A Comparative Analysis of Convolutional Neural Networks for Trash Classification
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Abstract
In the era of the twenty-first century, automation is the biggest field of research. Although several works have been done successfully, there is still so much to do. Increasing trash could be the biggest threat to a better human life in the future. To automate the task, machines need to understand the type of trash to be recycled or separate similar trash for recycling. For this process, the perfect classification of trash plays a vital role in making a better life and a cleaner world. There are so many popular convolutional neural network (CNN) models for image classification. In this work, we examine and analyze the outcomes of several Residual Network (ResNet) and Visual Geometry Group (VGG) CNN models on a trash dataset. Our main investigation is the accuracy evaluation for different VGG and ResNet models where the training dataset, test dataset, number of epochs, and batch size are the same for all the models. Finally, we compare VGG and ResNet models with each other. We have got the peak accuracy for ResNet152 among all ResNet models and the peak accuracy on VGG16 among all VGG models. And we have got the maximum accuracy on ResNet152 among all the ResNet and VGG models which is about 94%.
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