EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle

TL;DR

EISeg is an interactive segmentation tool that significantly speeds up image annotation with minimal clicks, supporting multiple domains and offering domain-specific models to enhance annotation efficiency.

Contribution

The paper introduces EISeg, a novel, efficient interactive segmentation tool that reduces annotation time and effort across various domains with specialized models.

Findings

Drastically improves annotation efficiency

Generates highly accurate masks with few clicks

Supports multiple domain-specific models

Abstract

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.