  Retail scenes usually contain densely packed high number of objects in each
image. Standard object detection techniques use fully supervised training
methodology. This is highly costly as annotating a large dense retail object
detection dataset involves an order of magnitude more effort compared to
standard datasets. Hence, we propose semi-supervised learning to effectively
use the large amount of unlabeled data available in the retail domain. We adapt
a popular self supervised method called noisy student initially proposed for
object classification to the task of dense object detection. We show that using
unlabeled data with the noisy student training methodology, we can improve the
state of the art on precise detection of objects in densely packed retail
scenes. We also show that performance of the model increases as you increase
the amount of unlabeled data.
