dataannotation

Data annotation is the process of labeling or tagging raw data to make it usable for training and evaluating supervised machine learning models. By providing ground truth or target labels, annotated data enables algorithms to learn input–output mappings and to be assessed against objective metrics. Annotation is a foundational step in data preparation that can influence model performance, bias, and generalization.

Common modalities include image and video, text, audio, and 3D sensor data. In computer vision, annotations range from image-level labels to bounding boxes, polygons for semantic and instance segmentation, and keypoints for pose estimation. In natural language processing, annotations cover named entities, sentiment, part-of-speech tagging, and relation extraction. In audio, transcripts and speaker labels are common, as are event tagging.

Typical workflow starts with labeling guidelines, then data collection and task assignment to annotators, often via crowdsourcing or dedicated teams. After labeling, quality control includes double annotation, adjudication, and measuring inter-annotator agreement. Gold standards and pilot tasks help calibrate performance before scaling up. Data governance concerns such as privacy, consent, and handling sensitive information are important, as is versioning and auditability for reproducibility.

Applications span computer vision, natural language processing, speech recognition, and autonomous systems. The fidelity of annotations affects model bias, robustness, and safety. As data needs grow, teams balance speed, cost, and quality through refined guidelines, tooling, and workflow strategies.