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Object Classification research benchmarks – Page 3 · SOTA2 Research

Object Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

ModelNet10 (clean)

95.82Overall Accuracy

8

Jul 8, 2026

60.8Average Score

8

Feb 27, 2026

CIFAR-10 (test)

100Attack Accuracy

8

Feb 27, 2026

PASCAL VOC 2007

54.3mAP@1

8

Feb 27, 2026

67.82Top-1 Seen Accuracy

8

Feb 26, 2026

Objaverse LVIS 1.0 (evaluation)

46.7Top-1 Acc

8

Feb 26, 2026

CIFAR100 (test)

66.09Accuracy

8

Feb 26, 2026

89.79Accuracy

8

Feb 26, 2026

3Top-1 Accuracy

8

Feb 26, 2026

93.42Accuracy

8

Jul 3, 2026

Caltech-101 (test)

1.04SURFMAE

7

Mar 31, 2026

PACS ResNet-50 (test)

98.36Accuracy (Photo)

7

Feb 26, 2026

3RScan/3DSSG Geometric Segments 1.0 (test)

SceneGraphFusion Fusion

79R@1

7

Feb 26, 2026

SO-Net (3-layer)

95.5Class Acc

7

Feb 26, 2026

36.7Top-1 Balanced Accuracy

6

Jul 3, 2026

Tactile Real-world Object Classification No transfer

99.72Accuracy

6

Jun 23, 2026

Tactile Real-world Object Classification (Intra-sensor set)

90.23Accuracy

6

Jun 23, 2026

0.47SURF MAE

6

Mar 31, 2026

ImageNet InceptionV3 (test)

0.35SURF MAE

6

Mar 31, 2026

57.76Seen Accuracy

6

Feb 26, 2026

ImageNet subset of 2048

76.76Natural Accuracy

6

Feb 26, 2026

VOC 2007 (test)

74.56Accuracy (k=1)

6

Feb 26, 2026

ScanObjectNN noise robustness (test)

88.5Accuracy (OBJ_ONLY)

6

Feb 26, 2026

90.3Plane Accuracy

6

Feb 26, 2026

LINEMOD Synth Objects to LINEMOD (test)

100Accuracy

6

Feb 26, 2026

Page 3 of 5

Popular datasets

ModelNet ScanObjectNN ImageNet CIFAR Caltech

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