Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitating the creation and management of annotations.
Core features include:
- Support for common annotation tasks:
- Text classification
- Named entity recognition
- Tabular classification and regresion
- Images recognition with bounding boxes and polygons
- Log labeling
- Active learning with uncertainly sampling to query unlabeled data
- Project tracking with real time data aggregation and review process
- User management panel with role-based access control
- Data management
- Import in common data formats
- Export in ML friendly formats
- Data sharing through community datasets
- Swagger API for programmatic labeling, connecting to data pipelines and more
DAML project includes three components:
- annotation-app: Angular application for the UI
- annotation-service: Backend services built with Node & Express
- active-learning-service: Django application providing active learning api using modAL library for pool-based uncertainty sampling to rank the unlabelled data
For development environment and build configuration see build documentation
DAML project team welcomes contributions from the community. For more detailed information, see CONTRIBUTING.md.
Have a bug or a feature request? Please first read the issue guidelines and search for existing and closed issues. If your problem or idea is not addressed yet, please open a new issue.
Copyright 2019-2021 VMware, Inc. SPDX-License-Identifier: Apache-2.0.