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

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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

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

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