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Grocery Shelves Dataset

It is a labeled supermarket shelves dataset containing over 5,000 high-quality shelf images from grocery stores, designed for product detection, object recognition, and image classification tasks in computer vision models, with annotations for facing, flipped, and occluded grocery items to support retail automation and grocery delivery applications.

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  • photos
    5,000+
  • Data annotation
  • Computer Vision
  • Retail Analytics
  • Object Detection
  • Machine learning

It is a labeled supermarket shelves dataset containing over 5,000 high-quality shelf images from grocery stores, designed for product detection, object recognition, and image classification tasks in computer vision models, with annotations for facing, flipped, and occluded grocery items to support retail automation and grocery delivery applications.

Get in touch Download sample
  • Data annotation
  • Computer Vision
  • Retail Analytics
  • Object Detection
  • Machine learning
  • photos
    5,000+

Dataset Info

Characteristic Data
Description Grocery shelves images with labeling for detection tasks
Data types Image
Tasks Detection, Classification
Total number of photos 5,000+
Attribute of the product Facing, flipped, occluded.
Original image
Labeling of the image
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Technical
Characteristics

Characteristic Data
File extension png
Extension of labeling file xml
Source and collection methodology. Data was collected by UniData team by using the crowdsourcing service

Dataset Use Cases

  • Retail Analytics & Inventory Management

    Automating Product Detection on Store Shelves

    Grocery Shelves Dataset helps retailers develop computer vision systems for object detection and product recognition on grocery shelves. By training models with shelf images containing various grocery items, businesses can automate stock monitoring, identify missing products, and improve inventory accuracy across supermarket shelves and retail outlets.

  • E-Commerce & Grocery Delivery Platforms

    Enhancing Visual Search and Product Matching

    This product detection dataset supports grocery delivery and online retail platforms in building image classification models that recognize and categorize items from uploaded photos. The dataset consists of diverse store shelves and grocery market setups, helping improve real-time data analysis for better catalog matching and visual search accuracy.

  • Artificial Intelligence & Computer Vision Research

    Training Deep Learning Models for Retail Object Recognition

    Researchers use such datasets to train deep learning and object recognition algorithms capable of detecting multiple grocery products in complex environments. The dataset provides high-quality annotated shelf images from real grocery stores, allowing scientists to refine trained models for scalable retail automation.

  • Retail Operations & Merchandising

    Monitoring Planogram Compliance and Shelf Layouts

    This shelves dataset enables retailers to verify planogram compliance and optimize retail shelves layout through automated analysis. Using computer vision techniques, models trained on the dataset can identify misplaced items, analyze grocery shelves organization, and generate insights that enhance store performance and visual merchandising efficiency.

FAQs

What is included in this dataset?
The dataset contains over 5,000 high-resolution images of grocery store and supermarket shelves. Each image is labeled with detailed metadata describing product positioning, including whether items are facing, flipped, or occluded, to enhance model training accuracy.
What types of annotations are provided?
Each image includes XML-based annotations that define object boundaries and product attributes. The annotations are designed for object detection and classification models, making the dataset suitable for both training and evaluation of retail AI systems.
How is the data collected?
Data for Grocery Shelves Dataset was collected by the Unidata team using crowdsourcing services. Contributors captured real-world store shelf images under varying lighting and environmental conditions, ensuring a broad representation of grocery product layouts and real-time retail scenarios.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free dataset samples are offered for evaluation and testing, while full versions are available for purchase.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. Unidata datasets are curated in compliance with GDPR and other applicable privacy laws. All data is collected from legally permissible sources, ensuring ethical and lawful handling of visual and contextual information.
How are Unidata datasets stored?
Unidata securely stores all datasets on AWS cloud infrastructure, ensuring scalability, high availability, and secure access. These storage practices comply with ISO 27001 and ISO 27701 standards, providing a reliable and privacy-focused environment for handling image datasets.
How long does it take to receive the dataset?
After submitting a request, Unidata will contact you to review dataset details and finalize the documentation. Following agreement and payment, Grocery Shelves Dataset will be delivered securely within 3–10 business days.
Is this a real-world dataset or synthetic data?
This is a real-world dataset. All grocery shelf images were captured from actual supermarkets and retail stores, reflecting real-world conditions such as lighting variations, occlusions, and shelf arrangements - essential for training reliable computer vision systems.
Still have questions about using Unidata datasets? Read our user-guides

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