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Commercial
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.
-
- photos
- 5,000+
- Data annotation
- Computer Vision
- Retail Analytics
- Object Detection
- Machine learning
- 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.
- 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. |
Technical
Characteristics
| Characteristic | Data |
| File extension | PNG |
| Extension of labeling file | XML |
Dataset Use Cases
FAQs
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.
How does annotated shelf data help improve product recognition models?
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.
Still have questions about using Unidata datasets?
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