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LABELBOX
Data labeling and annotation platform for AI and ML
ABOUT
Building high-quality training datasets for machine learning is time-consuming and error-prone, often requiring custom tooling for annotation, quality control, and data management across different data types. Labelbox solves this by providing a unified platform for annotating images, videos, text, and audio — with model-assisted labeling to reduce manual work, automated quality reviews, and seamless integration with ML training pipelines.
INSTALL
pip install labelboxINTEGRATION GUIDE
1. Image and video annotation: label bounding boxes, polygons, segmentation masks, keypoints, and classification tags for computer vision datasets
2. Text and NLP annotation: label named entities, relationships, sentiment, classification, and question-answer pairs for natural language models
3. Model-assisted labeling: use existing ML models to pre-label data, then have human annotators review and correct — reducing labeling time by 50-80%
4. Quality management: set up consensus-based labeling, review workflows, and automated quality metrics to ensure high annotation accuracy
5. Dataset management: organize, search, version, and export labeled data with flexible ontology management and cloud storage integrations
TAGS
data-labelingannotationcomputer-visionnlptraining-datadataset-managementdata-curation