Retail computer vision · Image

Retail Shelf Intelligence

A store-shelf imagery concept for product detection, facings, availability, planogram compliance and price-tag OCR.

Catalogue statusIllustrative custom specification

Reference volumeIllustrative 120K-image specification

Delivery modelScoped pilot → versioned production

Designed for

Retail analytics and consumer-goods teams building shelf-monitoring and merchandising intelligence products.

Every specification is adapted to the buyer's model, production environment, data rights and measurable acceptance criteria.

Primary use cases

SKU and category detection
Facing and share-of-shelf measurement
Out-of-stock detection
Planogram-compliance analysis
Price-tag OCR

Annotation schema

Labels tied to model behaviour.

shelf region
product box or mask
SKU/category identifier
facing count
stock-gap region
price-tag region and text
image-quality attributes

Coverage design

Variation before volume.

Modern trade and traditional retail
Different shelf heights and layouts
Reflective packaging and occlusion
Crowded shelves and partial products
Multiple camera devices and viewing angles

Quality target

Target: ≥96% audited object-label acceptance with SKU-specific reporting

Targets are agreed during scoping and reported only after measurement on the applicable delivery.

Quality controls

SKU taxonomy calibration
Bounding-box and mask audits
Duplicate and near-duplicate detection
Store and category coverage checks
Low-confidence review queues

Delivery format

COCO JSON, CSV catalogue maps and JPEG media

Buyer-specific schema and storage requirements can be incorporated before production.

Example file package

01images/

02annotations.json

03sku_catalogue.csv

04store_split.csv

05dataset_card.md

06quality_report.pdf

Documentation included

Evidence travels with the files.

SKU and category taxonomy
Store and collection-context summary
Annotation guide and ambiguity rules
Coverage by retailer/category
Quality metrics and known blind spots

Known limitations

What buyers should understand.

SKU availability changes over time
Commercial image rights and store permissions must be scoped
Catalogue accuracy depends on buyer-provided product references

Build from this pattern

Turn your requirement into a pilot.

Share the model objective, target environment, approximate volume and delivery constraints. We will respond with the questions needed to scope a credible dataset.

Request this dataset pattern ↗