---
title: "Grocery Shelves  Dataset"
description: "5,000+ photos"
url: "https://unidata.pro/datasets/grocery-shelves/"
date_modified: "2025-10-11T19:13:19+03:00"
language: "en-US"
---
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.

## Dataset Structure

### The Numbers Section

**Numbered list:** - **Number:** 5,000+ — **Text:** photos

### Tooltips Section

**Tooltip items:**

- **Name:** Data annotation
- **Name:** Computer Vision
- **Name:** Retail Analytics
- **Name:** Object Detection
- **Name:** Machine learning

### Dataset Information

**Table with data:**

| 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. |

**Media Slider:**

- **Image in the slider:** ![Original image](https://unidata.pro/wp-content/uploads/2024/12/grocery-shelves-original-image.webp)
- **Image in the slider:** ![Labeling of the image](https://unidata.pro/wp-content/uploads/2024/12/grocery-shelves-labeling-of-the-image.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/u/0/folders/1fujuPX3EM0VBIKfs9J4YQY1QD3OwRvjH)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| File extension | PNG |
| Extension of labeling file | XML |

**Source and data collection methodology:** Source and collection methodology. Data was collected by UniData team by using the crowdsourcing service

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Retail Analytics & Inventory Management — **Title:** Automating Product Detection on Store Shelves — **Text:** 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.
- **Industry:** E-Commerce & Grocery Delivery Platforms — **Title:** Enhancing Visual Search and Product Matching — **Text:** 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.
- **Industry:** Artificial Intelligence & Computer Vision Research — **Title:** Training Deep Learning Models for Retail Object Recognition — **Text:** 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.
- **Industry:** Retail Operations & Merchandising — **Title:** Monitoring Planogram Compliance and Shelf Layouts — **Text:** 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.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What types of annotations are provided? — **Answer:** 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.
- **Question:** How is the data collected? — **Answer:** 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.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model: free dataset samples are offered for evaluation and testing, while full versions are available for purchase.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** 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.
- **Question:** How are Unidata datasets stored? — **Answer:** 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.
- **Question:** How long does it take to receive the dataset? — **Answer:** 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.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** 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.
- **Question:** How does annotated shelf data help improve product recognition models? — **Answer:** Annotated shelf data provides clear examples that allow AI models to learn product locations and visual characteristics. This improves the accuracy of detection and classification algorithms used in automated retail systems.

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