论文标题
Google Landmark检索2020的第二名解决方案
2nd Place Solution to Google Landmark Retrieval 2020
论文作者
论文摘要
本文介绍了2020年Google Landmark检索竞赛的第二名解决方案。我们提出了一种无需后处理的地标取回的全球功能模型的培训方法，例如本地功能和空间验证。在这场比赛中，我们的检索方法中有两个部分。该培训方案主要包括通过增加Arcmargin损失的保证金值和逐步增加图像分辨率来培训。型号由PaddlePaddle框架和Pytorch框架训练，然后转换为Tensorflow 2.2。使用这种方法，我们的公共得分为0.40176，私人得分为0.36278，并在2020年的Google Landmark检索竞赛中获得了第二名。
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.
