Mobiusi

Self-Service Checkout Product Recognition Image Dataset

#image classification #object detection #product recognition #retail industry #e-commerce platform #product recognition #self-service checkout
  • 500 records
  • 1.2G
  • JPG
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

With the rapid development of retail e-commerce, self-service checkout has become crucial in enhancing the shopping experience in retail stores. However, traditional checkout methods require manual intervention, resulting in relatively low efficiency and possibly long waiting times. Existing AI-based product recognition systems often suffer from data scarcity and low recognition accuracy. This dataset addresses the product image recognition problem by providing high-quality and diversified product images, effectively improving the recognition efficiency and accuracy of self-service checkout systems. The images in the dataset are captured using high-resolution cameras in simulated real supermarket or mall environments. For quality control, multiple rounds of annotation and consistency checks are employed, and a team of image experts reviews to ensure labeling accuracy. The annotation team consists of 10 professionals with imaging backgrounds. Data preprocessing includes image cropping, color correction, and background noise removal to ensure high data availability. Images are stored in JPG format and are categorized by product type.

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
product_categorystringThe category to which the identified product in the image belongs.
product_brandstringThe brand of the identified product in the image.
product_namestringThe name of the identified product in the image.
product_pricefloatThe price of the identified product in the image.
product_quantityintegerThe quantity of the same product identified in the image.
barcode_presentbooleanIndicates whether there is a recognizable barcode present in the image.
sku_numberstringThe SKU (Stock Keeping Unit) number of the identified product in the image.
expiration_datedateThe expiration date of the identified product in the image, if applicable.
package_typestringThe type of packaging of the identified product in the image (e.g., box, bag, etc.).

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the Self-Checkout Counter Item Recognition Image Dataset?
The Self-Checkout Counter Item Recognition Image Dataset is an image dataset focused on the e-commerce retail sector, used for training item recognition algorithms.
How can this dataset be applied in the retail industry?
This dataset can be used to improve the accuracy of item recognition on self-checkout machines, enhancing the efficiency and experience of self-service checkout for customers.
Why does item recognition algorithm training need this dataset?
Item recognition algorithms require a large amount of high-quality image data for training to accurately identify and distinguish different items, improving the accuracy and robustness of the algorithm.
What are the technical requirements for using the Self-Checkout Counter Item Recognition Image Dataset?
The technical requirements for using this dataset for training include knowledge and skills in image processing, deep learning frameworks, and algorithm development.
What are the potential benefits of this dataset?
Using this dataset can help retailers improve the accuracy of item recognition, reduce the need for manual intervention, thereby lowering operational costs and enhancing customer satisfaction.

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Cite this Work

@dataset{Mobiusi2026,
  title={Self-Service Checkout Product Recognition Image Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/03670dd37859aa480d94533a2d33bd2a},
  urldate={2026-02-04},
  keywords={self-service checkout, product recognition, retail e-commerce dataset, image recognition dataset},
  version={1.0}
}

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