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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
| Date | Stars |
|---|---|
| 2026-07-24 | 9818 |
| 2026-07-25 | 9822 |
| 2026-07-28 | 9828 |
| 2026-07-30 | 9828 |
| 2026-08-06 | 9828 |
Today
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<p align="center">
<img width="320" height="320" src="figures/U2Net_Logo.png">
<h1 align="center">U<sup>2</sup>-Net: U Square Net</h1>
</p>
This is the official repo for our paper **U<sup>2</sup>-Net(U square net)** published in Pattern Recognition 2020:
## [U<sup>2</sup>-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://arxiv.org/pdf/2005.09007.pdf)
[Xuebin Qin](https://xuebinqin.github.io/), [Zichen Zhang](https://webdocs.cs.ualberta.ca/~zichen2/), [Chenyang Huang](https://chenyangh.com/), [Masood Dehghan](https://sites.google.com/view/masooddehghan), [Osmar R. Zaiane](http://webdocs.cs.ualberta.ca/~zaiane/) and [Martin Jagersand](https://webdocs.cs.ualberta.ca/~jag/)
__Contact__: xuebin[at]ualberta[dot]ca
## Updates !!!
** (2022-Aug.-24) ** We are glad to announce that our U<sup>2</sup>-Net published in Pattern Recognition has been awarded the 2020 Pattern Recognition BEST PAPER AWARD !!!
** (2022-Aug.-17) **
Our U<sup>2</sup>-Net models are now available on [PlayTorch](https://playtorch.dev/), where you can build your own demo and run it on your Android/iOS phone. Try out this demo on [](https://playtorch.dev/snack/@playtorch/u2net/) and bring your ideas about U<sup>2</sup>-Net to truth in minutes!
** (2022-Jul.-5)** Our new work **Highly Accurate Dichotomous Image Segmentation (DIS) [**Project Page**](https://xuebinqin.github.io/dis/index.html), [**Github**](https://github.com/xuebinqin/DIS) is accepted by ECCV 2022. Our code and dataset will be released before July 17th, 2022. Please be aware of our updates.
** (2022-Jun.-3)** Thank [**Adir Kol**](https://github.com/adirkol) for sharing the iOS App [**3D Photo Creator**](https://apps.apple.com/us/app/3d-photo-creator/id1619676262) based on our U<sup>2</sup>-Net.
** (2022-Mar.-31)** Thank [**Hikaru Tsuyumine**] for implementing the iOS App [**Portrait Drawing**](https://apps.apple.com/us/app/portrait-drawing/id1623269600) based on our U<sup>2</sup>-Net portrait generation model.
** (2022-Apr.-12)** Thank [**Kevin Shah**](https://github.com/ioskevinshah) for providing us a great iOS App [**Lensto**](https://apps.apple.com/in/app/lensto-background-changer/id1574844033), ([**Demo Video**](https://www.youtube.com/shorts/jWwUiKZjfok)), based on U<sup>2</sup>-Net.
** (2022-Mar.-31)** Our U<sup>2</sup>-Net model is also integrated by [**Hotpot.ai**](https://hotpot.ai/) for art design.
** (2022-Mar-19)** Thank [**Kikedao**](https://github.com/Kikedao) for providing a fantastic webapp [**Silueta**](https://silueta.me/) based on U<sup>2</sup>-Net. More details can be found at [**https://github.com/xuebinqin/U-2-Net/issues/295**](https://github.com/xuebinqin/U-2-Net/issues/295).
** (2022-Mar-17)** Thank [**Ezaldeen Sahb**](https://github.com/Ezaldeen99/BackgroundRemoval) for implementing the iOS library for image background removal based on U<sup>2</sup>-Net, which will greatly facilitate the developing of mobile apps.
<!-- ** (2022-Mar-10)** Thank [**Doron Adler**](https://github.com/Norod/U-2-Net-StyleTransfer) for training the awesome style transfer U<sup>2</sup>-Net.
 -->
** (2022-Mar-8)** Thank [**Levin Dabhi**](https://github.com/levindabhi/cloth-segmentation) for training the amazing clothes segmentation U<sup>2</sup>-Net.
** (2022-Mar-3)** Thank [**Renato Violin**](https://github.com/renatoviolin/bg-remove-auExcerpt of 17,805 characters
Read on GitHub
Xuebin Qin · University of Alberta · Canada
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0568679d60c49f65, topic:computer-vision, topic:image-segmentation, desc:object detection