Download model: https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-0012.params https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-symbol.json

Geolocation model inspired by ideas presented in: PlaNet - Photo Geolocation with Convolutional Neural Networks (ECCV 2016), Tobias Weyand, Ilya Kostrikov, James Philbin https://research.google.com/pubs/pub45488.html

Our data come from the geotagged images in the YFCC100M Multimedia Commons dataset. Training, validation, and test images are split so that images uploaded by the same person do not appear in multiple sets. Classes are created with the training data using Google's S2 Geometry Library as described in the PlaNet paper above. The classes are defined in grids.txt where the i-th line is the i-th class and the columns are: S2 Cell Token, Latitude, Longitude.

The values indicate the percentages of images within test set that were correctly localized within the given distance.

Note that these result in the table are not directly comparable as the test set images used in PlaNet is not publicly released. The values indicate the percentages of images within test set that were correctly localized within the given distance.