FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
This tool addresses the problem of expensive and time-consuming data annotation for the computer vision community, offering a practical solution for crowdsourced or private use, though it is incremental as it builds on existing scribble-based annotation methods.
The authors tackled the high cost of image segmentation annotation by introducing FreeLabel, an open-source web tool that enables users to create high-quality masks with just a few freehand scribbles in seconds, achieving results comparable to manual annotation on datasets like PASCAL and an agricultural dataset.
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.