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Smart Bins have become popular in smart cities and campuses around the world.These bins have a compaction mechanism that increases the bins' capacity aswell as automated real-time collection notifications. In this paper, we proposeWasteNet, a waste classification model based on convolutional neural networksthat can be deployed on a low power device at the edge of the network, such asa Jetson Nano. The problem of segregating waste is a big challenge for manycountries around the world. Automated waste classification at the edge allowsfor fast intelligent decisions in smart bins without needing access to thecloud. Waste is classified into six categories: paper, cardboard, glass, metal,plastic and other. Our model achieves a 97\% prediction accuracy on the testdataset. This level of classification accuracy will help to alleviate somecommon smart bin problems, such as recycling contamination, where differenttypes of waste become mixed with recycling waste causing the bin to becontaminated. It also makes the bins more user friendly as citizens do not haveto worry about disposing their rubbish in the correct bin as the smart bin willbe able to make the decision for them.

WasteNet: Waste Classification at the Edge for Smart Bins