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## U^2-Net
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection, [Xuebin Qin](https://webdocs.cs.ualberta.ca/~xuebin/), [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/)."
## U^2-Net Results
## U^2-Net Results (173.6 MB)
![U^2-Net Results](figures/u2netqual.png)
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```
git clone https://github.com/NathanUA/U-2-Net.git
```
2. Download the pre-trained model [u2net.pth 173.6 MB](https://drive.google.com/file/d/1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ/view?usp=sharing) or [u2netp.pth 4.7 MB](https://drive.google.com/file/d/1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy/view?usp=sharing) and put it into the dirctory './saved_models/u2net/' and './saved_models/u2netp/'
2. Download the pre-trained model [u2net.pth (173.6 MB)](https://drive.google.com/file/d/1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ/view?usp=sharing) or [u2netp.pth (4.7 MB)](https://drive.google.com/file/d/1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy/view?usp=sharing) and put it into the dirctory './saved_models/u2net/' and './saved_models/u2netp/'
3. Cd to the directory 'U-2-Net', run the train or inference process by command: ```python u2net_train.py```
or ```python u2net_test.py``` respectively. The 'model_name' in both files can be changed to 'u2net' or 'u2netp' for using different models.