Image Classification2009en

Canadian Institute for Advanced Research 100

60K 32x32 color images in 100 fine-grained classes grouped into 20 superclasses. More challenging than CIFAR-10.

Samples:60,000
Metrics:accuracy
Paper / WebsiteDownload
Current State of the Art

ViT-H/14

Google

94.55

accuracy

accuracy Progress Over Time

Showing 3 breakthroughs from Aug 2017 to Oct 2020

76.481.386.391.296.2Aug 2017Mar 2019Oct 2020accuracyDate

Key Milestones

Aug 2017
ResNet-50

With Cutout augmentation.

78.0
May 2019
EfficientNet-B7

Transfer learning from ImageNet.

91.7
+17.5%
Oct 2020
ViT-H/14Current SOTA

Fine-tuned from ImageNet pretraining.

94.5
+3.1%
Total Improvement
21.2%
Time Span
3y 3m
Breakthroughs
3
Current SOTA
94.5

Top Models Performance Comparison

Top 4 models ranked by accuracy

accuracy1ViT-H/1494.5100.0%2EfficientNet-B791.797.0%3ViT-B/1691.596.8%4ResNet-5078.082.5%0%25%50%75%100%% of best
Best Score
94.5
Top Model
ViT-H/14
Models Compared
4
Score Range
16.5

accuracyPrimary

#ModelScorePaper / CodeDate
1
ViT-H/14Open Source
Google
94.55Dec 2025
2
EfficientNet-B7Open Source
Google
91.7Dec 2025
3
ViT-B/16Open Source
Google
91.48Dec 2025
4
ResNet-50Open Source
Microsoft
78.04Dec 2025

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