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Papers Benchmarks Datasets Tasks

Fine grained classification research benchmarks – Page 5 · SOTA2 Research

Fine grained classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

Oxford Flowers 102 (test)

70.07Accuracy

5

May 1, 2026

97.27Top-1 Accuracy

5

Apr 1, 2026

Fine-grained Classification Suite (FLWR, DTD, PETS, CARS, UCF, CAL, FOOD, SUN, AIR, ESAT) LAION2B-pretrained backbone (test)

70.82Accuracy (FLWR)

5

Feb 26, 2026

OxfordPets (test)

90.57Accuracy

5

Feb 26, 2026

33.3AutoAttack Accuracy

4

Jun 23, 2026

45.1AutoAttack Robust Accuracy

4

Jun 23, 2026

CUB-200-2011 Nc=317 (test)

13.5Top-5 Accuracy

4

May 8, 2026

76.95Base Accuracy

4

Feb 26, 2026

95.78Base Accuracy

4

Feb 26, 2026

UCMerced (test)

47Accuracy

4

Feb 26, 2026

iNaturalist 2021-Mini (test)

66.2Top-1 Accuracy

4

Feb 26, 2026

90.14Accuracy

4

Mar 18, 2026

90.25Last-Task Accuracy (AT)

3

Feb 26, 2026

2.3CW Robustness

3

Feb 27, 2026

0.541CW Robustness Score

3

Feb 27, 2026

Food101 base-to-new class split

90.7Accuracy (Base Classes)

3

Feb 26, 2026

StanfordCars T=20

90.28Last-Task Accuracy (AT)

2

Feb 26, 2026

FGVC-Aircraft T=10

68.02Last-Task Accuracy (AT)

2

Feb 26, 2026

88.89Last Task Accuracy (AT)

1

Feb 26, 2026

FGVC-Aircraft T=20

65.56Last-Task Accuracy (AT)

1

Feb 26, 2026

Stanford Cars 20% symmetric noise (test)

Cross-Entropy + SNSCL

83.24Accuracy

1

Feb 26, 2026

StanfordCars (base)

—

—Accuracy

0

Feb 26, 2026

Page 5 of 5

Popular datasets

Aircraft DTD Caltech Food101 Pets

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