Xception Architecture Transfer Learning for Garbage Classification

Abstract

Solid waste management issue is main problem especially in developing countries, including Indonesia. Several efforts are made to solve waste management problem. Indonesia government has launched movement to sort different type of garbage on September 2019. Automatic garbage sortation is able to help this program. In order to be able to perform this task, the computer needs to differentiate each type of garbage. This process can be done by using machine learning method to differentiate garbage type. In this research, Transfer learning is used to perform classification task on TrashNet dataset. The models used in this research are ImageNet pretrained VGG16, ResNet-50 and Xception.The experiment result shows that Xception model is able to achieve highest accuracy of 88%, average precision of 84%, and average recall of 84%.