Skip to content

ideas from paper 'BoxSup' and 'Simple does it' and realize it on sputum smear and drone images

Notifications You must be signed in to change notification settings

Richardyu114/weakly-segmentation-with-bounding-box

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

27 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Example Dataset:

Sputum Smear--Makerere University, Uganda

USC Drone

Requirements:

python3
scipy==0.19.0
numpy
pytroch>=1.1.0
torchvision>=0.3.0
PIL
opencv-python
matplotlib
lxml
[pydensecrf](https://github.com/lucasb-eyer/pydensecrf)

You can use pip to install these packages. Please add -i https://mirrors.aliyun.com/pypi/simple after package name if you are in China.

Features

Only support binary pixel classification (one object + background) now!

Model

  • FCN
  • UNet
  • Deeplab v3+

Loss

  • BCE
  • Focal Loss
  • Dice Loss
  • Lovase Loss

Pseudo segmentation label generation

  • all bounding box
  • inner area of bounding box
  • grabcut

Dense CRF for post-process

Training

  • sgd
  • adam
  • update label for iteration training
  • mixup

Further work

  • support multi-classes
  • more fast and simple mIoU calculation
  • more useful model
  • more appropriate optimizer

Example of this project:

GrabCut+FCN+FL

About

ideas from paper 'BoxSup' and 'Simple does it' and realize it on sputum smear and drone images

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages