--- title: Tutorial - Training and Validation keywords: fastai sidebar: home_sidebar summary: "Train and validate models ensembles for image segmentation" description: "Train and validate models ensembles for image segmentation" nb_path: "nbs/tutorial_train.ipynb" ---
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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, ...
  • Corresponding masks must start with name or ID + a mask suffix__
    • _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