  This paper presents the 2nd place solution to the Google Landmark Retrieval
Competition 2020. We propose a training method of global feature model for
landmark retrieval without post-processing, such as local feature and spatial
verification. There are two parts in our retrieval method in this competition.
This training scheme mainly includes training by increasing margin value of
arcmargin loss and increasing image resolution step by step. Models are trained
by PaddlePaddle framework and Pytorch framework, and then converted to
tensorflow 2.2. Using this method, we got a public score of 0.40176 and a
private score of 0.36278 and achieved 2nd place in the Google Landmark
Retrieval Competition 2020.
