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AI Tennis Match Analyzer leveraging PyTorch and fine-tuned YOLO models for game insights and player performance metrics

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NoahBakayou/AI-Tennis-Analyzer

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AI Tennis Analyzer

Welcome to my AI Tennis Analyzer, an advanced machine learning and deep learning project designed to revolutionize how we understand and analyze tennis. Leveraging state-of-the-art technologies, this project offers comprehensive insights into tennis matches by detecting objects in images and videos, tracking objects across frames, analyzing detection data, and employing data-driven methodologies for enhanced feature development.

Features

  • Object Detection with YOLOv8: Utilized the cutting-edge Ultralytics YOLOv8 model for high-precision object detection within tennis matches, enabling the identification and classification of key elements in real-time.

  • Custom Model Training on A100 GPUs: Fine-tuned YOLOv5 on a custom dataset to improve tennis ball tracking accuracy. Training was performed on NVIDIA A100 GPUs through Google Colab and Jupyter Notebooks, ensuring rapid processing and superior model performance.

  • Keypoint Extraction with CNNs: Employed Convolutional Neural Networks (CNNs) within PyTorch to accurately extract keypoints from the tennis court, facilitating detailed motion analysis and player positioning strategies.

  • Advanced Object Tracking: Integrated sophisticated object tracking algorithms to maintain continuity across frames, providing seamless analysis of player movements and ball trajectories throughout rallies.

  • Video Processing with OpenCV: Leveraged OpenCV (cv2) for comprehensive video processing capabilities, including reading, manipulating, and saving video data for further analysis.

  • Data Analysis with Pandas: Utilized the Pandas library for efficient data manipulation and analysis, enabling sophisticated handling of datasets to derive meaningful insights and inform feature development.

  • Data-Driven Feature Development: Analyzed detection data to iteratively develop and refine project features, adopting a data-driven approach to enhance the accuracy and utility of the analytics provided.

  • Comprehensive Machine Learning Pipeline: Combined multiple ML/DL models into a unified project with tangible outputs, showcasing the potential of AI in transforming tennis analysis.

Technologies Used

  • YOLOv8 & Ultralytics: For cutting-edge object detection and model fine-tuning.
  • PyTorch: The backbone for CNN model development and training.
  • Google Colab & Jupyter Notebooks: Flexible and powerful platforms for developing and executing deep learning models.
  • OpenCV (cv2): For all video reading, processing, and saving needs.
  • Pandas: For data analysis and manipulation, enabling effective feature development and insights extraction.
  • Interpolation Object Tracking Algorithms: To track the movement of players and the ball across video frames.

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AI Tennis Match Analyzer leveraging PyTorch and fine-tuned YOLO models for game insights and player performance metrics

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