YouTube-8M: A Large-Scale Video Classification Benchmark

TL;DR

YouTube-8M is a large-scale, multi-label video classification dataset with 8 million videos and 4800 labels, enabling rapid training and benchmarking of video understanding models.

Contribution

The paper introduces YouTube-8M, the largest multi-label video dataset, with high-quality labels and a scalable framework for training and evaluating video classification models.

Findings

Models trained on YouTube-8M achieve competitive performance.

Training models on the dataset can be done in less than a day on a single machine.

The dataset facilitates rapid development and benchmarking of video classification algorithms.

Abstract

Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of ~8 million videos (500K hours of video), annotated with a vocabulary of 4800 visual entities. To get the videos and their labels, we used a YouTube video annotation system, which labels videos with their main topics. While the labels are machine-generated, they have high-precision and are derived from a variety of human-based signals including metadata and query click signals. We filtered the video labels (Knowledge Graph entities) using both automated and manual curation strategies, including asking human raters if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pre-trained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow. We plan to release code for training a TensorFlow model and for computing metrics.