  This paper presents the 1st place solution for the Google Universal Images
Embedding Competition on Kaggle. The highlighted part of our solution is based
on 1) A novel way to conduct training and fine-tuning; 2) The idea of a better
ensemble in the pool of models that make embedding; 3) The potential trade-off
between fine-tuning on high-resolution and overlapping patches; 4) The
potential factors to work for the dynamic margin. Our solution reaches 0.728 in
the private leader board, which achieve 1st place in Google Universal Images
Embedding Competition.
