Welcome to the Kili Tutorials Homepage!
We understand that getting started with a new product can sometimes be challenging. That's why we have created this page to provide you with easy-to-follow tutorials that will help you understand how to use the Kili Python SDK in no time.
Here is a brief overview of our tutorials:
Basic project setup
In this tutorial you will learn how to set up a new project in Kili, configure its settings, and add assets and users to it.
This tutorial will show you how to import assets into your Kili project and add asset metadata.
Because videos and Rich Text assets may be more complex to import, we’ve created separate tutorials devoted to them:
In this tutorial you will learn how to import different types of label formats supported by Kili, including model-based pre-annotations and pre-existing labels from other projects.
This tutorial explains how to use a powerful OpenAI Large Language Model (LLM) to generate pre-annotations, which will then be imported into a Named Entity Recognition (NER) Kili project.
For other specific use cases, see these tutorials:
- Importing OCR pre-annotations
- Importing segmentation pre-annotations
- Importing DINOv2 classification pre-annotations
Additionally, we’ve devoted one tutorial to explaining the most common use cases for importing and using model-generated labels: actively monitoring the quality of a model currently deployed to production to detect issues like data drift, and using a model to speed up the process of label creation.
Working with labels
In this section, you’ll learn the various ways you can process labels with Kili.
This tutorial shows you how to upload medical images to Kili using pydicom, upload dicom tags as metadata to your assets, download segmentation labels from Kili, and finally convert them to Numpy masks for visualization with matplotlib.
The Tagtog to Kili tutorial will show you how to convert and import your tagtog assets and labels into Kili.
The label parsing tutorial will show you how you can read and write label data more efficiently.
This tutorial shows how to import COCO annotations into Kili.
This tutorial shows how to import PascalVOC annotations into Kili.
On this tutorial, you will learn how to import GeoJSON annotations into Kili, and how to export Kili annotations to GeoJSON.
In this tutorial you will learn how to manage your review queue, set up quality assurance measures, assign specific labelers to assets, and prioritize assets to be annotated.
Exporting Project Data
This tutorial will show you how to export your project’s assets and labels to different formats supported by Kili.
A plugin is a custom Python script uploaded to Kili and triggered by an event that you define. For instance, you can trigger a specific action when a labeler clicks on Submit.
In this tutorial you will learn how to create your own custom plugins.
Here, you’ll find example use cases for using Kili plugins.
For a more specific use case, follow this tutorial on how to set up and use Kili plugins to monitor the quality of labels added to your project in real-time, without having to involve human reviewers.
Webhooks are really similar to plugins, except they are self-hosted, and require a web service deployed at your end, callable by Kili. To learn how to use webhooks, follow this tutorial.
This tutorial will show you how train an object detection model with Vertex AI AutoML and Kili for faster annotation
For more tutorials and recipes, see our Github repository.