TrashBox: Trash Detection and Classification using Quantum Transfer Learning

The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset. To the best of our knowledge, TrashBox is the most comprehensive dataset in this field. We also experiment with transfer learning based models trained on TrashBox to evaluate its generalizability, and achieved a remarkable accuracy of 98.47%. Furthermore, a novel deep learning framework leveraging quantum transfer learning was also explored. Experimental evaluation on benchmark datasets has shown very promising results. Further, parallelization was incorporated, which helped optimize the time taken to train the models, recording a 10.84% improvement in the performance and 27.4% decline in training time.