  This paper presents our 7th place solution to the second YouTube-8M video
understanding competition which challenges participates to build a
constrained-size model to classify millions of YouTube videos into thousands of
classes. Our final model consists of four single models aggregated into one
tensorflow graph. For each single model, we use the same network architecture
as in the winning solution of the first YouTube-8M video understanding
competition, namely Gated NetVLAD. We train the single models separately in
tensorflow's default float32 precision, then replace weights with float16
precision and ensemble them in the evaluation and inference stages., achieving
48.5% compression rate without loss of precision. Our best model achieved
88.324% GAP on private leaderboard. The code is publicly available at
https://github.com/boliu61/youtube-8m
