1st 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 techniques for a specific competition.
The paper tackled the Google Landmark Retrieval 2020 Competition by developing a solution based on metric learning with transfer learning, fine-tuning, loss adjustments, and ensemble methods, achieving a score of 0.38677 mAP@100 on the private leaderboard.
This paper presents the 1st place solution to the Google Landmark Retrieval 2020 Competition on Kaggle. The solution is based on metric learning to classify numerous landmark classes, and uses transfer learning with two train datasets, fine-tuning on bigger images, adjusting loss weight for cleaner samples, and esemble to enhance the model's performance further. Finally, it scored 0.38677 mAP@100 on the private leaderboard.