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Photo Geolocation Estimation On Im2Gps

Metriken

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

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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 Title	Repository
PIGEOTTO	40.9	91.1	82.3	70.5	4.5M	63.3	14.8	4.5M	PIGEON: Predicting Image Geolocations
base (L, m)	35.0	79.7	64.1	-	0	49.8	13.5	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
PlaNet (6.2M)	18.1	65.8	45.6	-	0	30.0	6.3	6.2M	PlaNet - Photo Geolocation with Convolutional Neural Networks
Im2GPS ([L] KNN, sigma=4)	33.3	71.3	57.4	-	0	44.3	12.2	6M	Revisiting IM2GPS in the Deep Learning Era	-
Im2GPS (... 28m database)	33.3	73.4	61.6	-	28M	47.7	14.4	6M	Revisiting IM2GPS in the Deep Learning Era	-
ISNs (M, f*, S3)	43.0	80.2	66.7	-	0	51.9	16.9	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
PlaNet (91M)	24.5	71.3	53.6	-	0	37.6	8.4	91M	PlaNet - Photo Geolocation with Convolutional Neural Networks
CPlaNet (1-5, PlaNet)	37.1	78.5	62.0	-	0	46.6	16.5	30.3M	CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps	-
Im2GPS ([L] 7011C)	21.9	63.7	49.4	-	0	34.6	6.8	6M	Revisiting IM2GPS in the Deep Learning Era	-
base (M, f*)	40.9	78.5	65.4	-	0	51.5	15.2	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
StreetCLIP (Zero-Shot)	28.3	88.2	74.7	-	0	45.1	-	1.1M	Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

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