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Classification of objects into their specific classes is always been significant tasks of machine learning. As the study of flower, categorizing specific class of flower is important subject in the field of Botany but the similarity between the diverse species of flowers, texture and color of flowers, and the dissimilarities amongst the same species of flowers, there still are some challenges in the recognition of flower images. Existing recent Google's inception-v3 model comparatively takes more time and space for classification with high accuracy. In this paper, we have shown experimental performance of MobileNets model on TensorFlow platform to retrain the flower category datasets, which can greatly minimize the time and space for flower classification compromising the accuracy slightly.
Gavai et al. (Fri,) studied this question.
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