YouTube-8M Dataset

Creators:
Abu-El-Haija, Sami; Kothari, Nisarg; Lee, Joonseok; Natsev, Paul; Toderici, George; Varadarajan, Balakrishnan; Vijayanarasimhan, Sudheendra
Publication Date:
2016
Data Category:
Dataset Description:
YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs and with high-quality machine-generated & partially human-verified annotations from a diverse vocabulary of 3,800+ visual entities. It comprises two subsets: 8M Segments Dataset: 230K human-verified segment labels, 1000 classes, 5 segments/video 8M Dataset: May 2018 version (current): 6.1M videos, 3862 classes, 3.0 labels/video, 2.6B audio-visual features Thus, it comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. This makes it possible to train a strong baseline model on this dataset in less than a day on a single GPU! At the same time, the dataset's scale and diversity can enable deep exploration of complex audio-visual models that can take weeks to train even in a distributed fashion. YouTube offers the YouTube8M dataset for download as TensorFlow Record files on their website. Starter code for the dataset can be found on their GitHubpage.
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