Tennis Instance Segmentation Dataset and Pre-Trained Model by Deep

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Here are a few use cases for this project:

Sports Analytics: This "Tennis" model can be instrumental for sports analysts to break down the gameplay of tennis players, track ball possession, speed, trajectory, and player's positions, enhancing their ability to interpret game strategies and performance.
Event Organizing: Event organizers for tennis tournaments can use this model to monitor crowd size, identify peak engagement moments, or ensure player safety by accurately spotting tennis players and tracking the exact location of the ball.
Training and Coaching: Tennis coaches can leverage this model to analyze player's techniques, ball movements, strike positions, and footwork from recorded training sessions. This can aid in providing personalized feedback and training programs.
Broadcast Enhancement: Media companies can make use of the "Tennis" model in their broadcasts to provide real-time analytics, player tracking, ball speed, and trajectory to viewers, enriching the viewing experience.
Player Performance Simulation: Game developers could use this model for creating more realistic tennis computer games or virtual reality experiences, where the movements of professional players and ball dynamics are translated into the game algorithm.

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.

Cite this Project

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

@misc{ tennis-yfcgx_dataset,
    title = { Tennis Dataset },
    type = { Open Source Dataset },
    author = { Deep },
    howpublished = { \url{ https://universe.roboflow.com/deep-hbapi/tennis-yfcgx } },
    url = { https://universe.roboflow.com/deep-hbapi/tennis-yfcgx },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jan },
    note = { visited on 2023-12-07 },
}