Skip to content

Latest commit

 

History

History
30 lines (22 loc) · 1.47 KB

Risk.md

File metadata and controls

30 lines (22 loc) · 1.47 KB
Raw

Smile Classification Project

Overview

This project will classify smiles and no-smiles using a custom dataset and the YOLOv8 model. The trained model is then used in real-time to capture selfies automatically based on user-selected gestures.

Project Structure

  • dataset_creation/: Contains scripts and resources for creating the custom dataset.
  • model_training/: Includes code and files related to training the YOLOv8 model.
  • real_time_implementation/: Implements the real-time smile classification using the trained model.
  • app_development/: Resources for developing the mobile app for gesture-based selfie capture.

Potential Risks

  • Data Quality: Ensure the dataset captures diverse smile and no-smile gestures.
  • Model Performance: Evaluate and optimize the YOLOv8 model for accurate real-time classification.
  • Hardware Compatibility: Test the real-time implementation on different hardware setups.
  • User Experience: Design the app interface to be intuitive and user-friendly.
  • Ethical Considerations: Address biases and obtain user consent for image processing.

Future Work

  • Implement additional features such as emotion recognition.
  • Enhance model performance through continual training and refinement.

License

This project is licensed under MIT License.

Acknowledgments

  • YOLO: Inspiration for model training.
  • OpenCV: Used for real-time image processing.