Encord Active

Test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data. Evaluate and validate your production AI models with new data to surface, curate, and prioritize the most valuable data for continious model improvement. Encord Active evaluates models on visual and multimodal data, detects issues like label errors and weak classes, curates priority samples, and runs active learning to improve production AI performance. The core toolkit is open source on GitHub under Apache 2.0, with additional enterprise features and scalability in the Encord platform version. It handles images, videos, and multimodal visual data, with extensions to formats like DICOM in the full Encord ecosystem for medical or specialized use. It automatically identifies potential mislabels using vector embeddings, quality metrics, and model prediction confidence to highlight problematic samples. It connects tightly with Encord Annotate for active learning loops, and works with exported predictions from other annotation platforms too. It includes acquisition functions to rank and prioritize uncertain or impactful data for labeling, helping focus effort where it boosts models most.