  One third of food produced in the world for human consumption --
approximately 1.3 billion tons -- is lost or wasted every year. By classifying
food waste of individual consumers and raising awareness of the measures,
avoidable food waste can be significantly reduced. In this research, we use
deep learning to classify food waste in half a million images captured by
cameras installed on top of food waste bins. We specifically designed a deep
neural network that classifies food waste for every time food waste is thrown
in the waste bins. Our method presents how deep learning networks can be
tailored to best learn from available training data.
