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Multi-Label Classification research benchmarks – Page 13 · SOTA2 Research

Multi-Label Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

VIGIL 1.0 (test)

50.29Macro F1-Score (Clothing)

4

Feb 26, 2026

BigEarthNet 10% new class labels (train)

85.11mAP

4

Feb 26, 2026

COCO-20i (4 folds average)

62.3Error Rate

4

Feb 26, 2026

87.4Abnormality Acc

4

Feb 26, 2026

0.904Abnormality AUC

4

Feb 26, 2026

57.3ML F1

4

Feb 26, 2026

Open Images v6 (test)

86.8mAP

4

Feb 26, 2026

Pascal-VOC (val)

ViT-B-16 (Semantic Softmax)

93.1mAP

4

Feb 26, 2026

87.34mAP (C)

4

Feb 26, 2026

MultiTHUMOS (test)

0.017HL

4

Feb 26, 2026

55.46mAP

4

Feb 26, 2026

Zhihu dataset (test)

1.267Precision

4

Feb 26, 2026

DBLP Author Citation (90% train)

66.05Micro-F1

4

Feb 26, 2026

DBLP Author Citation (80% train)

66.12Micro-F1

4

Feb 26, 2026

DBLP Author Citation (70% train)

66.3Micro-F1

4

Feb 26, 2026

DBLP Author Citation (60% train)

66.25Micro-F1

4

Feb 26, 2026

DBLP Author Citation (50% train)

66.19Micro-F1

4

Feb 26, 2026

DBLP Author Citation (30% train)

65.85Micro-F1

4

Feb 26, 2026

DBLP Author Citation (10% train)

64.69Micro-F1

4

Feb 26, 2026

Multi-label error pattern classification dataset

0.809AUROC (Micro)

3

Mar 18, 2026

79.77Weighted F1

3

Mar 13, 2026

70.7Weighted F1

3

Mar 13, 2026

68.5Weighted F1

3

Mar 13, 2026

HateXplain (test)

5.89Hamming Loss

3

Mar 11, 2026

MHS Corpus (test)

6.85Hamming Loss

3

Mar 11, 2026

Page 13 of 14

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