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Fine-grained Image Classification research benchmarks – Page 3 · SOTA2 Research

Fine-grained Image Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

iNaturalist 2017

82Accuracy

17

Feb 26, 2026

FGVC (CUB-200, Stanford Dogs, Stanford Cars, NABirds) (test)

90.06Average Top-1 Accuracy

16

Jul 9, 2026

96.72Accuracy

14

Feb 27, 2026

97.9Accuracy

14

Feb 26, 2026

74.2Accuracy

13

Jun 8, 2026

64.4Accuracy

13

Jun 8, 2026

76.4Accuracy

13

Apr 8, 2026

86.65Accuracy

13

Feb 26, 2026

23.7Accuracy

13

Mar 4, 2026

Aircraft New classes

37.63Accuracy

12

Feb 26, 2026

Aircraft Base classes

49.57Accuracy

12

Feb 26, 2026

Flowers Harmonic Mean

86.72Accuracy

12

Feb 26, 2026

Flowers New classes

77.8Accuracy

12

Feb 26, 2026

Flowers Base classes

98.9Accuracy

12

Feb 26, 2026

StanfordCars Harmonic Mean

78.14Accuracy

12

Feb 26, 2026

StanfordCars New classes

0.751Accuracy

12

Feb 26, 2026

OxfordPets New classes

98.07Accuracy

12

Feb 26, 2026

OxfordPets Base classes

96.1Accuracy

12

Feb 26, 2026

Drink-Bottle (test)

Knowledge Mining with Scene Text

85.26mAP

12

Feb 26, 2026

iNaturalist 2018 (test)

88.7Accuracy

12

Feb 27, 2026

iNaturalist 2018

87.5Top-1 Acc

12

Feb 26, 2026

FGVC 1.0 (test)

89.9CUB-2011

11

Feb 26, 2026

Flood Depth (test)

17.1Top-1 Error

11

Feb 26, 2026

Aircrafts (test)

0.828Top-1 Acc

11

Feb 26, 2026

Crowd Activity (test)

Knowledge Mining with Scene Text

87.45mAP

11

Feb 26, 2026

Page 3 of 6

Popular datasets

CUB Flowers Oxford Flowers iNaturalist Stanford Cars

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