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Model-based pre-annotation

Pre-annotation can help speed up the labeling process for various use cases and tasks.
You can use machine learning to speed up your Kili annotation project by:

Some of our customers who implemented model-based pre-annotation managed to achieve impressive results. Here are some examples:

  • Semantic segmentation: performance increased by 70% (medical imaging project).
  • Bounding box detection: performance increased by 45% (facilities inspection project).
  • NER and text classification: performance increased by 30% (bank and insurance project).
  • Video object tracking: performance increased by 50%.

Learn more

For an end-to-end example of how to programmatically import model-based pre-annotations to a Kili project using Kili's Python SDK, refer to our tutorial on importing assets and labels.