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Image Classification research benchmarks – Page 308 · SOTA2 Research

Image Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

570Avg Samples per Episode

3

Feb 26, 2026

14,651.4Samples per Episode

3

Feb 26, 2026

10,000Avg Samples per Episode

3

Feb 26, 2026

12,000Avg Samples per Episode

3

Feb 26, 2026

59.43Defocus Blur Severity 1 Accuracy

3

Feb 26, 2026

31.4Training Time (h)

3

Feb 26, 2026

30.53Training Time (h)

3

Feb 26, 2026

Authority Backdoor

94.13Acc (Auth)

3

Feb 26, 2026

8,028.78Throughput (img/s)

3

Feb 26, 2026

7,975.09Throughput (img/s)

3

Feb 26, 2026

Clothing-1M (clean-labeled)

78.99Top-1 Accuracy

3

Feb 26, 2026

FGVCAircraft (10% labels)

57.1Top-1 Acc

3

Feb 26, 2026

Flowers102 1-shot

Meta Co-Training

99.6Top-1 Accuracy

3

Feb 26, 2026

Food101 (1% labels)

Meta Co-Training

91.7Top-1 Acc

3

Feb 26, 2026

iNaturalist 1010 most frequent classes (1% labels)

Meta Co-Training

58.1Top-1 Acc

3

Feb 26, 2026

iNaturalist 1010 most frequent classes (10% labels)

Meta Co-Training

76Top-1 Acc

3

Feb 26, 2026

ImageNet-1K (test val)

0.026ECE

3

Feb 26, 2026

CIFAR-10 (test)

0.93ECE

3

Feb 26, 2026

306.9Cross-Entropy Loss

3

Feb 26, 2026

91.73Clean Accuracy

3

Feb 26, 2026

VisDA-V Unauthorized domain

20.9Accuracy

3

Feb 26, 2026

STL10 Authorized domain

0.864Accuracy

3

Feb 26, 2026

ImageNet-1k (test)

37.6Zero-shot Accuracy

3

Feb 26, 2026

88.4Accuracy

3

Feb 26, 2026

Describable Textures Dataset 8-shot

0.742Accuracy

3

Feb 26, 2026

Page 308 of 333

Popular datasets

CIFAR ImageNet MNIST CIFAR-10 CIFAR-100

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