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Football AI ## Overview The ultimate goal of this project is to develop a program that acts as an offensive coordinator for a football team. This system will suggest plays based on various factors, including: - Defensive formations - Players on field - Weather conditions - Player statistics - Time of game - Recent plays and results - Recent wins and losses - Historical performance against specific teams This project utilizes the YOLOv8 object detection model to identify football players in video footage. The dataset is sourced and annotated using [Roboflow](https://roboflow.com), and the model is trained to accurately detect players in various conditions and environments. ## Table of Contents - [Prerequisites](#prerequisites) - [Installation](#installation) - [Dataset](#dataset) - [Training the Model](#training-the-model) - [Usage](#usage) - [Contributing](#contributing) - [License](#license) ## Prerequisites Before you begin, ensure you have the following installed: - Python 3.7 or higher - pip install the required libraries - Download pre-trained yolo model https://drive.google.com/file/d/1VPc3qaO87EuUwYh3oOUc5QkAdX6ch5Ez/view?usp=drive_link -Use own All22 video Required Libraries • Roboflow • Ultralytics • OpenCV Roboflow Account: • Create a Roboflow account to access and manage your datasets. Data Preparation: • Have a dataset prepared that includes relevant images or videos, along with annotations for training the model. This dataset should ideally capture various game scenarios, defensive formations, and player actions. Development Environment: • A code editor or IDE (such as PyCharm, Visual Studio Code, or Jupyter Notebook) for writing and executing your Python code. • Basic Knowledge: • Familiarity with Python programming, machine learning concepts, and how to work with APIs. • Additional Data Sources (Optional): • If you plan to incorporate player stats, weather conditions, and historical performance, you may need access to additional data sources or APIs that provide this information.
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Use machine learning to identify players, refs and football field markings.
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