Structured everyday object image collection format for object recognition, robot perception, classification, inventory AI, and visual grounding evaluation.
Everyday Object Photo Sets are built for teams evaluating how vision systems recognize common objects across angles, materials, lighting conditions, and capture setups. The format is designed around object instances, not loose photo uploads.
Each object entry can include multiple views captured under defined instructions: front, side, top, angled, close-up, scale reference, or in-context placement where scoped. Images are paired with object category labels, material metadata, view angle fields, resolution checks, annotation status, QA fields, and manifest records.
Structured collection
Everyday Object Photo Sets use object-based capture tasks.
Metadata depth
The dataset format supports object-level alignment across multiple views by linking all images of the same object through stable…
Delivery-ready package
Includes image quality, object visibility, view coverage reviews, plus dataset manifest, CSV catalog, consent status metadata, QA…
Request sample access
Share your object categories, view requirements, annotation needs, and evaluation goals. We’ll reply with a technical scope for the collection.
Object image files · Object IDs · Image dimensions · Optional scale reference fields · Object category labels
Object sub-type labels · Material metadata · View angle labels · Background type metadata · Lighting condition metadata
Resolution metadata · Capture context metadata · Optional bounding boxes · Optional segmentation masks · Optional object-state labels
Everyday Object Photo Sets use object-based capture tasks. Contributors photograph approved objects from defined angles under clear instructions for background, lighting, scale, and object visibility.
Image quality review · Object visibility review · View coverage review · Category label review · Annotation consistency review · Consent status review · Delivery approval
Image files · Object metadata CSV · View metadata CSV · Annotation files where scoped · Dataset manifest JSON · CSV file catalog · QA summary · Consent status metadata · Delivery notes
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