  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.
