OSAI introduces OpenTTGames - an open dataset aimed at evaluation of different computer vision tasks in Table Tennis: ball detection, semantic segmentation of humans, table and scoreboard and fast in-game events spotting.

It includes full-HD videos of table tennis games recorded at 120 fps with an industrial camera. Every video is equipped with an annotation containing the frame numbers and corresponding targets for this particular frame: manually labeled in-game events (ball bounces, net hits, or empty event targets) and/or ball coordinates and segmentation masks, which were labeled with deep learning-aided annotation models.