  In this paper, we describe our solution to the Google Landmark Recognition
2019 Challenge held on Kaggle. Due to the large number of classes, noisy data,
imbalanced class sizes, and the presence of a significant amount of distractors
in the test set, our method is based mainly on retrieval techniques with both
global and local CNN approaches. Our full pipeline, after ensembling the models
and applying several steps of re-ranking strategies, scores 0.37606 GAP on the
private leaderboard which won the 1st place in the competition.
