racket
Datasets
All datasets matching “racket”RacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation… See the full description on the dataset page: https://huggingface.co/datasets/linfeng302/RacketVision.RacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose… See the full description on the dataset page: https://huggingface.co/datasets/yanbing2/RacketVision.Airbot_MMK2_move_tennis_racket_ball
Airbot_MMK2_move_tennis_racket_ball
Dataset Description
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
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Overview
Total Episodes: 48
Total Frames: 10833
FPS: 30
Dataset Size: 455.12 MB
Robot Name: Airbot_MMK2
End-Effector Type: five_finger_gripper
Teleoperation Type: Due to some reasons, this dataset temporarily cannot provide the teleoperation type information.… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/Airbot_MMK2_move_tennis_racket_ball.marin-starcoderdata_racketracketdbRacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose… See the full description on the dataset page: https://huggingface.co/datasets/Corgiluu/RacketVision.