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Image Classification research benchmarks – Page 34 · SOTA2 Research

Image Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

Multi-dataset Average (Pets, Flowers, Aircraft, Cars, Country, Food, STL, GTSRB)

60.5Top-1 Acc

44

Feb 26, 2026

ImageNet 1k (val)

90.664Top-5 Accuracy

44

Feb 26, 2026

CIFAR-10 LeNet-5 (test)

49Communication Rounds

44

Feb 26, 2026

77.2Best Accuracy

44

Mar 4, 2026

ImageNet Base-to-New

75.2H Score

44

Jun 18, 2026

Oxford 102 Flowers (test)

99.1Mean Per-Class Accuracy

44

May 15, 2026

98Accuracy

44

Jul 7, 2026

98.9Plane Accuracy

44

Feb 26, 2026

1.96Error Rate

44

Mar 19, 2026

CIFAR-100 10k labels

0.328Test Error Rate

44

Apr 24, 2026

99.7Accuracy

44

Feb 26, 2026

Birdsnap (test)

85.5Top-1 Acc

44

Feb 26, 2026

AG-MNIST (test)

98.81Top-1 Accuracy

43

Jun 16, 2026

CIFAR-10 uniform label noise, η=0.5 (test)

47.17Accuracy

43

Mar 19, 2026

CIFAR-10 uniform label noise, η=0.3 (test)

57.12Accuracy

43

Mar 19, 2026

CIFAR-10 uniform label noise, η=0.1 (test)

65.08Accuracy

43

Mar 19, 2026

Fashion-MNIST uniform noise η=0.5 (7-fold CV)

84.36Accuracy

43

Mar 19, 2026

Fashion-MNIST uniform noise η=0.4 (7-fold CV)

86.29Accuracy

43

Mar 19, 2026

Fashion-MNIST uniform noise η=0.3 (7-fold CV)

87.45Accuracy

43

Mar 19, 2026

Fashion-MNIST uniform noise η=0.2 (7-fold CV)

88.32Accuracy

43

Mar 19, 2026

Fashion-MNIST uniform noise η=0.1 (7-fold CV)

88.92Accuracy

43

Mar 19, 2026

ImageNet-C Severity 5

6Error Rate (Gaussian)

43

May 20, 2026

ImageNet-1K (test)

80.9Accuracy

43

May 28, 2026

CIFAR-10N (test)

98.8Accuracy (Random 1)

43

May 12, 2026

ImageNet V2 (val)

80.9Top-1 Accuracy

43

Feb 26, 2026

Page 34 of 333

Popular datasets

CIFAR ImageNet MNIST CIFAR-10 CIFAR-100

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