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SETSCI - Volume 3 (2018)
ISAS2018-Winter - 2nd International Symposium on Innovative Approaches in Scientific Studies, Samsun, Turkey, Nov 30, 2018

Fine-Tuning Models Comparisons on Garbage Classification for Recyclability (ISAS2018-Winter_99)
Umut Özkaya1*, Levent Seyfi2
1Konya Technical University, Konya, Turkey
2Konya Technical University, Konya, Turkey
* Corresponding author: uozkaya@selcuk.edu.tr
Published Date: 2019-01-14   |   Page (s): 514-517   |    8     2

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%.  
KEYWORDS Recycling, Garbage Classification, Transfer Learning, Fine-tuned Models, Softmax, Support Vector Machines
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