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Guides, comparisons and field notes on data annotation for computer vision and audio AI.
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.
Building a Robust Annotation Infrastructure: A Guide to CVAT Online and Enterprise Platforms
This guide will help you understand the key differences between CVAT Online and Enterprise, and choose the right annotation platform for your use case.
How Data and Annotation Quality Improves ML Model Training
Wondering how data and annotation quality improves ML model training? This article breaks down why accurate data is so critical.
What ML & AI Teams Should Learn from the Scale AI Data Leak
The Scale AI data leak is a significant moment for AI/ML teams. Here’s what you need to know and steps to protect your data.
CVAT Celebrates 14K Stars on GitHub and Its Three-Year Anniversary!
From Intel prototype to global data annotation platform — explore 16 milestones that shaped CVAT’s journey to 14,000 GitHub stars in just 3 years.