Review of Deep Transfer Learning Models for Image Classification

With the rapid rise in urbanization, solid waste generation has increased exceedingly. This study aims to develop a Convolutional Neural Network (CNN) to classify trash into biodegradable and non-biodegradable wastes. The TrashNet dataset was utilized in this study, and image augmentation was employed to make the model more robust against translation invariance. Transfer Learning methods based on CNN have shown promising outcomes on diverse image classification problems. This paper reviews the deep learning models available with pre-trained weights in the Keras library. The performance of the models was compared, and the model based on NASNetMobile had the highest accuracy of 97%. Further, the model’s hyper-parameters were tuned, and the significance of a hyper-parameter on the model’s accuracy was studied.