EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle

• Published in: arXiv.org 2022-10
• Main Authors: Hao, Yuying, Liu, Yi, Chen, Yizhou, Lin, Han, Peng, Juncai, Tang, Shiyu, Chen, Guowei, Wu, Zewu, Chen, Zeyu, Lai, Baohua
• Format: Article
• Language: English
• Subjects: Algorithms; Annotations; Image quality; Image segmentation; Machine learning; Medical imaging; Neural networks; Remote sensing; Source code
• ISSN: 2331-8422

In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the full potential of such models, large numbers of high-quality annotated images are necessary for model training. Currently, many widely used open-source image segmentation software relies heavily on manual annotation which is tedious and time-consuming. In this work, we introduce EISeg, an Efficient Interactive SEGmentation annotation tool that can drastically improve image segmentation annotation efficiency, generating highly accurate segmentation masks with only a few clicks. We also provide various domain-specific models for remote sensing, medical imaging, industrial quality inspections, human segmentation, and temporal aware models for video segmentation. The source code for our algorithm and user interface are available at: https://github.com/PaddlePaddle/PaddleSeg.