  We present an efficient end-to-end pipeline for largescale landmark
recognition and retrieval. We show how to combine and enhance concepts from
recent research in image retrieval and introduce two architectures especially
suited for large-scale landmark identification. A model with deep orthogonal
fusion of local and global features (DOLG) using an EfficientNet backbone as
well as a novel Hybrid-Swin-Transformer is discussed and details how to train
both architectures efficiently using a step-wise approach and a sub-center
arcface loss with dynamic margins are provided. Furthermore, we elaborate a
novel discriminative re-ranking methodology for image retrieval. The
superiority of our approach was demonstrated by winning the recognition and
retrieval track of the Google Landmark Competition 2021.
