube patterns via a camera. By integrating computer vision 🖼️, COCO annotations 🗂️, and state-of-the-art object detection models 🚀, the system detects and identifies each face and tile color of a Rubik's Cube, paving the way for automated solving.

The project is structured into three core components:

1. Data Preparation 🖌️:

   • Collected Rubik's Cube images annotated using LabelMe.
   • Converted annotations from LabelMe JSON format to COCO JSON format using a custom Python script.
   • Defined detection categories, including color tiles (red_tile, white_tile, blue_tile, etc.).

2. Model Training 🏋️‍♀️:

   • This project uses the RetinaNet-SpineNet-49 architecture, specifically the retinanet_spinenet_mobile_coco model provided by TensorFlow as the base model.
   • Fine-tuned the model for real-time classification and localization of Rubik's Cube tiles.

3. Visualization and Evaluation 👀:

   • Developed a visualization script to overlay predictions on test images with bounding boxes and labels.
   • Exported results to PNG format for validation and analysis.

• Advanced Object Detection 🎯: High-precision Rubik's Cube tile detection using the RetinaNet-SpineNet-49 model.
• Dynamic Tile Recognition 🕹️: Real-time identification of cube tiles and colors via camera input.
• Streamlined Annotation Workflow 🔄: Seamless conversion of LabelMe JSON annotations into the COCO JSON format.
• Custom Visualization Tools 🖼️: Debugging and enhancing predictions through overlayed visual outputs.

1. Data Collection: Annotate Rubik's Cube images using LabelMe. ✍️
2. COCO Conversion: Convert annotations with the labelme_to_coco.py script. 🔄
3. Model Training:
   • Configure the retinanet_spinenet_mobile_coco model using TensorFlow Model Garden.
   • Train the model on a custom Rubik's Cube dataset. 🧠
4. Deployment: Use TensorFlow.js to deploy the trained model for real-time detection. 🌐

• Python 3.8+
• TensorFlow
• TensorFlow.js
• TensorFlow Model Garden
• LabelMe for annotation

1. Clone the repository:

   git clone [repository_link]
   cd rubiks-cube-detection

2. Install dependencies:

   pip install -r requirements.txt

3. Annotate your Rubik's Cube images using LabelMe and place them in the images/ folder.

1. Convert Annotations:

   python labelme_to_coco.py

2. Train the Model:

   • Modify the configuration for retinanet_spinenet_mobile_coco.
   • Train the model:

     python train.py --model=retinanet_spinenet_mobile_coco --config=configs/retinanet_spinenet_mobile_coco.config --data_dir=path_to_coco_data

3. Run Detection:

   • Convert the trained model to TensorFlow.js format.
   • Deploy the model for real-time detection.

Sample visualizations with bounding boxes and labels are saved in the outputs/ folder. 🖼️

• 🤖 Integrate a Rubik's Cube-solving algorithm.
• 📈 Expand the dataset to improve detection accuracy.
• 🌐 Deploy the system as a web-based application.

Contributions are welcome! Feel free to submit issues or pull requests for improvements. 💡