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EfficientNets are a family of state-of-the-art image classification modelsbased on efficiently scaled convolutional neural networks. Currently,EfficientNets can take on the order of days to train; for example, training anEfficientNet-B0 model takes 23 hours on a Cloud TPU v2-8 node. In this paper,we explore techniques to scale up the training of EfficientNets on TPU-v3 Podswith 2048 cores, motivated by speedups that can be achieved when training atsuch scales. We discuss optimizations required to scale training to a batchsize of 65536 on 1024 TPU-v3 cores, such as selecting large batch optimizersand learning rate schedules as well as utilizing distributed evaluation andbatch normalization techniques. Additionally, we present timing and performancebenchmarks for EfficientNet models trained on the ImageNet dataset in order toanalyze the behavior of EfficientNets at scale. With our optimizations, we areable to train EfficientNet on ImageNet to an accuracy of 83% in 1 hour and 4minutes.

Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour