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Photo Geolocation Estimation On Im2Gps3K

Métriques

City level (25 km)

Continent level (2500 km)

Country level (750 km)

Region level (200 km)

Street level (1 km)

Training Images

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	City level (25 km)	Continent level (2500 km)	Country level (750 km)	Region level (200 km)	Street level (1 km)	Training Images	Paper Title	Repository
Translocator	31.1	80.1	58.9	46.7	11.8	4.7M	Where in the World is this Image? Transformer-based Geo-localization in the Wild
PIGEOTTO	36.7	85.3	72.4	53.8	11.3	4.5M	PIGEON: Predicting Image Geolocations
GeoCLIP	34.5	83.8	69.7	50.7	14.1	4.7M	GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
GeoDecoder	33.5	76.1	61.0	45.9	12.8	4.7M	Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes	-
StreetCLIP (Zero-Shot)	22.4	80.4	61.3	37.4	-	1.1M	Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
Im2GPS (kNN, sigma = 4)	19.4	55.9	38.9	26.9	7.2	6M	Revisiting IM2GPS in the Deep Learning Era	-
ISNs (M, f*, S3)	28.0	66.0	49.7	36.6	10.5	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
base (M, f*)	27.0	66.0	49.2	35.6	9.7	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
base (L, m)	24.9	65.8	48.8	34.0	8.3	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
Im2GPS ([M] 7011C)	14.2	52.7	33.5	21.3	3.7	6M	Revisiting IM2GPS in the Deep Learning Era	-
CPlaNet (1-5, PlaNet)	26.5	64.4	48.6	34.6	10.2	30.3M	CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps	-
Im2GPS ([L] 7011C)	14.8	52.4	32.6	21.4	4.0	6M	Revisiting IM2GPS in the Deep Learning Era	-

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