Flower Image Classification Using Deep Convolutional Neural Network
Neda Alipour, Omid Tarkhaneh, Mohammad Awrangjeb, Hongda Tian
Abstract
These days deep learning methods play a pivotal role in complicated tasks, such as extracting useful features, segmentation, and semantic classification of images. These methods had significant effects on flower types classification during recent years. In this paper, we are trying to classify 102 flower species using a robust deep learning method. To this end, we used the transfer learning approach employing DenseNet121 architecture to categorize various species of oxford-102 flowers dataset. In this regard, we have tried to fine-tune our model to achieve higher accuracy respect to other methods. We performed preprocessing by normalizing and resizing of our images and then fed them to our fine-tuned pretrained model. We divided our dataset to three sets of train, validation, and test. We could achieve the accuracy of 98.6% for 50 epochs which is better than other deep-learning based methods for the same dataset in the study.