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Papers Benchmarks Datasets Tasks

Image Classification research benchmarks – Page 167 · SOTA2 Research

Image Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

CIFAR-10 Hue-shifted (test)

93.8Accuracy

10

Feb 26, 2026

Caltech-101 Hue-shifted (test)

74.4Accuracy

10

Feb 26, 2026

Caltech-101 Original (test)

74.38Accuracy

10

Feb 26, 2026

STL-10 100 labels (test)

89.56Top-1 Acc

10

Feb 26, 2026

CIFAR-100 400 labels (test)

78.8Top-1 Acc

10

Feb 26, 2026

CIFAR-100 200 labels (test)

74.17Top-1 Accuracy

10

Feb 26, 2026

STL10 C8-augmented (test)

Prior-Regularized LC

96.37C8-Avg Accuracy

10

Feb 26, 2026

CIFAR100 C8-augmented (test)

Prior-Regularized LC

83.61C8 Average Accuracy

10

Feb 26, 2026

CIFAR10 Noise C (test)

73.3Acc (Gaussian)

10

Feb 26, 2026

CIFAR10-C Far (test)

0.733Acc (Gaussian)

10

Feb 26, 2026

SVHNcore (test)

98.3Accuracy

10

Feb 26, 2026

STL-10 40 labels (test)

85.09Top-1 Accuracy

10

Feb 26, 2026

ImageNet-R Full set

66.75Top-1 Acc

10

May 8, 2026

CIFAR-100 Asymmetric Noisy Labels, r=0.4 (test)

63.3Accuracy

10

Feb 26, 2026

CIFAR-100 Asymmetric Noisy Labels, r=0.3 (test)

65.8Accuracy

10

Feb 26, 2026

CIFAR-100 Asymmetric Noisy Labels, r=0.2 (test)

67.5Accuracy

10

Feb 26, 2026

95.17ANN Accuracy

10

Feb 26, 2026

ImageNet-S target

49.15Top-1 Acc

10

May 8, 2026

CIFAR-10.2 (test)

14.8Error Rate

10

Feb 26, 2026

CIFAR-10 OOD: SVHN (test)

0.843AUROC (H)

10

Feb 26, 2026

FashionMNIST OOD: MNIST (test)

0.971AUROC (H)

10

Feb 26, 2026

13 Transfer Learning Datasets (Aircraft, Caltech101, Cars, CIFAR10, CIFAR100, DTD, Flowers102, Food101, INat19, Pets, STL10, SVHN, Yoga82) (val)

48.6Aircraft Accuracy

10

Feb 26, 2026

93.01Top-1 Acc

10

Feb 26, 2026

CIFAR-10 (test)

95.8Accuracy (Sym. 20%)

10

Feb 26, 2026

Food-101N r ≈ 20% (test)

DivideMix + SNSCL

86.4Accuracy

10

Feb 26, 2026

Page 167 of 333

Popular datasets

CIFAR ImageNet MNIST CIFAR-10 CIFAR-100

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