Insights, tips, and product updates

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.

Supervised learning is one the major types of Machine Learning algorithms mainly involved in Image Processing. It refers to the way algorithms are trained, with the guidance of a human teacher.

Image annotation plays an important role in training a machine to automatically assign relevant metadata information to a digital picture. This metadata often includes captions, keywords, location markers, or any combination of these details.

Text annotation is widely used in organisations to solve NLP tasks for machine learning models. Learn more about text annotation in machine learning and how to employ the tool for better productivity!

If you’ve ever worked on object detection, specifically using R-CNN and YOLO models, you may have come across the object detection metric named Mean Average Precision (mAP). Here's what you need to know about it.

Image Classification is one of the most important tasks in Computer Vision. It forms the basis for image recognition, making a successful image classification project is easy with the right process.

A concise explanation of the differences between ML training, validation and test sets | How to include enough data to train machine learning models.

The mastery of entity recognition and text classification is crucial for machine learning engineers in the field of natural language processing (NLP).

Discover the best free data labeling platforms for machine learning. Unveil hidden costs and understand the importance of paid vendor solutions.

Welcome to the exciting world of image recognition! To help you kickstarting your journey, here's a beginner-friendly tutorial on how to train your image recognition model.

Building a text classification pipeline for customer opinion analysis with Kili Technology and HuggingFace AutoTrain

A guide on how to ake the training process more productive by empowering your pipeline with Kili AutoML and Weights and Biases.

How to use Twitter APIs to collect tweets and build your dataset for NLP applications.