Rethinking the Inception Architecture for Computer Vision pdf download.
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety oftasks.Since 2014 very deep convolutional networks startedto become mainstream,yielding substantial gains in various benchmarks.Although increased model size and computational cost tend to translate to immediate quality gainsfor most tasks (as long as enough labeled data is providedfor training)，computational efficiency and low parametercount are still enabling factors for various use cases such asmobile vision and big-data scenarios.Here we are exploring ways to scale up networks in ways that aim at utilizingthe added computation as efficiently as possible by suitablyfactorized convolutions and aggressive regularization.Webenchmark our methods on the ILSVRC 2012 classificationchallenge validation set demonstrate substantial gains overthe state of the art:21.2%top-1 and 5.6%top-5 error forsingle frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and withusing less than 25 million parameters.With an ensemble of4 models and multi-crop evaluation,we report 3.5%top-5error and 17.3%top-1 error on the validation set and 3.6%top-5 error on the official test set.