Flower Categorization using Deep Convolutional Neural Networks
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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 whereas the top 1 and top 5 accuracies of AlexNet are 43.39 respectively. These results are extremely good when compared to random classification accuracy of 0.98 be implemented in real time applications and can be used to help botanists for their research as well as camping enthusiasts.
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