Meta Pseudo Labels

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Benchmark Model Rank Results
image-classification-on-imagenetMeta Pseudo Labels (EfficientNet-L2)#5Top 1 Accuracy: 90.2%Number of params: 480MHardware Burden: 95040G…
image-classification-on-imagenetMeta Pseudo Labels (EfficientNet-B6-Wide)#10Top 1 Accuracy: 90%Number of params: 390M
image-classification-on-imagenetMeta Pseudo Labels (ResNet-50)#436Top 1 Accuracy: 83.2%
image-classification-on-imagenet-realMeta Pseudo Labels (EfficientNet-B6-Wide)#4Accuracy: 91.12%
image-classification-on-imagenet-realMeta Pseudo Labels (EfficientNet-L2)#8Accuracy: 91.02%
semi-supervised-image-classification-on-2Meta Pseudo Labels (ResNet-50)#29Top 1 Accuracy: 73.89%Top 5 Accuracy: 91.38%
semi-supervised-image-classification-on-cifarMeta Pseudo Labels (WRN-28-2)#2Percentage error: 3.89± 0.07
semi-supervised-image-classification-on-svhnMeta Pseudo Labels (WRN-28-2)#1Accuracy: 98.01 ± 0.07

Rank counts only results with a code link.