Research on Flower Image Classification Based on Transfer Learning

Abstract

Due to the high similarity between flowers, it is difficult to identify them if they do not have the corresponding biological knowledge when classifying varieties manually. Given the above problems, to improve the accuracy and efficiency of flower classification, this paper proposes a migration parameter pre-training and fine-tuning VGG16 model based on the ImageNet data set to solve this problem. In this paper, the grid coverage enhancement method enhances the flower classification data set to expand the training sample data. The model uses the migration learning pre-training and fine-tuning method to improve network stability and accelerate network convergence. The results of comparative experiments show that the performance of the improved model has been significantly improved, and the result is better on the flower image data set, which has specific practical value.