AI/U-2-Net
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input size and readme

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Nathan Qin 2020-05-16 13:54:08 -06:00
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README.md
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@ -11,7 +11,7 @@ __Contact__: xuebin[at]ualberta[dot]ca
**(2020-May-16)** We fixed the upsampling issue of the network. Now, the model should be able to handle **arbitrary input size**. (Tips: This modification is to facilitate the retraining of U^2-Net on your own datasets. When using our pre-trained model on SOD datasets, please keep the input size as 320x32 to guarantee the performance.) **(2020-May-16)** We fixed the upsampling issue of the network. Now, the model should be able to handle **arbitrary input size**. (Tips: This modification is to facilitate the retraining of U^2-Net on your own datasets. When using our pre-trained model on SOD datasets, please keep the input size as 320x32 to guarantee the performance.)
**(2020-May-16)** We highly appreciate Cyril Diagne for building this fantastic AR project: [AR Copy and Paste](https://github.com/cyrildiagne/ar-cutpaste) using our [BASNet](https://github.com/NathanUA/BASNet) and U^2-Net. The [demo video](https://twitter.com/cyrildiagne/status/1256916982764646402) has achieved over **5M** views, which is phenomenal and show us more probabilities of SOD. **(2020-May-16)** We highly appreciate Cyril Diagne for building this fantastic AR project: [AR Copy and Paste](https://github.com/cyrildiagne/ar-cutpaste) using our **U^2-Net** and [**BASNet**](https://github.com/NathanUA/BASNet). The [demo video](https://twitter.com/cyrildiagne/status/1256916982764646402) in twitter has achieved over **5M** views, which is phenomenal and shows us more probabilities of SOD.
## U^2-Net Results (173.6 MB) ## U^2-Net Results (173.6 MB)