PaddleSeg VS Entity

EISeg-Med3D: https://github.com/PaddlePaddle/PaddleSeg/blob/develop/EISeg/med3d/README_en.md

Thank you for your compliment. You are very welcome to try it. If you have any problem, please don't hesitate to put an issue here: https://github.com/PaddlePaddle/PaddleSeg/issues.

Source code implemented by python and models: https://github.com/PaddlePaddle/PaddleSeg

Github: https://github.com/PaddlePaddle/PaddleSeg

https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/EISeg/README_EN.md Open-source, easy-to-use and powerful. Provides specialized models for better performance.

Found relevant code at https://github.com/PaddlePaddle/PaddleSeg + all code implementations here

Github: https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/contrib/PP-HumanSeg

you can put forward an issue here: https://github.com/PaddlePaddle/PaddleSeg/issues/new/choose

Official code is available at: https://github.com/PaddlePaddle/PaddleSeg

Abstract: We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels. It has many practical applications in image manipulation/editing where the segmentation mask quality is typically crucial but category labels are less important. In this setting, all semantically-meaningful segments are equally treated as categoryless entities and there is no thing-stuff distinction. Based on our unified entity representation, we propose a center-based entity segmentation framework with two novel modules to improve mask quality. Experimentally, both our new task and framework demonstrate superior advantages as against existing work. In particular, ES enables the following: (1) merging multiple datasets to form a large training set without the need to resolve label conflicts; (2) any model trained on one dataset can generalize exceptionally well to other datasets with unseen domains. Our code is made publicly available at this https URL.