HyperAI超神経
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HyperAI超神経

Photo Geolocation Estimation On Im2Gps

評価指標

City level (25 km)
Continent level (2500 km)
Country level (750 km)
Median Error (km)
Reference images
Region level (200 km)
Street level (1 km)
Training images

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
City level (25 km)
Continent level (2500 km)
Country level (750 km)
Median Error (km)
Reference images
Region level (200 km)
Street level (1 km)
Training images
Paper TitleRepository
PIGEOTTO40.991.182.370.54.5M63.314.84.5MPIGEON: Predicting Image Geolocations
base (L, m)35.079.764.1-049.813.54.7MGeolocation Estimation of Photos using a Hierarchical Model and Scene Classification-
PlaNet (6.2M)18.165.845.6-030.06.36.2MPlaNet - Photo Geolocation with Convolutional Neural Networks
Im2GPS ([L] KNN, sigma=4)33.371.357.4-044.312.26MRevisiting IM2GPS in the Deep Learning Era-
Im2GPS (... 28m database)33.373.461.6-28M47.714.46MRevisiting IM2GPS in the Deep Learning Era-
ISNs (M, f*, S3)43.080.266.7-051.916.94.7MGeolocation Estimation of Photos using a Hierarchical Model and Scene Classification-
PlaNet (91M)24.571.353.6-037.68.491MPlaNet - Photo Geolocation with Convolutional Neural Networks
CPlaNet (1-5, PlaNet)37.178.562.0-046.616.530.3MCPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps-
Im2GPS ([L] 7011C)21.963.749.4-034.66.86MRevisiting IM2GPS in the Deep Learning Era-
base (M, f*)40.978.565.4-051.515.24.7MGeolocation Estimation of Photos using a Hierarchical Model and Scene Classification-
StreetCLIP (Zero-Shot)28.388.274.7-045.1-1.1MLearning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
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