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.
翻译:对图像分割数据集进行大规模批注往往费用高得令人望而却步,因为这通常需要大量工时才能获得高质量的结果。然而,大量可靠的数据对于通过深层学习模型完成图像理解任务的进展至关重要。在本文中,我们引入了FreeLabel,这是一个直观的开放源网络界面,用户可以在几秒钟内获得高质量的分离面罩,只有几条自由手笔迹。FreeLabel的功效通过PACAL数据集和农业领域的数据集的实验结果在数量上得到证明。为了让计算机视觉界受益,FreeLabel可以同时用于众源或私人笔记,并且有一个模块结构,可以很容易地用于任何图像数据集。