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Use machine learning to identify players, refs and football field markings.

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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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