Kili Technology is an image, text and voice data annotation tool designed to help companies deploy machine learning applications faster. In a few minutes you can start annotating your data thanks to a catalogue of intuitive and configurable interfaces. You can easily accelerate the labeling process by connecting one of your models to pre annotate the data. The work of the annotators is 2 to 5 times faster. Kili Technology facilitates collaboration between technical teams and the business, but also with outsourced annotation companies. Data governance is managed, and production quality control is facilitated. Kili Technology meets the needs of small teams as well as those of large companies with massive stakes.
Kili Technology allows you to:
- Quickly annotate text, images, video, audio and frames (3D images, DICOM Images and scans) thanks to simple and intuitive interfaces
- Easily ingest data, in drag & drop, from your cloud provider, or while keeping your data On Premise, when necessary.
- Manage participants, roles and responsibilities
- Monitor production quality using leading indicators and workflows for production monitoring and data quality validation
- Easily export the produced data
| Named Entities Extraction and Relation | Rich format support |
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| Classification | Object detection (bounding-box here) |
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| Video annotation | Video classification |
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| Speech to Text | |
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Kili Playground is a Python client wrapping the GraphQL API of Kili Technology. It allows data scientists and developers to control Kili Technology from an IDE.
- Clone the repository and install with pip
pip install kili- Export an API KEY In
My Account->API KEY:
- In your favourite IDE :
from kili.client import Kili
kili = Kili(api_key='MY API KEY')
# You can now play with the playgroundYou can follow those tutorials to get started :
You can find all of recipes here. Among them:
- How to import assets (run it here)
- How to export labels (run it here)
- How to import predictions (run it here)
- How to query data through the API (run it here)
- How to use AutoML for faster labeling with Kili (run it here)
- How to use Transfer Learning for faster labeling with Kili (run it here)
If you want more details on what you can do with the API, follow the technical documentation.