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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.

A simple to use algorithm to automatically detect labeling errors with the help of Cleanlab for image classification projects in Kili.

Document annotation is one of the key bases of training data, significantly impacting the efficiency of machine learning and natural language processing applications. Deep dive into the process of document annotation and learn why it's important for you to choose a good annotation tool.

When it comes to collecting and analyzing data in images and videos, one of the most efficient tools is the bounding box. Focus on these helpful imaginary boxes.

7 ready-to-go steps to improve your data annotation project management get excellent-quality training data, and (finally) performing ML models.

What is unstructured document layout analysis? How unstructured document impact the training process of your ML model? Here are your answers.

Learn how businesses use intelligent data extraction to classify, sort, and process data at scale with greater speed and accuracy.

Data annotation for machine learning should not be underestimated.