How to Use Dropout Correctly on Residual Networks with Batch Normalization

Benchmark Model Rank Results
fine-grained-image-classification-on-caltechPreResNet-101#13Top-1 Error Rate: 15.8036%
fine-grained-image-classification-on-oxford-2PreResNet-101#16Accuracy: 85.5897
image-classification-on-cifar-10PreResNet-110#150Percentage correct: 94.4367
image-classification-on-cifar-100PreResNet-110#150Percentage correct: 73.98
image-classification-on-imagenetDenseNet-169 (H4*)#730Top 1 Accuracy: 79.152%