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README.md
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The code for our newly accepted paper in Pattern Recognition 2020: 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: 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/).
__Contact__: xuebin[at]ualberta[dot]ca
## U^2-Net Results (173.6 MB) ## U^2-Net Results (173.6 MB)
![U^2-Net Results](figures/u2netqual.png) ![U^2-Net Results](figures/u2netqual.png)
__Contact__: xuebin[at]ualberta[dot]ca
## Our previous work: [BASNet (CVPR 2019)](https://github.com/NathanUA/BASNet) ## Our previous work: [BASNet (CVPR 2019)](https://github.com/NathanUA/BASNet)

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u2net_train.py
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@ -111,7 +111,7 @@ ite_num = 0
running_loss = 0.0 running_loss = 0.0
running_tar_loss = 0.0 running_tar_loss = 0.0
ite_num4val = 0 ite_num4val = 0
save_frq = 10 # save the model every 2000 iterations save_frq = 2000 # save the model every 2000 iterations
for epoch in range(0, epoch_num): for epoch in range(0, epoch_num):
net.train() net.train()