Image annotation tool

Image annotation tool

Kili Technology makes image annotation simple and fast. Classify, draw bounding boxes, polygon, or do segmentation to build your datasets. Highly customizable interfaces allow you to combine tasks to improve productivity.

You are in good company – small and large


Fast image annotation on all use cases

From simple image classification to advanced object detection tasks combining tools as bounding box, polyline, segmentation, you are covered. Each image annotation interface is designed to be more productive. Each image labeling interface allows high composability to adapt to the specific needs of each use case.

For all tasks: image classification single class, multiclass, dropdrown for long list, hierarchical classification to manage complex ontologies. Point, polyline, polygon, bounding box, segmentation are available.

File types: png or jpg.

  • image classification nested
  • image point
  • image polygon
  • image polyline
  • image segmentation


Master labeled image data quality

Kili image labeling online or on-premise tool facilitates collaboration with business experts or external workforce, and data-scientists. Labeled data quality control is build-in thanks to simple & powerful tools: consensus analysis, honeypot, review, and last but not least instructions.



Collaboration made simple between tech teams & annotators

Kili labeling tools online for image, are designed to simply collaboration: on-board business experts or external labeling workforce, build-in data governance tools, powerful data queue management distribution when you need to scale the labeling of a large number of data.

labeling collaboration text data


Work on a solid image annotation platform

Kili tool is available online or on premise. We developed recipes, which are made open source on our Github for the community. These help to use our powerful GraphQL API to simplify interactions with the platform. Kili is designed as an open solution to the ecosystem: you can plug for instance Yolo (open source framework) to do transfer learning in order to speed-up the annotation process; Snorkel to implement weakly supervised learning strategies: it allows to combine simple approaches to build more relevant pre annotators, for instance a specific way to combine the output of 2 models. A Python API makes it even simpler for everyone.




Ready to simplify labelling in your company?

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