Python-用于物体跟踪的全卷积连体网络SiameseFC的Pytorch实现

上传者: 39841848 | 上传时间: 2020-01-03 11:39:34 | 文件大小: 28.65MB | 文件类型: zip
“用于物体跟踪的全卷积连体网络 - SiameseFC”的Pytorch实现

文件下载

资源详情

[{"title":"( 46 个子文件 28.65MB ) Python-用于物体跟踪的全卷积连体网络SiameseFC的Pytorch实现","children":[{"title":"Pytorch-SiamFC-master","children":[{"title":"vis_app.py <span style='color:#111;'> 5.76KB </span>","children":null,"spread":false},{"title":"train.py <span style='color:#111;'> 25.09KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"exceptions.py <span style='color:#111;'> 888B </span>","children":null,"spread":false},{"title":"bbox_transforms.py <span style='color:#111;'> 2.11KB </span>","children":null,"spread":false},{"title":"crops.py <span style='color:#111;'> 5.48KB </span>","children":null,"spread":false},{"title":"colormaps.py <span style='color:#111;'> 4.81KB </span>","children":null,"spread":false},{"title":"visualization.py <span style='color:#111;'> 3.33KB </span>","children":null,"spread":false},{"title":"profiling.py <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"tensor_conv.py <span style='color:#111;'> 1.27KB </span>","children":null,"spread":false},{"title":"load_baseline.py <span style='color:#111;'> 8.20KB </span>","children":null,"spread":false},{"title":"color_tables","children":[{"title":"inferno.py <span style='color:#111;'> 13.85KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 63B </span>","children":null,"spread":false},{"title":"viridis.py <span style='color:#111;'> 10.85KB </span>","children":null,"spread":false}],"spread":true},{"title":"image_utils.py <span style='color:#111;'> 4.74KB </span>","children":null,"spread":false}],"spread":true},{"title":"profile_script.sh <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":"images","children":[{"title":"second_line.png <span style='color:#111;'> 376.69KB </span>","children":null,"spread":false},{"title":"results_train.png <span style='color:#111;'> 86.65KB </span>","children":null,"spread":false},{"title":"scalars.png <span style='color:#111;'> 75.56KB </span>","children":null,"spread":false},{"title":"quick_train_screen.gif <span style='color:#111;'> 646.45KB </span>","children":null,"spread":false},{"title":"correlation_better.gif <span style='color:#111;'> 1.60MB </span>","children":null,"spread":false},{"title":"train_screen.png <span style='color:#111;'> 123.99KB </span>","children":null,"spread":false},{"title":"viz_app.gif <span style='color:#111;'> 11.31MB </span>","children":null,"spread":false},{"title":"third_line.png <span style='color:#111;'> 17.98KB </span>","children":null,"spread":false},{"title":"first_line.png <span style='color:#111;'> 4.92KB </span>","children":null,"spread":false},{"title":"pairs.png <span style='color:#111;'> 360.61KB </span>","children":null,"spread":false},{"title":"schema.png <span style='color:#111;'> 160.14KB </span>","children":null,"spread":false},{"title":"catpair.png <span style='color:#111;'> 115.05KB </span>","children":null,"spread":false}],"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 18.63KB </span>","children":null,"spread":false},{"title":"appSiamFC","children":[{"title":"display.py <span style='color:#111;'> 7.81KB </span>","children":null,"spread":false},{"title":"app_utils.py <span style='color:#111;'> 2.42KB </span>","children":null,"spread":false},{"title":"producer.py <span style='color:#111;'> 10.91KB </span>","children":null,"spread":false}],"spread":true},{"title":"training","children":[{"title":"experiments","children":[{"title":"default","children":[{"title":"parameters.json <span style='color:#111;'> 459B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"crops_train.py <span style='color:#111;'> 5.50KB </span>","children":null,"spread":false},{"title":"labels.py <span style='color:#111;'> 3.13KB </span>","children":null,"spread":false},{"title":"datasets.py <span style='color:#111;'> 22.58KB </span>","children":null,"spread":false},{"title":"models.py <span style='color:#111;'> 11.88KB </span>","children":null,"spread":false},{"title":"optim.py <span style='color:#111;'> 262B </span>","children":null,"spread":false},{"title":"metrics.py <span style='color:#111;'> 2.90KB </span>","children":null,"spread":false},{"title":"losses.py <span style='color:#111;'> 947B </span>","children":null,"spread":false},{"title":"train_utils.py <span style='color:#111;'> 7.92KB </span>","children":null,"spread":false},{"title":"summary_utils.py <span style='color:#111;'> 9.29KB </span>","children":null,"spread":false}],"spread":true},{"title":"BaselinePretrained.pth.tar <span style='color:#111;'> 14.89MB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 245B </span>","children":null,"spread":false},{"title":"setup.sh <span style='color:#111;'> 534B </span>","children":null,"spread":false},{"title":"environment.yaml <span style='color:#111;'> 3.75KB </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