SRGAN-PyTorch:使用生成的对抗性网络实现逼真的单图像超分辨率的非官方PyTorch实现-源码

上传者: 42115003 | 上传时间: 2021-09-14 16:58:28 | 文件大小: 1.37MB | 文件类型: ZIP
SRGAN-PyTorch 该资源库包含在纸上的非官方pyTorch实施SRGAN也SRResNet的,CVPR17。 我们密切关注原始SRGAN和SRResNet的网络结构,培训策略和培训设置。 我们还CVPR16将子像素卷积层实现为。 也分享了对该存储库的贡献。 许可和引文 所有代码和其他材料(包括但不限于表格)仅用于学术研究目的,不提供任何担保。 任何商业用途都需要我们的同意。 如果我们的工作对您的研究有所帮助,或者您在研究中使用了代码的任何部分,请适当确认: @InProceedings{ledigsrgan17,    author = {Christian Ledig and Lucas Theis and Ferenc Huszár and Jose Caballero and Andrew Cunningham and Alejandro Acosta and

文件下载

资源详情

[{"title":"( 30 个子文件 1.37MB ) SRGAN-PyTorch:使用生成的对抗性网络实现逼真的单图像超分辨率的非官方PyTorch实现-源码","children":[{"title":"SRGAN-PyTorch-master","children":[{"title":"train.py <span style='color:#111;'> 4.59KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"util.py <span style='color:#111;'> 748B </span>","children":null,"spread":false},{"title":"metric.py <span style='color:#111;'> 563B </span>","children":null,"spread":false},{"title":"convert.py <span style='color:#111;'> 1.74KB </span>","children":null,"spread":false},{"title":"logger.py <span style='color:#111;'> 1.15KB </span>","children":null,"spread":false}],"spread":true},{"title":"subset.txt <span style='color:#111;'> 13.08MB </span>","children":null,"spread":false},{"title":"models","children":[{"title":"sr_resnet_model.py <span style='color:#111;'> 4.24KB </span>","children":null,"spread":false},{"title":"modules","children":[{"title":"block.py <span style='color:#111;'> 5.80KB </span>","children":null,"spread":false},{"title":"util.py <span style='color:#111;'> 689B </span>","children":null,"spread":false},{"title":"vgg_feat.py <span style='color:#111;'> 2.19KB </span>","children":null,"spread":false},{"title":"loss.py <span style='color:#111;'> 1.99KB </span>","children":null,"spread":false},{"title":"generator.py <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false},{"title":"discriminator.py <span style='color:#111;'> 1.42KB </span>","children":null,"spread":false}],"spread":true},{"title":"sr_resnet_test_model.py <span style='color:#111;'> 1.58KB </span>","children":null,"spread":false},{"title":"models.py <span style='color:#111;'> 602B </span>","children":null,"spread":false},{"title":"base_model.py <span style='color:#111;'> 919B </span>","children":null,"spread":false},{"title":"sr_gan_model.py <span style='color:#111;'> 9.36KB </span>","children":null,"spread":false},{"title":"networks.py <span style='color:#111;'> 2.34KB </span>","children":null,"spread":false}],"spread":true},{"title":"test.py <span style='color:#111;'> 3.69KB </span>","children":null,"spread":false},{"title":"options","children":[{"title":"options.py <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"train","children":[{"title":"SRGAN_x4.json <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"SRResNet_x4.json <span style='color:#111;'> 1.09KB </span>","children":null,"spread":false}],"spread":true},{"title":"test","children":[{"title":"SRGAN_x4.json <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false},{"title":"SRResNet_x4.json <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"README.md <span style='color:#111;'> 4.49KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"bicubic_down_dataset.py <span style='color:#111;'> 1.43KB </span>","children":null,"spread":false},{"title":"util.py <span style='color:#111;'> 1.12KB </span>","children":null,"spread":false},{"title":"datasets.py <span style='color:#111;'> 427B </span>","children":null,"spread":false},{"title":"transforms.py <span style='color:#111;'> 2.76KB </span>","children":null,"spread":false},{"title":"data_loader.py <span style='color:#111;'> 716B </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明