Fine-Tuning Models Comparisons on Garbage Classification for Recyclability

TL;DR

This paper compares deep learning models for garbage classification to improve recyclability, using transfer learning and different classifiers, achieving high accuracy with GoogleNet+SVM.

Contribution

It evaluates and compares fine-tuned deep learning models and classifiers for garbage type recognition to enhance recycling processes.

Findings

GoogleNet+SVM achieved 97.86% accuracy.

Transfer learning reduced training time and improved accuracy.

Deep learning models effectively classify garbage types.

Abstract

In this study, it is aimed to develop a deep learning application which detects types of garbage into trash in order to provide recyclability with vision system. Training and testing will be performed with image data consisting of several classes on different garbage types. The data set used during training and testing will be generated from original frames taken from garbage images. The data set used for deep learning structures has a total of 2527 images with 6 different classes. Half of these images in the data set were used for training process and remaining part were used for testing procedure. Also, transfer learning was used to obtain shorter training and test procedures with and higher accuracy. As fine-tuned models, Alexnet, VGG16, Googlenet and Resnet structures were carried. In order to test performance of classifiers, two different classifiers are used as Softmax and Support Vector Machines. 6 different type of trash images were correctly classified the highest accuracy with GoogleNet+SVM as 97.86%.