Google releases AI training data set with 5 million images and 200,000 landmarks

Designing AI systems capable of accurate instance-level landmark recognition (i.e., distinguishing Niagara Falls from just any waterfall) and retrieving images (matching objects in an image to other instances of that object in a catalog) is a longstanding pursuit of Google's AI research division. Last year, it released Google-Landmarks, a landmarks data set it claimed at the time was the world's largest, and hosted two competitions (Landmark Recognition 2018 and Landmark Retrieval 2018) in which more than 500 machine learning researchers participated. Additionally, it's launched two new challenges (Landmark Recognition 2019 and Landmark Retrieval 2019) on Kaggle, its machine learning community, and released the source code and model for Detect-to-Retrieve, a framework for regional image retrieval. "Both instance recognition and image retrieval methods require ever-larger datasets in both the number of images and the variety of landmarks in order to train better and more robust systems," wrote Google AI software engineers Bingyi Cao and Tobias Weyand. "We hope that this dataset will help advance the state-of-the-art in instance recognition and image retrieval."