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Multi-Label Classification research benchmarks – Page 6 · SOTA2 Research

Multi-Label Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

YouTube (1% train ratio)

40.2Micro-F1

10

Feb 26, 2026

91.37mAP

9

May 28, 2026

NUS-WIDE 81 labels

53.5Precision @ K=3

9

Feb 27, 2026

33.2AP

9

Feb 26, 2026

30.6AP

9

Feb 26, 2026

0.551AP

9

Feb 26, 2026

0.242Micro-F1

9

Feb 26, 2026

41.14Micro-F1

9

Feb 26, 2026

Reuters-21578 (Tail)

0.489Micro-F1

9

Feb 26, 2026

Reuters-21578 (Medium)

80.25Micro-F1

9

Feb 26, 2026

Reuters-21578 (Head)

92.46Micro-F1

9

Feb 26, 2026

Reuters-21578 (Total)

0.907Micro-F1

9

Feb 26, 2026

OpenImages V6 (val)

86.72mAP (C)

9

Feb 26, 2026

Open Images (val)

58.11mAP

9

Feb 26, 2026

97.4Macro AUC

8

Jul 2, 2026

93.6Macro AUC

8

Jul 2, 2026

77.66Macro-AUC

8

May 12, 2026

71.4mAP

8

May 1, 2026

51mAP

8

May 1, 2026

81.6Precision

8

Apr 27, 2026

CPSC 2018 (test)

96.7Macro AUC

8

Jul 2, 2026

0.913Macro AUC

8

Jul 2, 2026

MS COCO 2014 or 2017 (test)

94.29Macro AUC

8

Mar 24, 2026

CIFAR-10 multi-label 28 (beta=0.1, gamma=0.5)

0.922Macro AUC

8

Mar 24, 2026

CIFAR-10 multi-label 28 (beta=0.5, gamma=0.5)

92.2macro AUC

8

Mar 24, 2026

Page 6 of 14

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