Abstract
Although complicated and deep neural network models can achieve high accuracy
of image recognition, they require huge amount of computations and parameters
and are not suitable for mobile and embedded devices. As a result, MobileNet
was proposed, which can reduce the amount of parameters and computational cost
dramatically. In this paper, we propose two different methods to improve
MobileNet, which are based on adjusting two hyper-parameters width multiplier
and depth multiplier, combing max pooling or Fractional Max Pooling with
MobileNet. We tested the improved models on images classification database
CIFAR-10 and CIFAR-100 with good results .
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