Flower Categorization using Deep Convolutional Neural Networks

We have developed a deep learning network for classification of different flowers. For this, we have used Visual Geometry Group’s 102 category flower data-set having 8189 images of 102 categories from Oxford University. The method is basically divided in two parts i.e. Image segmentation and classification. We have compared two different Convolutional Neural Network architectures GoogLeNet and AlexNet for the classification purpose. By keeping same hyper-parameters for both the architectures, we have found that the Top-1 and Top-5 accuracies of GoogLeNet are 47.15% and 69.17% respectively whereas the Top-1 and Top-5 accuracies for AlexNet are 43.39% and 68.68% respectively. These results are extremely good when compared to random classification accuracy of 0.98%. This method for classification of flowers can be implemented in realtime applications and can be used to help botanists for their research as well as camping enthusiasts. Keywords— Image Segmentation, Deep Convolutional Neural Networks, Classification, Transfer Learning