论文标题
Google Landmark检索2020的第一名解决方案
1st Place Solution to Google Landmark Retrieval 2020
论文作者
论文摘要
本文介绍了Google Landmark检索2020在Kaggle上的第一名解决方案。该解决方案基于公制学习，以对众多具有里程碑意义的类别进行分类，并将转移学习与两个火车数据集使用，对更大的图像进行微调，调整清洁剂样品的减肥体重，并进行Esemble，以进一步增强模型的性能。最后，它在私人排行榜上为0.38677 MAP@100得分。
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.
