Insights, tips, and product updates

Get to know some common annotation errors and how you can avoid them, image annotation errors-Kili Technology

In this tutorial, we will show how to work with Kili and YOLO v7 to produce a SOTA-grade object detection system.

AI-powered apps fuel more and more companies' growth, and regularly gets involved in GAFAM-related controversies, reliable AI gets more and more coverage. Still, many stakeholders are reluctant to invest in its building. Here's why you should, and how to do so.

Uncover ML engineers' ultimate guide to picking the top data labeling platform. Master assessing data quality, UI, tools, scalability & security for AI triumphs.

The future has arrived, and it's all thanks to the awe-inspiring power of computer vision technology. Let's dive into the leading-edge applications of computer vision and discover where it can lead us!

Labeling Service Providers are an essential part of the Machine Learning industry. As the market crystalizes in 2023, each player will face heavy challenges to establish their differenciation, remain competitive, and secure business. In this article, we'll dive into a few things LSPs can do to remain leaders in their industry.

Image annotation is long, painful, and inefficient? Let's dive into the value of foundation models, and more specifically the Segment Anything Model (SAM) from Meta, to understand how they can improve the labeling experience.

With Mapper algorithm, a tool from Topological Data Analysis, we create a powerful representation of a dataset in conjunction with a model.

Few shot Named Entity Recognition learning methods have been developed to be able to generalize to new tasks with few labeled examples. We will review several recent methods and see how they are different from traditional learning.

Despite the critical role it plays in the success of labeling projects, building efficient labeling guidelines can be very difficult.  This blog will present the general approach and provide tips based on Kili Technology’s expertise in the construction of such guidelines.

A first experiment to automatically detect labeling errors for image object detection projects in Kili

This tutorial will help you understand that how automated machine learning (AutoML) is used to accelerate your labelling process.

Learn how to annotate geospatial data efficiently and accurately at scale.

Plastic pollution in oceans threatens the whole ecosystem! Rivers are the very first source of plastic in Oceans and AI can play a huge role in the struggle against it. Help us to create a major benchmark dataset for training and testing river plastic detection models!

Presentation of the principles of few-shot methods and the value they bring: analysis of an approach.

How to train an aerial object detection model using your custom dataset, annotated using kili technology, and model trained with YoloV5 from ultralytics.

Kili's annotation platform is designed to quickly get a production-ready dataset. Let's see how active learning is applied to object detection.

Kili Technology

In August, we had a community challenge on financial news annotation. Read on to see how it went and what our community members had to share about this experience.

Labeling geospatial data poses unique challenges due to complexity, variability, and geometric distortion. Manual labeling, automated labeling, and semi-automated labeling are common techniques used in the annotation process.