Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research

Wednesday, September 28, 2016

Posted by Sudheendra Vijayanarasimhan and Paul Natsev, Software Engineers

Many recent breakthroughs in machine learning and machine perception have come from the availability of large labeled datasets, such as ImageNet, which has millions of images labeled with thousands of classes. Their availability has significantly accelerated research in image understanding, for example on detecting and classifying objects in static images.

Video analysis provides even more information for detecting and recognizing objects, and understanding human actions and interactions with the world. Improving video understanding can lead to better video search and discovery, similarly to how image understanding helped re-imagine the photos experience. However, one of the key bottlenecks for further advancements in this area has been the lack of real-world video datasets with the same scale and diversity as image datasets.

Today, we are excited to announce the release of YouTube-8M, a dataset of 8 million YouTube video URLs (representing over 500,000 hours of video), along with video-level labels from a diverse set of 4800 Knowledge Graph entities. This represents a significant increase in scale and diversity compared to existing video datasets. For example, Sports-1M, the largest existing labeled video dataset we are aware of, has around 1 million YouTube videos and 500 sports-specific classes--YouTube-8M represents nearly an order of magnitude increase in both number of videos and classes.

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Continua qui: https://research.googleblog.com/2016/09/announcing-youtube-8m-large-and-diverse.html