  Waste is a wealth in a wrong place. Our research focuses on analyzing
possibilities for automatic waste sorting and collecting in such a way that
helps it for further recycling process. Various approaches are being practiced
managing waste but not efficient and require human intervention. The automatic
waste segregation would fit in to fill the gap. The project tested well known
Deep Learning Network architectures for waste classification with dataset
combined from own endeavors and Trash Net. The convolutional neural network is
used for image classification. The hardware built in the form of dustbin is
used to segregate those wastes into different compartments. Without the human
exercise in segregating those waste products, the study would save the precious
time and would introduce the automation in the area of waste management.
Municipal solid waste is a huge, renewable source of energy. The situation is
win-win for both government, society and industrialists. Because of fine-tuning
of the ResNet18 Network, the best validation accuracy was found to be 87.8%.
