1 - Data

Required Steps:

1. Select image folder
2. Select masks folder
3. Click Load Data

Input Details:

• Images must have unique name or ID
  • _0001.tif --> name/ID: 0001; img_5.png --> name/ID: img5, ...
  • Arbitrary number of channels (e.g., 1 greyscale; 3 RGB)
• Corresponding masks must start with name or ID + a mask suffix__
  • Semantic segmentation mask pixel values: background-class: 0; foreground-classes: 1,2,...,C (or 255 if binary)
  • Instance segmentation mask pixel values (binary only): background-class: 0; foreground-instances: 1,2,...,I
  • _0001 -> 0001_mask.png (mask_suffix = "mask.png")
  • _0001 -> 0001.png (masksuffix = ".png")
  • mask suffix is inferred automatically
  • binary segmentations of an image, that is, there must be a single foreground value that represents positively classified pixels
  • instance segmentations of an image (instances represent positively classified pixels)

Examplary input folder structure:

──images            -> one image folder
  │   0001.tif      
  │   0002.tif
──masks             -> one masks folder
  │   0001_mask.png
  │   0002_mask.png

All common image formats (tif, png, etc.) are supported. See imageio docs.

2 - Ensemble Training

Required Steps:

1. Click Start Training
   • Optional customize train settings

3 - Validation

Required Steps:

1. Click Run Validation