pytorch-deeplab-xception:PyTorch中的DeepLab v3 +模型。 支持不同的骨干网

上传者: 42097668 | 上传时间: 2021-09-01 15:38:46 | 文件大小: 559KB | 文件类型: ZIP
pytorch-deeplab-xception 于2018/12/06更新。 提供在VOC和SBD数据集上训练的模型。 于2018/11/24更新。 发布最新版本的代码,该代码可以解决一些以前的问题,并增加对新主干和多GPU培训的支持。 有关以前的代码,请参见上previous分支 去做 支持不同的骨干网 支持VOC,SBD,城市景观和COCO数据集 多GPU训练 骨干 火车/评估系统 价值 预训练模型 ResNet 16/16 78.43% 移动网 16/16 70.81% DRN 16/16 78.87% 介绍 这是的PyTorch(0.4.1)实现。 它可以使用Modified Aligned Xception和ResNet作为主干。 目前,我们使用Pascal VOC 2012,SBD和Cityscapes数据集训练DeepLab V3 Plus。 安装 该代

文件下载

资源详情

[{"title":"( 39 个子文件 559KB ) pytorch-deeplab-xception:PyTorch中的DeepLab v3 +模型。 支持不同的骨干网","children":[{"title":"pytorch-deeplab-xception-master","children":[{"title":"train_coco.sh <span style='color:#111;'> 191B </span>","children":null,"spread":false},{"title":"train.py <span style='color:#111;'> 13.30KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"saver.py <span style='color:#111;'> 2.43KB </span>","children":null,"spread":false},{"title":"loss.py <span style='color:#111;'> 1.89KB </span>","children":null,"spread":false},{"title":"metrics.py <span style='color:#111;'> 1.78KB </span>","children":null,"spread":false},{"title":"calculate_weights.py <span style='color:#111;'> 985B </span>","children":null,"spread":false},{"title":"lr_scheduler.py <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false},{"title":"summaries.py <span style='color:#111;'> 1.15KB </span>","children":null,"spread":false}],"spread":true},{"title":"modeling","children":[{"title":"deeplab.py <span style='color:#111;'> 2.91KB </span>","children":null,"spread":false},{"title":"backbone","children":[{"title":"drn.py <span style='color:#111;'> 14.28KB </span>","children":null,"spread":false},{"title":"xception.py <span style='color:#111;'> 11.28KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 514B </span>","children":null,"spread":false},{"title":"mobilenet.py <span style='color:#111;'> 5.26KB </span>","children":null,"spread":false},{"title":"resnet.py <span style='color:#111;'> 6.08KB </span>","children":null,"spread":false}],"spread":true},{"title":"aspp.py <span style='color:#111;'> 3.52KB </span>","children":null,"spread":false},{"title":"sync_batchnorm","children":[{"title":"comm.py <span style='color:#111;'> 4.34KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 447B </span>","children":null,"spread":false},{"title":"replicate.py <span style='color:#111;'> 3.14KB </span>","children":null,"spread":false},{"title":"unittest.py <span style='color:#111;'> 834B </span>","children":null,"spread":false},{"title":"batchnorm.py <span style='color:#111;'> 12.63KB </span>","children":null,"spread":false}],"spread":true},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"decoder.py <span style='color:#111;'> 2.22KB </span>","children":null,"spread":false}],"spread":true},{"title":"doc","children":[{"title":"deeplab_xception.py <span style='color:#111;'> 15.82KB </span>","children":null,"spread":false},{"title":"deeplab_resnet.py <span style='color:#111;'> 10.98KB </span>","children":null,"spread":false},{"title":"results.png <span style='color:#111;'> 509.04KB </span>","children":null,"spread":false}],"spread":true},{"title":"dataloaders","children":[{"title":"utils.py <span style='color:#111;'> 3.27KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 1.96KB </span>","children":null,"spread":false},{"title":"datasets","children":[{"title":"cityscapes.py <span style='color:#111;'> 5.24KB </span>","children":null,"spread":false},{"title":"coco.py <span style='color:#111;'> 5.58KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"combine_dbs.py <span style='color:#111;'> 3.23KB </span>","children":null,"spread":false},{"title":"pascal.py <span style='color:#111;'> 4.39KB </span>","children":null,"spread":false},{"title":"sbd.py <span style='color:#111;'> 3.99KB </span>","children":null,"spread":false}],"spread":true},{"title":"custom_transforms.py <span style='color:#111;'> 4.93KB </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 3.13KB </span>","children":null,"spread":false},{"title":"train_voc.sh <span style='color:#111;'> 209B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false},{"title":"mypath.py <span style='color:#111;'> 637B </span>","children":null,"spread":false}],"spread":false}],"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