Home Projects DIS
DIS
Jupyter Notebook

DIS

This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

by xuebinqin · GitHub
Stars
Forks
License
Created
Last commit
background-removalcomputer-visiondeep-learningApache-2.0Jupyter Notebook
View on GitHub
In plain words

Try out a demo for accurately separating images into different parts using a specialized deep learning model.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
DIS — GitHub preview card
📈 Star history
2.57k2.56k
2026-07-072026-08-31
📈 Track DIS

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

DIS has 2.6k stars on GitHub. It has been forked 289 times. DIS is written mainly in Jupyter Notebook. It has been in active development since 2022. DIS is available under the Apache-2.0 license. Its main topics are background-removal, computer-vision, deep-learning, dichotomous-image-segmentation.

Frequently asked questions

What is DIS?

This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

Is DIS open source?

DIS is an open-source project. It is released under the Apache-2.0 license.

Is DIS free?

Yes. DIS is free and open source — you can use, modify and self-host it.

What license does DIS use?

DIS is available under the Apache-2.0 license.

What language is DIS written in?

DIS is written mainly in Jupyter Notebook.

🏅 Maintainer of this project?
olud.ai badge — DIS

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=xuebinqin-dis)](https://olud.ai/project/xuebinqin-dis.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.