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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
Umut Özkaya1*, Levent Seyfi2
1Konya Technical University, Konya, Turkey
2Konya Technical University, Konya, Turkey
* Corresponding author:
Published Date: 2019-01-14   |   Page (s): 514-517   |    243     8

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
REFERENCES [1] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates,Inc., 2012, pp. 1097–1105.
[2] J. Donovan, “Auto-trash sorts garbage automatically at the techcrunch disrupt hackathon.”
[3] G. Mittal, K. B. Yagnik, M. Garg, and N. C. Krishnan, “Spotgarbage: Smartphone app to detect garbage using deep learning,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ser. UbiComp ’16. New York, NY, USA: ACM, 2016, pp. 940–945.
[4] S. Zhang and E. Forssberg, “Intelligent liberation and classification of electronic scrap,” Powder technology, vol. 105, no. 1, pp. 295–301, 1999.
[5] C. Liu, L. Sharan, E. H. Adelson, and R. Rosenholtz, “Exploring features in a bayesian framework for material recognition,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010, pp. 239–246.
[6] Thung, Gary and M. Yang. “Classification of Trash for Recyclability Status.” (2016).
[7] Bircanoglu, C., Atay, M., Beser, F., Genc, O., & Kizrak, M. A. (2018). RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks. 2018 Innovations in Intelligent Systems and Applications (INISTA). doi:10.1109/inista.2018.8466276.
[8] Abubaker A., 2012. Mass Lesion Detection Using Wavelet Decomposition Transform and Support Vector Machine, IJCSIT, 4(2), 33–46.
[9] Saitta L., 1995. Support-Vector Networks, Machine Learning, 20, 273–297.
[10] Duda R.O., Hart E.P., Stork D.G., 2006. Pattern Classification, 2nd ed., John Wiley & Sons Asia PTE.
[11] Vanschoenwinkel B., Manderick B., 2005. Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data, Machine Learning Workshop LNAI, 3635, 255–279.
[12] G. Thung, “Trashnet,” GitHub repository, 2016.

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