  While image retrieval and instance recognition techniques are progressing
rapidly, there is a need for challenging datasets to accurately measure their
performance -- while posing novel challenges that are relevant for practical
applications. We introduce the Google Landmarks Dataset v2 (GLDv2), a new
benchmark for large-scale, fine-grained instance recognition and image
retrieval in the domain of human-made and natural landmarks. GLDv2 is the
largest such dataset to date by a large margin, including over 5M images and
200k distinct instance labels. Its test set consists of 118k images with ground
truth annotations for both the retrieval and recognition tasks. The ground
truth construction involved over 800 hours of human annotator work. Our new
dataset has several challenging properties inspired by real world applications
that previous datasets did not consider: An extremely long-tailed class
distribution, a large fraction of out-of-domain test photos and large
intra-class variability. The dataset is sourced from Wikimedia Commons, the
world's largest crowdsourced collection of landmark photos. We provide baseline
results for both recognition and retrieval tasks based on state-of-the-art
methods as well as competitive results from a public challenge. We further
demonstrate the suitability of the dataset for transfer learning by showing
that image embeddings trained on it achieve competitive retrieval performance
on independent datasets. The dataset images, ground-truth and metric scoring
code are available at https://github.com/cvdfoundation/google-landmark.
