🏸 High-Speed Badminton Match AI Analyser (ML Research & Development)

This repository serves as the research lab and archive for the High-Speed Badminton Match AI Analyser. It contains the raw notebooks used for data preparation, model experimentation, and training.

⚠️ Notice

Please note: The notebooks in this repository are raw research artifacts.

• Code State: Many notebooks are messy and may contain non-working code, debug blocks, or hardcoded paths specific to the original environment, which include Google Colab, Google Drive, and H100 servers (via RunAI).
• Version Uncertainty: As this project evolved rapidly, it is not able to determine which specific notebook version produced the final production weights.
• Purpose: These files are provided for transparency and reference regarding the development methodology, rather than as a clean, execute-from-top-to-bottom tutorial.

For the clean, production-ready inference pipeline, please refer to the Backend API repository linked below.

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🔗 Project Structure & Repositories

This project is divided into three main repositories to handle the frontend, backend logic, and model development separately:

• Frontend: https://github.com/bryanlje/mds06-frontend
• Backend API: https://github.com/bryanlje/mds06-backend
• Model Training & Data Preparation (This Repo): https://github.com/bryanlje/mds06-ml

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📂 Repository Content

1. Final Pipeline (00_final_pipeline/)

• FINAL_FULL_PIPELINE.ipynb: The most complete integration notebook used to prototype the end-to-end logic (Detection → Tracking → Shuttle → Action) before it was refactored into the production backend.

2. Data Preparation (01_data_preparation/)

Scripts used to transform raw annotations into training datasets.

• cvat_to_csv_bryan.ipynb: Converting raw CVAT export JSONs into standardised CSV formats.
• generate_clips_from_csv_bryan.ipynb: Slicing full match videos into action-specific clips.
• new_dataset_format.ipynb: Formatting data for PyTorchVideo loaders.

3. Model Training & Experiments (02_training_experiments/)

A. Player Detection (YOLO)

Notebooks for training YOLOv8/11 to detect players.

• yolo_bryan training my_yolo11s_finals.ipynb (Final training run).

B. Player Tracking (StrongSORT + ReID)

Experiments with StrongSORT and OSNet for player Re-Identification.

C. Action Recognition (SlowFast)

Extensive experimentation with SlowFast (ResNet-101) for classifying badminton shots.

• Note on Training Process: The training for the final SlowFast model was highly iterative and is not fully captured by a single notebook. It involved:
  • Many stop-start cycles.
  • Manually adjusting learning rates and epochs based on interim results.
  • Swapping datasets (high and standard frame rate clips) mid-training to refine performance.
  • Therefore, the notebooks here represent snapshots of this process, with NO exact uninterrupted training run.

4. Miscellaneous Research (03_misc_research/)

Exploratory work including DINOv3 foundation models and pose estimation tests.

❌ Missing Components

Please be aware that the following notebooks are not included in this archive:

1. 1D CNN Training: The training script for the contact detection model (Shuttlecock trajectory analysis).
2. Visualisation: The standalone notebook used for generating charts to showcase our results (bar charts, heatmaps, etc).

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👥 Team

This project was developed by Team MDS06:

• Bryan Leong Jing Ern
• Phua Yee Yen
• Ting Shu Hui
• Lee Jian Jun Thomas

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📧 Contact

Email: 2025mds06@gmail.com