Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition

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Benchmark Model Rank Results
long-tail-learning-on-cifar-100-lt-r-10Difficulty-Net#12Error Rate: 34.78
long-tail-learning-on-cifar-100-lt-r-100Difficulty-Net#21Error Rate: 47.04
long-tail-learning-on-cifar-100-lt-r-50Difficulty-Net#15Error Rate: 43.1
long-tail-learning-on-imagenet-ltDifficulty-Net (ResNet-50 using RandAugment, single model)#29Top-1 Accuracy: 57.4
long-tail-learning-on-imagenet-ltDifficulty-Net (ResNet-50 w/o using RandAugment, single model)#39Top-1 Accuracy: 54.0
long-tail-learning-on-imagenet-ltDifficulty-Net (ResNet-10 w/o using RandAugment, single model#55Top-1 Accuracy: 44.6
long-tail-learning-on-places-ltDifficulty-Net (ResNet-152)#10Top-1 Accuracy: 41.7

Rank counts only results with a code link.