TableTennisTutor is an innovative software powered by machine learning algorithms. It takes as input a video, analyzes the table tennis shots being played, and classifies them. Following the classification, it pulls up a repository of tutorials and demonstrates how to correctly execute the identified shots. A user-friendly GUI makes the interaction with the software intuitive and straightforward.
- Utilizes machine learning algorithms for video analysis and shot classification.
- Provides practical tutorials on how to correctly execute identified shots.
- Interactive and user-friendly GUI.
- Follows best practices in data cleaning and preprocessing for model training.
To get started with TableTennisTutor, follow the steps below:
- Clone this repository to your local machine using
https://github.com/zaidharis2801/TableTennisTutor.git. - Navigate to the project directory. For example,
cd TableTennisTutor. - Run the application (Specify how to do this based on your project's setup).
TableTennisTutor is powered by machine learning algorithms trained on a diverse dataset of table tennis shots. The software takes as input a user-provided video and processes it, identifying and classifying the shots being played.
Once a shot has been identified, the software consults a comprehensive repository of table tennis tutorials and provides the user with a demonstration on how to execute the shot correctly. This process allows for real-time feedback and learning, assisting users in improving their table tennis skills.
In creating TableTennisTutor, we implemented best practices for data cleaning and preprocessing, ensuring high-quality training data for our machine learning model. This rigorous process aids in enhancing the model's performance, accuracy, and generalization capabilities.
Contributions, bug reports, and improvements are very welcome! Feel free to open an issue or submit a pull request.
This project is licensed under Apache Liscence.
For questions, feel free to contact me at Szbharis@gmail.com.