Inventory Software Retail Images
AI-powered retail management solutions
The Inventory Software Retail Images dataset contains 1 million authentic, in-store photos captured across major grocery retailers in the US, UK, Ireland, the Netherlands, Denmark and Germany. The collection spans displays, planograms, shelf layouts, product adjacencies, packaging and seasonal or event-based merchandising. Each JPEG image includes rich metadata, including folder hierarchy (Year, Month, Retailer, Event, Category), EXIF camera details and more. This large-scale dataset is ideal for training computer vision models for retail analytics, product detection and merchandising optimisation.
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Use this dataset to:
Build Computer Vision Foundational Models and Fine-tune Vision AI: Train and optimize complete retail computer vision systems that cover every stage of in store analysis. Enhance product recognition, price tag reading, planogram verification, merchandising evaluation and support for autonomous store operations, all powered by large-scale, real-world imagery from global grocery environments.
AI use cases:
Shelf Product Detection and Recognition: Train models to identify SKUs, detect product facings, measure shelf share and understand product adjacencies using diverse, real-world merchandising conditions.
Generative Retail Simulation and Augmentation: Fine-tune generative vision models to create synthetic shelves, displays, planograms or promotional concepts grounded in real retail arrangements.
Technical Specifications
- Type: Image
- Quantity: 1,000,000
- Data Type: Real in-store images
- File Type: JPEG
- Metadata: Folder hierarchy (Year > Month > Retailer > Event > Category); EXIF metadata (camera, timestamp); SHA checksum (file integrity); event label (e.g. “Christmas 2023 – Bakery” for seasonal events); and optional text captions and tags
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License Information
This dataset is covered by our standard Data license agreement. The license agreement is perpetual and allows for the commercialization of all models built on the data.
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