Cabinet

Data annotation for retail and e-commerce

We prepare images, video and catalogs for AI training — product recognition, shelf analytics, classification, segmentation, OCR and visual search.

From a test sample to a production-ready dataset.

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Product image annotation for retail and e-commerce

Data quality defines AI accuracy in retail

Problem

  • confuses similar products;
  • misses packaging from different angles;
  • loses products under partial occlusion;
  • fails when packaging design changes;
  • misreads whether a product is on the shelf;
  • struggles with a large number of SKUs;
  • returns irrelevant visual-search results.

Solution

  • mark products on images;
  • assign categories and attributes;
  • annotate packaging and separate elements;
  • segment objects;
  • recognize text and price tags;
  • handle hard and borderline cases;
  • check annotation quality before the dataset is delivered.

What is data annotation for retail and e-commerce?

Data annotation for retail and e-commerce is the preparation of images, video, text and product catalogs for training artificial intelligence models.

Depending on the task, annotators mark products in photos, assign categories and attributes, segment individual objects, recognize text on packaging and price tags, or link images to product cards.

These datasets automate shelf analytics, product recognition, assortment control, visual search, product classification and other AI services.

Retail has a large number of similar objects. One category can include hundreds or thousands of SKUs that differ by color, pack size, model or design details.

Training therefore depends not only on volume, but on shared annotation rules, a clear class structure and stable labeling quality.

Annotation types for retail and e-commerce

Product detection

Bounding boxes for individual products, packs, price tags, logos and other objects. Used for product recognition and automatic shelf-image analysis.

Product segmentation

Pixel-perfect or polygon annotation that separates a product from the background. Used in visual search, fashion, catalog generation and computer vision systems.

Product classification

Assigning categories to images:

  • product type;
  • brand;
  • model;
  • category;
  • subcategory;
  • purpose.

Attribute labeling

Additional characteristics:

  • color;
  • size;
  • material;
  • pack type;
  • style;
  • volume;
  • product condition;
  • other parameters.

OCR and text labeling

Highlighting and recognition of:

  • price tags;
  • barcodes;
  • labels;
  • names;
  • SKUs;
  • product specifications.

Shelf annotation

Products in store photos and video:

  • SKU presence;
  • product position;
  • facing count;
  • empty spaces;
  • price tags;
  • competitor products.

Annotation examples

Different annotation types for computer vision tasks

ML Pipeline

Full data preparation cycle from raw data to model-ready output

1
Data
Collect and prepare source audio data.
Order data prep
2
Annotation
Annotation aligned with task requirements.
Order annotation
3
Quality Control
Multi-step consistency and QA checks.
Check quality
4
Dataset
Final dataset in required format.
Get dataset
5
Model Training
Ready for ML/AI production pipelines.

Quality control

How we keep product annotation stable

Retail errors often come from visually similar products, a complex category tree and a large number of attributes.

We lock classification rules before scaling and describe borderline cases separately.

Annotation passes several review levels, with extra attention to class correctness, completeness and consistency across annotators.

01
Classes and SKUs
We check that a product matches the specified category, brand, SKU or attribute set.
02
Hard cases
Similar packs, partial occlusions, poor photos and new product variants are reviewed separately.
03
Shared rules
The whole team uses the same guidelines so the dataset stays consistent as it scales.

Where retail and e-commerce annotation is used

Product recognitionModels that identify a product or its category from a photo.
Shelf analyticsPresence, facing counts, empty spaces and planogram compliance.
Visual searchFinding similar products from an image instead of a text query.
Catalog automationImage classification, category detection and automatic product attributes.
Fashion e-commerceClothing, footwear and accessories, item segmentation and attributes.
OCR and price tagsText, price, article numbers and other information from packs and tags.
Assortment controlWhether specific products and categories are present in a store.
PersonalizationStructured data for search and recommendation models.

AI for shelf analysis

Computer vision systems can automatically analyze photos and video from stores.

Training those models requires each product and its position on the shelf to be marked correctly.

US-DATA can annotate:

This annotation is used in merchandising control, automatic store audits and product-availability analysis.

US-DATA advantages

Large catalogs

Projects with many categories, products and attributes.

Computer Vision and ML

We consider how data structure will affect model training, not only the formal labels.

Flexible class structure

We follow your taxonomy or help formalize categories and borderline cases before launch.

Several data types

Images, video, text, product cards and other data can be handled in one project.

Scaling

Start with a small pilot and increase volume after quality is confirmed.

Quality control

Multi-step review keeps the same rules across the project.

Result for your ML project

1

More accurate product recognition

2

Correct category and SKU classification

3

Better visual search

4

Stable work with large catalogs

5

A dataset ready for training and testing

Data security

Enterprise-grade data protection
Security & Compliance
NDA before the project starts.
Access control for source data.
Role separation for staff.
Secure file transfer and storage.
Compliance with the customer's local law and applicable standards.
Work only in the agreed infrastructure.

Pricing

Expandable sections with indicative cost tables.

Calculate annotation cost

Choose parameters and get instant estimate

Segmentation
Bounding Box
OCR
Classification
1,000 images

Our offer

Price per 1,000 units$150
Number of images1,000
Number of classes1
ComplexityLow
Project cost$150*
Detailed termsCreate request

* This estimate is not a public offer. Final cost is determined after technical analysis and data review.

News

Latest materials on data annotation and machine learning

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Need a dataset for retail or e-commerce?

Describe the task and share a data sample — we will estimate complexity, suggest an annotation format and prepare a test batch.

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Data annotation for retail and e-commerce

Modern retail and e-commerce rely on artificial intelligence and computer vision. AI models recognize products, analyze shelf layout, automate catalogs, run visual search and extract information from packaging and price tags. Those systems need carefully prepared and annotated data.

US-DATA provides image annotation and other data labeling for retail and e-commerce. We can mark individual products with bounding boxes, polygons and segmentation masks, classify images, assign product attributes and run OCR on packaging and price tags.

One important area is datasets for recognizing products on retail shelves. A single image can contain dozens or hundreds of objects, many of them visually similar. Annotation must set product boundaries, category, brand or SKU consistently, including borderline cases across the whole volume. Precise outlines use image segmentation, and finding a product in the frame uses object detection.

In e-commerce, annotated data supports visual search, catalog building, image classification and product characteristics. This is especially relevant for fashion, marketplaces, FMCG and companies with large catalogs.

US-DATA can join at any stage: a test batch, guideline design, an existing data pool, or scaling a process that already works. Multi-step review and shared rules keep the dataset consistent as volume grows. When a catalog needs separate image classification, class rules are fixed before scaling.

If your project needs retail data annotation, a product dataset, on-shelf recognition, catalog classification or data for an e-commerce AI model, US-DATA will prepare data in the agreed format for training, testing and further use.