基于Paddle框架的fcanet复现

Related tags

Deep Learningfcanet-Paddle
Overview

fcanet-Paddle

基于Paddle框架的fcanet复现

fcanet

本项目基于paddlepaddle框架复现fcanet，并参加百度第三届论文复现赛，将在2021年5月15日比赛完后提供AIStudio链接～敬请期待

参考项目：

frazerlin-fcanet

数据准备

本项目已挂载论文所使用的数据集，对于tgz及tar文件需要利用以下命令解压

tar -xvf benchmark.tgz
tar xvf VOCtrainval_11-May-2012.tar

整个工程具有以下目录结构

/home/aistudio
|───Data(数据集)
└───────benchmark_RELEASE
└───────VOCdevkit
└───────GrabCut
└───────Berkeley
└───fcanet(代码文件)
└───InitialPaddleModel(初始化权重)

训练

The official PyTorch implementation of CVPR 2020 paper "Interactive Image Segmentation with First Click Attention". 并未提供训练代码。通过邮件联系作者，作者由于企业合作项目原因，合作结束后会将会提供训练代码

测试

模型下载

提取码：2ira

AIStudio链接

验证集测试

python fcanet/evaluate.py --backbone [resnet/res2net] --dataset [GrabCut,Berkeley,DAVIS(not exists in this repo),VOCdevkit] (--sis)

如下图所示，默认的backbone均为101

resnet101测试示例

res2net101测试示例

backbone dataset mNoC mIoU-NoC
resnet101 Berkeley 4.23 [0. 0.728 0.854 0.885 0.912 0.915 0.926 0.935 0.939 0.935 0.94 0.943 0.942 0.944 0.945 0.945 0.947 0.947 0.948 0.947 0.949]
resnet101 GrabCut 2.24 [0. 0.78 0.87 0.923 0.944 0.95 0.956 0.966 0.964 0.971 0.971 0.971 0.975 0.977 0.978 0.979 0.978 0.978 0.979 0.979 0.979]
resnet101 VOC2012 2.9810329734461627 [0. 0.715 0.838 0.885 0.909 0.926 0.937 0.945 0.951 0.957 0.962 0.964 0.967 0.969 0.971 0.973 0.974 0.976 0.977 0.978 0.979]
res2net101 Berkeley 3.98 [0. 0.788 0.872 0.901 0.921 0.93 0.933 0.938 0.938 0.943 0.943 0.943 0.943 0.945 0.947 0.948 0.949 0.949 0.95 0.951 0.95 ]
res2net101 GrabCut 2.16 [0. 0.819 0.877 0.927 0.916 0.931 0.948 0.96 0.966 0.967 0.969 0.971 0.973 0.976 0.977 0.976 0.978 0.977 0.98 0.977 0.979]
res2net101 VOC2012 2.793988911584476 [0. 0.757 0.841 0.882 0.908 0.925 0.937 0.945 0.952 0.958 0.963 0.966 0.968 0.971 0.973 0.974 0.976 0.977 0.978 0.98 0.98 ]

可视化测试

利用annotator.py可以实现可视化操作，感兴趣的读者可是利用Qt实现UI程序，实现效果如下所示

需要注意的是，AIStudio环境暂不支持这种可视化方式，你需要将此仓库部署到本地运行，你可能需要修改代码文件中的路径

python fcanet/annotator.py --backbone res2net --input fcanet/test.jpg --output test_mask.jpg

关于作者

姓名 郭权浩
学校 电子科技大学研2020级
研究方向 计算机视觉
主页 Deep Hao的主页
如有错误，请及时留言纠正，非常蟹蟹！
后续会有更多论文复现系列推出，欢迎大家有问题留言交流学习，共同进步成长！
Owner
QuanHao Guo
master at UESTC
QuanHao Guo
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