Ball-action-spotting-app

This project focuses on the development and implementation of classification system for ball events in a football match, also called ball action spotting. It utilizes the SoccerNet dataset and draws inspiration from previously proposed solutions.

Soccer Ball Actions Detected

12 classes of soccer ball actions:

Action	Action	Action
Pass	Header	Ball Player Block
Drive	High Pass	Player Successful Tackle
Out	Cross	Free Kick
Throw In	Shot	Goal

Structure of the project

ball-action-spotting-app/
│
├── notebooks/                 #Jupyter notebooks for model training and experimentation
│
├── streamlit_app/             # Contains the Streamlit app code
│   ├── app.py                 # Main app script
│   ├── model.py               # Network definition 
│   ├── video_prediction.py    # Video processing and prediction logic
│   ├── train_model.h          # Model weights
│   └── requirements.txt       # Python dependencies
│
└── presentation/              # Presentation of the project

How to Run the App

1. Clone the Repository:

   git clone https://github.com/yourusername/ball-action-spotting-app.git

2. Navigate to the Application Directory:

  cd ball-action-spotting-app/streamlit_app

4. Install Required Dependencies:

   pip install -r requirements.txt

5. Run the Streamlit Application:

   streamlit run app.py

How to use the streamlit app