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Annotation 101
Guides, comparisons and field notes on data annotation for computer vision and audio AI.
What is Semantic Segmentation & How Does it Work?
Find out what semantic segmentation is, how it works, and why it’s a critical technique for computer vision applications.
Consensus in Data Labeling: Benefits, Tradeoffs, and Common Mistakes
This article covers what consensus actually does, where it pays off, where it quietly wastes money, and the mistakes that show up again and again in real pipelines, with input from a core CVAT developer on what teams get wrong.
How to Write Annotation Guidelines That Actually Work in Production
Most annotation guidelines fail in production, not because of bad annotators, but because of vague specs. Here’s how to write guidelines that actually hold up at scale.
How to Choose the Right Dataset Format for Your ML Project
Discover how to select the optimal dataset format for your ML project. Evaluate the technical trade-offs of COCO, YOLO, Pascal VOC, and specialized schemas.
Automated Data Labeling: What It Is and When to Use It
Manual labeling slows down ML pipelines. This guide breaks down four ways to automate annotation in CVAT so you can label faster without sacrificing quality.
Ground Truth Data Explained: How to Build and Maintain a Quality Benchmark
Learn what ground truth data is, why it matters for ML model accuracy, and how to build and maintain a reliable quality benchmark for your annotation workflow.
Dataset Preparation for ML: What to Do Before You Start Labeling
Master dataset preparation for machine learning with our guide on how to define requirements, source data, clean datasets, and create labeling guidelines.
Point Cloud Annotation: A Complete Guide to 3D Data Labeling
3D point cloud annotation explained. Our in-depth article walks through applications in computer vision, and how to efficiently label 3D data.
Annotation Quality Assurance: A Multi-Layered Approach to Reliable Labeled Data
Annotation QA best practices for computer vision projects. Learn how different QA layers work together to ensure high-confidence, production-ready labeled data.
The Ultimate Guide to Video Annotation for Computer Vision (2026)
Learn how to annotate videos for computer vision applications. Explore challenges, best practices, and applications in this video annotation guide.
Image Annotation Explained: What It Is & How to Do It Right
What is image annotation, when is it used, and how does it work? Here’s a detailed outline of everything you need to know with some helpful examples.
Audio Annotation Guide: An Overview of Tools, Types, and Use Cases
2026 Audio annotation guide by CVAT.ai. Our helpful guide gives you an overview of tools, applications, data types, and use cases.
Is Your Training Data Trustworthy? How to Use Precision & Recall for Annotation QA
Why precision, recall, & accuracy are key annotation quality metrics. Discover how QA helps evaluate and improve the quality of labeled data in AI projects.
Why Data-Centric AI Leads to Better Results Than Model-Centric AI
Why does data-centric AI lead to better results than model-centric AI? It all comes down to label quality. Read this article to see the differences.