  Grocery stores have thousands of products that are usually identified using
barcodes with a human in the loop. For automated checkout systems, it is
necessary to count and classify the groceries efficiently and robustly. One
possibility is to use a deep learning algorithm for instance-aware semantic
segmentation. Such methods achieve high accuracies but require a large amount
of annotated training data.
  We propose a system to generate the training annotations in a weakly
supervised manner, drastically reducing the labeling effort. We assume that for
each training image, only the object class is known. The system automatically
segments the corresponding object from the background. The obtained training
data is augmented to simulate variations similar to those seen in real-world
setups.
