{"url":"/dataset/im2gps","name":"Im2GPS","full_name":null,"description_markdown":"Dataset of over 6 million GPS-tagged images from Flickr. Training dataset is private. Test dataset is composed by 237 images.","description_withheld":null,"homepage":"http://graphics.cs.cmu.edu/projects/im2gps/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Photo geolocation estimation","url":"/task/photo-geolocation-estimation","datasets_with_task":"/datasets/task/photo-geolocation-estimation"}],"languages":[],"variants":["Im2GPS"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/photo-geolocation-estimation-on-im2gps","task":"Photo geolocation estimation","dataset_variant":"Im2GPS","rows":11,"metrics":["Median Error (km)","Street level (1 km)","City level (25 km)","Region level (200 km)","Country level (750 km)","Continent level (2500 km)","Training images","Reference images"],"first_row_in_archive_order":{"model":"PIGEOTTO","paper":"/paper/pigeon-predicting-image-geolocations","metrics":{"City level (25 km)":"40.9","Continent level (2500 km)":"91.1","Country level (750 km)":"82.3","Median Error (km)":"70.5","Reference images":"4.5M","Region level (200 km)":"63.3","Street level (1 km)":"14.8","Training images":"4.5M"},"code_links":[{"title":"LukasHaas/PIGEON","url":"https://github.com/LukasHaas/PIGEON"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/pigeon-predicting-image-geolocations","title":"PIGEON: Predicting Image Geolocations","date":"2023-07-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-generalized-zero-shot-learners-for","title":"Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization","date":"2023-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/geolocation-estimation-of-photos-using-a","title":"Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification","date":"2018-09-01","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/cplanet-enhancing-image-geolocalization-by","title":"CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps","date":"2018-08-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/revisiting-im2gps-in-the-deep-learning-era","title":"Revisiting IM2GPS in the Deep Learning Era","date":"2017-05-13","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/planet-photo-geolocation-with-convolutional","title":"PlaNet - Photo Geolocation with Convolutional Neural Networks","date":"2016-02-17","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}