Encord's collaborative annotation tools helps you orchestrate and customise labeling workflows combining in-house data labeling teams, external workforces, and models.
The label editor is the primary way to view, create and edit annotations in Encord. The editor supports various computer vision modalities and is designed with maximal configurability and scalability in mind.
The editor automatically adjusts the interface based on the data type tasked for labeling, and currently supports the following data types:
- Videos & image sequences
- DICOM (e.g., CT, MRI, X-ray, and mammograms)
- Geospatial (e.g., electro-optical, synthetic-aperture radar)
The editor currently supports the following annotation types across all modalities:
- Bounding box
- Custom object primitives
- Segmentation mask (via export)
- Frame classification (radio classification, checklist classification, free-form text input)
- Dynamic classification (video only - radio classification, checklist classification, free-form text input)
Classifications can be applied at the frame or image-level, as attributes in objects with localization properties, and as events in objects with localization and temporal properties (for videos & image sequences).