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