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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
AI-powered padel analytics
| Date | Stars |
|---|---|
| 2026-07-24 | 261 |
| 2026-07-25 | 261 |
| 2026-07-28 | 261 |
| 2026-07-30 | 262 |
| 2026-08-06 | 262 |
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growth rate 0.00%/day
# Padel Analytics  This repository applies computer vision techniques to extract valuable insights from a padel game recording like: - Position and velocity of each player; - Position and velocity of the ball; - 2D game projection; - Heatmaps; - Ball velocity associated with distinct strokes; - Player error rate. To do so, several computer vision models where trained in order to: 1. Track the position of each individual players; 2. Players pose estimation with 13 degrees of freedom; 3. Players pose classification (e.g. backhand/forehand volley, bandeja, topspin smash, etc); 4. Predict ball hits. The goal of this project is to provide precise and robust analytics using only a padel game recording. This implementation can be used to: 1. Upgrade live broadcasts providing interesting data to be shared with the audience or to be stored in a database for future analysis; 2. Generate precious insights to be used by padel coachs or players to enhance their path of continuous improvement. # Setup #### 1. Clone this repository. #### 2. Setup virtual environment. ``` conda create -n python=3.12 padel_analytics pip conda activate padel_analytics pip install -r requirements.txt ``` #### 3. Install pytorch <https://pytorch.org/get-started/locally/>. #### 4. Download weights. The current model weights used are available here https://drive.google.com/drive/folders/1joO7w1Am7B418SIqGBq90YipQl81FMzh?usp=drive_link. Configure the config.py file with your own model checkpoints paths. # Inference At the root of this repo, edit the file config.py accordingly and run: ```` python main.py ```` #### VRAM requirements Using the default batch sizes one will need to have at least 8GB of VRAM. Reduce batch sizes editing the config.py file according to your needs. #### Implementation details Currently this implementation assumes a fixed camera setup. As a result, a UI for selecting court keypoints will pop up asking you to select 12 unique court keypoints that are further used for homographic computations. A video describing the keypoints selection is available at `./examples/videos/select_keypoints.mp4`. Please refer to main.py lines 24-38 where a diagram showcasing keypoints numeration is drawn. #### Keypoints selection  #### Inference results  # Collaborations I am currently looking for collaborations to uplift this project to new heights. If you are interested feel free to e-mail me at [email protected].
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4f97d6d2f5a10d54, topic:computer-vision, topic:object-detection, topic:yolo