TAPOS is a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. A sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition.

TAPOS contains 16,294 valid instances in total, across 21 action classes. These instances have a duration of 9.4 seconds on average. The number of instances within each class is different, where the largest class high jump has over 1,600 instances, and the smallest class beam has 200 instances. The average number of sub-actions also varies from class to class, where parallel bars has 9 sub-actions on average, and long jump has 3 sub-actions on average. All instances are split into train, validation and test sets, of sizes 13094, 1790, and 1763, respectively.