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

1st Place Solution in Google Universal Images Embedding