  Large-scale annotation of image segmentation datasets is often prohibitively
expensive, as it usually requires a huge number of worker hours to obtain
high-quality results. Abundant and reliable data has been, however, crucial for
the advances on image understanding tasks achieved by deep learning models. In
this paper, we introduce FreeLabel, an intuitive open-source web interface that
allows users to obtain high-quality segmentation masks with just a few freehand
scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively
demonstrated by experimental results on the PASCAL dataset as well as on a
dataset from the agricultural domain. Designed to benefit the computer vision
community, FreeLabel can be used for both crowdsourced or private annotation
and has a modular structure that can be easily adapted for any image dataset.
