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User friendly image bootstraping framework.

Project description

Label wrapper

User friendly image bootstraping framework.

Label bootstrapping flow

Label wrapper enables label bootstrapping process:

  1. Load first data batch
  2. Manually label first batch
  3. Train first segmentation model
  4. Load second data batch
  5. Use first trained segmentation model to predict labels
  6. Review labels and merge first and second labelled data
  7. train the second segmentation model
  8. Repeat steps 4.-7. until out of raw data or review of labels is no longer required.

Label bootstrapping

Technical implementation example

  1. Load data into dataset
  2. Export html
  3. Label
  4. Export to json
  5. Import json and convert json to tfrecords
  6. Train on tfrecords
  7. Introduce new data
  8. Predict with trained model to tf records
  9. Import stored tfrecords and convert to html with labels
  10. Review stored labels and export to json
  11. Join reviewed json and manual json (from step 4)
  12. Repeat 5 - 11 for n times
  13. Run out of data to label
  14. Measure performance


  • Finnish dual data dataset with gtiff (add test)
  • mask to shapefile (geocoded)
  • shapefile exporter
  • shapefile imporoter?
  • example inference step with a pretrained segmentation cnn
  • (maybe) constructor should take json and load it in postinit
  • (maybe) Add via html tests with js (selenium?)


Label editor used is VIA 2.0.6.

Project details

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