  We describe the DeepMind Kinetics human action video dataset. The dataset
contains 400 human action classes, with at least 400 video clips for each
action. Each clip lasts around 10s and is taken from a different YouTube video.
The actions are human focussed and cover a broad range of classes including
human-object interactions such as playing instruments, as well as human-human
interactions such as shaking hands. We describe the statistics of the dataset,
how it was collected, and give some baseline performance figures for neural
network architectures trained and tested for human action classification on
this dataset. We also carry out a preliminary analysis of whether imbalance in
the dataset leads to bias in the classifiers.
