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Convolution Neural Network Model to Classify Photo Gallery on an Android Device

V. Krishna Mohan, N. Aarthi Sai, S. Arun Kumar, A. Sathwika

Abstract


The classification of images is a classic topic in the domains of image processing, computer vision, and machine learning. Convolution Neural Networks, a sort of deep learning that falls under the Machine Learning sub-domain, have been used in this model. This model was inspired by the problem that students experience at the end of the academic year is that their phones become cluttered with photos from various sources. The aim of this paper is to build a model that separates the photographs into required categories with 98% accuracy. This model takes the folder location as input and evaluates each image before categorizing them into three classes namely Hand written, Text and Personal pictures. As a result, it is simple for the user to free up memory or use for future references in mobile devices like smartphones, tablets and laptops. This method incorporates the MobileNetV2 framework with a fully convolutional developed on real-time data in a GPU scenario.


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References


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