Stop guessing what’s wrong with your data

Your vision model is only as good as your data. But without a data-centric approach you’re likely to overlook critical data issues even while your datasets are sabotaging model performance.

Want to master a data-centric approach? Find out how in this free hands-on Coursera course.

Become a data superhero

The next revolution in computer vision isn’t about bigger models or better architectures. It’s about something far more fundamental: data quality. Yet here’s the dirty little secret about visual data quality: most teams are wasting precious time fine-tuning hyperparameters and tweaking architectures when it’s their data that is the problem.

We’ve partnered with Coursera and UC Davis to create a hands-on course about a systematic, data-centric approach that transforms how you use data to evaluate and improve your models. Gain in-depth knowledge and practical skills on how to:

Build systematic processes for data completeness and consistency checks
Implement robust data labeling workflows with built-in quality control
Create robust data pre-processing pipelines

Take the next step

Want to put a data-centric approach to visual AI to use? FiftyOne was designed from the start to help you put your data at the center of your development. Powered by open source, FiftyOne helps you create high-quality datasets and models easily and reliably.

Understand and analyze visual data

Easily explore data of any scale and modality to discover hidden patterns and distributions and gain a richer understanding of your data.

Refine and curate high-quality datasets

Systematically identify data inaccuracies, annotation errors, and shortcomings to create high-quality datasets while reducing inefficiencies and costs.

Evaluate and improve models

Experiment with models, uncover failure modes and find data gaps as you test and refine models to maximize performance and accuracy.

Fuel high-impact, efficient collaboration

Collaborate on datasets and models. Share datasets, request reviews, or tag mistakes to improve the quality and efficiency of development.