Flower image classification based on generative adversarial network and transfer learning

Abstract

Aiming at the problem that the classification accuracy of the traditional flower classification method is low and the deep neural network requires a large amount of original data. This paper designs a flower classification model that combines generative adversarial network and ResNet-101 transfer learning algorithm, and uses stochastic gradient descent algorithm to optimize the training process of the model. The experimental results on the the international public flower recognition dataset, Oxford flower-102 dataset, show that by enhancing the original data, the accuracy of the network's recognition and classification of flowers is improved. At the same time, the model proposed in this paper is superior to other traditional network models, with higher recognition accuracy and robustness.