2nd Place Solution to Google Landmark Retrieval 2020
This work addresses landmark retrieval for computer vision applications, but it is incremental as it builds on existing methods with specific optimizations.
The paper tackled the problem of landmark retrieval by proposing a training method for global feature models without post-processing, achieving a public score of 0.40176 and a private score of 0.36278 to secure 2nd place in the Google Landmark Retrieval Competition 2020.
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.