Flower Categorization using Deep Convolutional Neural Networks

TL;DR

This paper presents a deep learning approach using CNNs, specifically GoogLeNet and AlexNet, for classifying 102 flower categories with high accuracy, demonstrating potential for real-time botanical applications.

Contribution

It compares two CNN architectures for flower classification on a large dataset, providing insights into their relative performance and practical applicability.

Findings

GoogLeNet achieved 47.15% top-1 accuracy and 69.17% top-5 accuracy.

AlexNet achieved 43.39% top-1 accuracy and 68.68% top-5 accuracy.

Both models significantly outperform random classification (0.98%).

Abstract

We have developed a deep learning network for classification of different flowers. For this, we have used Visual Geometry Group's 102 category flower dataset having 8189 images of 102 different flowers from University of Oxford. The method is basically divided into two parts; Image segmentation and classification. We have compared the performance of two different Convolutional Neural Network architectures GoogLeNet and AlexNet for classification purpose. By keeping the hyper parameters same for both 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 of 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 real time applications and can be used to help botanists for their research as well as camping enthusiasts.