football-anotation Object Detection Dataset and Pre-Trained Model by kmitl

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Explore Dataset

Here are a few use cases for this project:

Sports Analytics: Use the "football-annotation" model to track player and ball movements during football matches, enabling coaches to analyze team performance, strategize formations, and improve player skills based on real-time data.
Automatic Game Highlight Compilation: Utilize the model to identify significant moments in a football match, such as goals, saves, tackles, or passes, and automatically create a highlights package with time-stamped events for easy review and sharing.
Injury Detection and Prevention: Implement the model to monitor player movements and collisions, helping to identify potential injuries or risky situations during matches, and allowing for swift intervention or post-game analysis for improved safety measures.
Audience Engagement: Enhance live football match broadcasts by using the "football-annotation" model to provide real-time player and ball tracking data, enabling commentators to offer insightful observations and creating interactive visualizations for viewers.
Training and Skill Development: Apply the model to assist coaches and trainers in tracking player performance during practice sessions, offering targeted feedback on areas for improvement, such as ball control, positioning, or teamwork within the context of specific drills or exercises.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            football-anotation_dataset,
                            title = { football-anotation Dataset },
                            type = { Open Source Dataset },
                            author = { kmitl },
                            howpublished = { \url{ https://universe.roboflow.com/kmitl-4lqaa/football-anotation } },
                            url = { https://universe.roboflow.com/kmitl-4lqaa/football-anotation },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-05 },
                            }