superpixel_fcn:[CVPR'20] SpixelFCN-源码

上传者: 42120405 | 上传时间: 2021-07-22 16:06:26 | 文件大小: 50.39MB | 文件类型: ZIP
SpixelFCN:具有完全卷积网络的超像素分割 这是CVPR-20文件中介绍的超像素分割网络的PyTorch实施: ,,和 如有任何疑问,请联系( )。 先决条件 该培训代码主要是使用python 2.7,PyTorch 0.4.1,CUDA 9和Ubuntu 16.04开发和测试的。 在测试过程中,我们利用中的组件连接方法来增强超像素的连接性。该代码已包含在/third_paty/cython 。要编译它: cd third_party/cython/ python setup.py install --user cd ../.. 演示版 演示脚本run_demo.py使用我们的预训练模型(在/pretrained_ckpt )为网格尺寸为16 x 16的超run_demo.py提供了像素。请随时通过将其复制到/demo/inputs来提供您自己的图像,然后运行 python ru

文件下载

资源详情

[{"title":"( 33 个子文件 50.39MB ) superpixel_fcn:[CVPR'20] SpixelFCN-源码","children":[{"title":"superpixel_fcn-master","children":[{"title":"models","children":[{"title":"__init__.py <span style='color:#111;'> 35B </span>","children":null,"spread":false},{"title":"Spixel_single_layer.py <span style='color:#111;'> 3.66KB </span>","children":null,"spread":false},{"title":"model_util.py <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false}],"spread":true},{"title":"loss.py <span style='color:#111;'> 1.40KB </span>","children":null,"spread":false},{"title":"main.py <span style='color:#111;'> 17.55KB </span>","children":null,"spread":false},{"title":"run_demo.py <span style='color:#111;'> 5.76KB </span>","children":null,"spread":false},{"title":"run_infer_nyu.py <span style='color:#111;'> 6.21KB </span>","children":null,"spread":false},{"title":"nyu_test_set","children":[{"title":"nyu_preprocess_tst.tar.gz <span style='color:#111;'> 26.40MB </span>","children":null,"spread":false}],"spread":true},{"title":"data_preprocessing","children":[{"title":"val.txt <span style='color:#111;'> 662B </span>","children":null,"spread":false},{"title":"pre_process_bsd500.py <span style='color:#111;'> 6.55KB </span>","children":null,"spread":false},{"title":"pre_process_bsd500_ori_sz.py <span style='color:#111;'> 5.32KB </span>","children":null,"spread":false},{"title":"test.txt <span style='color:#111;'> 1.31KB </span>","children":null,"spread":false},{"title":"train.txt <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"flow_transforms.py <span style='color:#111;'> 12.45KB </span>","children":null,"spread":false},{"title":"third_party","children":[{"title":"cython","children":[{"title":"connectivity.pyx <span style='color:#111;'> 4.61KB </span>","children":null,"spread":false},{"title":"connectivity.c <span style='color:#111;'> 935.23KB </span>","children":null,"spread":false},{"title":"setup.py <span style='color:#111;'> 200B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"demo","children":[{"title":"inputs","children":[{"title":"bedroom.jpg <span style='color:#111;'> 41.70KB </span>","children":null,"spread":false},{"title":"birds.jpg <span style='color:#111;'> 86.40KB </span>","children":null,"spread":false},{"title":"Lena.jpg <span style='color:#111;'> 70.71KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"pretrain_ckpt","children":[{"title":"SpixelNet_bsd_ckpt.tar <span style='color:#111;'> 26.08MB </span>","children":null,"spread":false}],"spread":true},{"title":"train_util.py <span style='color:#111;'> 14.81KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 5.56KB </span>","children":null,"spread":false},{"title":"run_infer_bsds.py <span style='color:#111;'> 7.88KB </span>","children":null,"spread":false},{"title":"datasets","children":[{"title":"listdataset.py <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"util.py <span style='color:#111;'> 1.31KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 48B </span>","children":null,"spread":false},{"title":"BSD500.py <span style='color:#111;'> 2.22KB </span>","children":null,"spread":false}],"spread":false},{"title":"eval_spixel","children":[{"title":"plot_benchmark_curve.m <span style='color:#111;'> 7.17KB </span>","children":null,"spread":false},{"title":"loadcsv.m <span style='color:#111;'> 444B </span>","children":null,"spread":false},{"title":"copy_resCSV.py <span style='color:#111;'> 868B </span>","children":null,"spread":false},{"title":"my_eval.sh <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false}],"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