Omkar2118/datasets huggingface dataset:
SoccerNet-GSR
Full name: SoccerNet Game State Reconstruction

The SoccerNet Game State Reconstruction task is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field based on a raw input videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number.

Introducing paper: SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
WavCaps

A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research.

Introducing paper: WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research