Case Study: Floy accelerates annotation and model development with Encord

Annotating and Reviewing Medical Imaging Data with Encord

Floy, a sports analytics company, needed to scale its video data labeling to train models for identifying key events in sports matches. Its in-house annotation tool was too slow and inflexible, restricting the ML team's ability to develop new features and models efficiently. It turned to Encord for a rapid and more adaptable annotation solution.

Encord provided a platform with fast object detection features, allowing the team to label frames much more quickly. The solution included API access to import existing datasets and a user-friendly interface for multiple annotators. This enabled Floy's ML team to experiment with new ontologies in minutes instead of months, significantly accelerating their model development and product innovation.

Floy

Hamza Guzel

Radiologist