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ResNet in TensorFlow

Deep residual networks, or ResNets for short, provided the breakthrough idea of identity mappings in order to enable training of very deep convolutional neural networks. This folder contains an implementation of ResNet for the ImageNet dataset written in TensorFlow.

In testing we found v1.5 requires ~12% more compute to train and has 6% reduced throughput for inference compared to ResNetv1. CIFAR-10 ResNet does not use the bottleneck and is thus the same for v1 as v1.5.


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