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Papers Benchmarks Datasets Tasks

Image Classification research benchmarks – Page 306 · SOTA2 Research

Image Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

CIFAR-100 p_miss = 0.1

3.36Miss Rate (L1)

3

Feb 26, 2026

CIFAR-100 p_miss = 0

3.15Error Rate (Noise Level 1)

3

Feb 26, 2026

CIFAR-10 p_miss = 0

0.35Error (Noise Level 1)

3

Feb 26, 2026

EMNIST balanced clean (train)

AddUNet (7–7–7–7–7) + MLP

91.9Accuracy

3

Feb 26, 2026

EMNIST balanced clean (test)

AddUNet (7–7–7–7–7) + MLP

89.58Accuracy

3

Feb 26, 2026

CIFAR-10 watermarked (10x10 top-left patch) (test)

Zero-Flow Encoders

47.36Accuracy

3

Feb 26, 2026

Digit-MNIST watermarked (random colors) (test)

Zero-Flow Encoders

90.56Accuracy

3

Feb 26, 2026

Fashion-MNIST watermarked (random colors) (test)

Zero-Flow Encoders

80.46Accuracy

3

Feb 26, 2026

0.999Top-1 Accuracy

3

Feb 26, 2026

100Top-1 Acc

3

Feb 26, 2026

92.92Top-1 Accuracy

3

Feb 26, 2026

CIFAR-100C contrast level 4

33Error Rate (Actual)

3

Feb 26, 2026

94.47Accuracy

3

Feb 26, 2026

70.3Accuracy

3

Feb 26, 2026

Fashion-MNIST-Corrupted (test)

88.92Accuracy

3

Feb 26, 2026

MNIST-Modified (test)

94.52Accuracy

3

Feb 26, 2026

93.8Accuracy

3

Feb 26, 2026

Adaptive CFF (Learnable)

93.8Test Accuracy

3

Feb 26, 2026

CIFAR10 Snow Corruption, Setup 2

70.6Accuracy (Severity 0)

3

Feb 26, 2026

CIFAR10 Snow Corruption, Setup 1

91.6Accuracy (Severity 0)

3

Feb 26, 2026

41.2Accuracy

3

Feb 26, 2026

CIFAR-10 Source (test)

81.17Accuracy

3

Feb 26, 2026

ImageNet-1K (test)

81.1Accuracy

3

Feb 26, 2026

33.4∆CA

3

Feb 26, 2026

2.3∆CA

3

Feb 26, 2026

Page 306 of 333

Popular datasets

CIFAR ImageNet MNIST CIFAR-10 CIFAR-100

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