CLASSIFICATION OF BAG TYPES IN 360 DEGREE IMAGES (FISH EYE) USING TENSORFLOW

Abstract

Classification of objects is one of the studies that are currently being developed. The impact on the world of fashion is where women and men, teenagers to parents today cannot be separated from the bag as an addition to daily fashion. In the selection of bags, mistakes are often made, so as not to make a wrong choice, in this final project, I classify the types of bags, which is the method used to distinguish the characteristics of bags by type. The bag classification system based on type is a program that can identify a person's bag according to the type that has been trained and stored in the database of the program being run. Classification of bag types can be done in various ways, one of which is Deep Learning with the Convolutional Neural Network (CNN) method, CNN implementation using Tensorflow with the python programming language. This study was conducted using 5 classifications of bag type datasets totaling 6,720 images that have been trained with an image size of 180 x 180 using a 360^o camera. It is hoped that this system is able to work well for classifying bag types in 360^o (fish eye) image format. This study resulted in true detection rates of 55% and false detection of 45% where true detection is seen from the number of truths of accuracy in determining the output results, while false detection is the opposite of true detection from the number of 135 images that have been tested.