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In this study, it is aimed to develop a deep learning application whichdetects types of garbage into trash in order to provide recyclability withvision system. Training and testing will be performed with image dataconsisting of several classes on different garbage types. The data set usedduring training and testing will be generated from original frames taken fromgarbage images. The data set used for deep learning structures has a total of2527 images with 6 different classes. Half of these images in the data set wereused for training process and remaining part were used for testing procedure.Also, transfer learning was used to obtain shorter training and test procedureswith and higher accuracy. As fine-tuned models, Alexnet, VGG16, Googlenet andResnet structures were carried. In order to test performance of classifiers,two different classifiers are used as Softmax and Support Vector Machines. 6different type of trash images were correctly classified the highest accuracywith GoogleNet+SVM as 97.86%.

Fine-Tuning Models Comparisons on Garbage Classification for Recyclability