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README.md
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...@@ -73,14 +73,14 @@ python setup.py develop ...@@ -73,14 +73,14 @@ python setup.py develop
- 训练： - 训练：
[ImageNet dataset](https://image-net.org/challenges/LSVRC/2012/2012-downloads.php) [ImageNet dataset](https://image-net.org/challenges/LSVRC/2012/2012-downloads.php)
[DIV2K Train Data (HR images)](http://113.200.138.88:18080/aidatasets/project-dependency/div2k/-/blob/master/DIV2K_train_HR.zip) [DIV2K Train Data (HR images)](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip)
[Flickr2K](http://113.200.138.88:18080/aidatasets/project-dependency/flickr2k/-/blob/master/Flickr2K.tar) [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar)
> 训练数据处理请参考[BasicSR](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md) > 训练数据处理请参考[BasicSR](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md)
- 测试： - 测试：
[Classical SR Testing](http://113.200.138.88:18080/aidatasets/project-dependency/classical-sr) `Classical SR Testing`
数据准备具体步骤如下： 数据准备具体步骤如下：
...@@ -192,7 +192,7 @@ bash val.sh ...@@ -192,7 +192,7 @@ bash val.sh
交通,公安,制造 交通,公安,制造
## 预训练权重 ## 预训练权重
预训练模型下载地址：[SCNet-AIModels](http://113.200.138.88:18080/aimodels/findsource-dependency/hat_pytorch) 预训练模型下载地址：[pretrained models](https://drive.google.com/drive/folders/1HpmReFfoUqUbnAOQ7rvOeNU3uf_m69w0)
## 源码仓库及问题反馈 ## 源码仓库及问题反馈
- https://developer.hpccube.com/codes/modelzoo/hat_pytorch - https://developer.hpccube.com/codes/modelzoo/hat_pytorch
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