Performance evaluation of pre-trained deep learning model on garbage classification with data augmentation approach

Abstract

Waste classification is one of the interesting topics for classifications in
which data can be very varied and complex. This data diversity is a
challenge to develop a model that is able to classify well. The purpose of this
study is to analyze the performance of the pre-trained deep learning model
using a data augmentation approach. There are three pre-training models
used in this study, namely residual networks 50 (ResNet50), visual
geometric group with 16 layers (VGG-16), and MobileNetV2. The results
showed that the MobileNetV2 model received the highest accuracy value,
reaching 84.45% for data without augmentation. With data augmentation
there is a decrease of 2.73%. Conversely, VGG-16 shows performance
stability with an increase in accuracy with augmentation data, reaching
75.84%. While ResNet50 gets the lowest results compared to both models.
The application of data augmentation techniques with the aim of increasing
data variations does not always have an impact on increasing the
generalization of the model.