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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Photo Geolocation Estimation On Im2Gps

평가 지표

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

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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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Photo Geolocation Estimation On Im2Gps | SOTA | HyperAI초신경