Bounding box annotation for logistics involves marking regions of interest in images or video data, such as vehicles, cargo, or delivery items. This technique is crucial for training AI models to track shipments, optimize routes, and improve operational efficiency within the logistics and transportation industries.
Polygon for logistics involves precisely labeling and separating different elements within images, such as parcels, vehicles, or inventory. This technique helps AI models understand and analyze complex environments, enhancing tracking, sorting, and route optimization in the logistics and supply chain sectors.
Unitlab supports warehouse images, package and vehicle video, documents, text, audio, geospatial imagery, and connected operational records.
Teams can use detection boxes, segmentation, polygons, keypoints, classifications, properties, relations, OCR regions, temporal labels, and object tracks.
Yes. Frame-accurate video annotation, object tracking, keyframes, temporal ranges, and event labels support warehouse, yard, and fleet footage.
Yes. PDF and document workflows support OCR fields, layout regions, tables, text entities, classifications, and relations for logistics document AI.
Image, video, document, text, geospatial, and related operational data can share ontologies, properties, review steps, and dataset versions.
Teams can search, filter, deduplicate, balance, version, and route selected logistics data into annotation and QA.
Yes. Bring Your Own Model workflows let teams use domain models for pre-labeling and review predictions inside governed workflows.
Shared instructions, roles, issues, rework, approvals, and annotation history help standardize quality across distributed teams.
Unitlab supports large datasets, long videos, collaborative projects, dataset versions, and programmatic upload and export.
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