Image Annotation

Product Classification and Shelf Image Annotation for a Retail Client

Image

How do you annotate shelves packed with thousands of ever-changing products? We built a high-speed pipeline to handle real-time updates and ensure merchandising insights stay current.

Industry E-commerce and Retail
Timeline 4 months
Data 100,000 annotated images
Image
Industry E-commerce and Retail
Timeline 4 months
Data 100,000 annotated images

The Task

A retail client approached us with a clear goal:
Automate the process of monitoring grocery store shelves using neural networks.

They needed a dataset that would enable a machine learning model to detect and classify products on shelves in real time. The end use case?
– Measure the success of promotions
– Optimize shelf space
– Respond faster to stockouts

But there was a significant challenge:
Shelves were filled with a huge variety of products—different brands, categories, package designs, and frequent seasonal updates. Traditional annotation workflows weren’t going to cut it.

The Solution

  • 01

    Structuring the Work

    We split our team into two focused groups:

    • Product Research Team:
      This team created a taxonomy of product categories. They studied the client’s inventory, researched visual differences between product types, and developed detailed classification criteria for annotators.
    • Annotation Team:
      Using these guidelines, annotators worked on labeling every image with high precision, tagging product types, positions on the shelf, and packaging variations.
  • 02

    Tooling and Workflow Setup

    • We used a combination of internal QA dashboards and custom labeling tools to track accuracy.
    • A feedback loop was built in—researchers could refine guidelines based on edge cases found by annotators.
    • Weekly calibration sessions ensured that annotators and researchers were always aligned.
  • 03

    Quality Assurance

    • A dual-pass review process was implemented: all images were reviewed by a second annotator.
    • Random samples were escalated to experts for manual audit.
    • Discrepancies were analyzed to refine both training and guidelines.

The Result

  • 40% Cost Reduction:
    By streamlining roles and using task specialization, we lowered total project costs significantly.

  • High-Precision Dataset:
    The annotated images provided clean, structured training data for the client’s neural network, supporting accurate real-time shelf analytics.

  • Better Business Insights:
    The client could now evaluate promotional campaign results in real time, detect planogram violations, and improve in-store execution.

Similar Cases

  • Image
    Document Annotation Text Annotation Text Labeling

    Legal Document Annotation

    From contracts to inheritance certificates, we annotated 6,000+ legal documents with high precision and custom validation logic.

    Lean more
  • Image
    Image Annotation

    Pose Estimation for Proctoring

    How do you teach AI to recognize when a student is cheating during an exam? By accurately annotating 6000 images of real exam scenarios — and that’s exactly what we did.

    Lean more
  • Image

    Advanced Message Filtering for Platform Safety

    We annotated and validated thousands of chat messages to train an AI model that now filters unsafe, abusive, or inappropriate content while keeping conversations natural and fast.

    Lean more
  • Image
    Image Annotation

    Optimizing Harvest Efficiency

    Our custom dataset powered the transition from manual picking to AI-assisted harvesting — optimizing yield through data-driven ripeness detection.

    Lean more
  • Image
    Audio Labeling services for ml Audio Transcription

    Banking Call Categorization

    Fast-tracked annotation of 363,000 banking calls with strict privacy — boosting NLP automation for debit, credit, and deposit queries.

    Lean more

Ready to get started?

Tell us what you need — we’ll reply within 24h with a free estimate

    What service are you looking for? *
    What service are you looking for?
    Data Labeling
    Data Collection
    Ready-made Datasets
    Human Moderation
    Medicine
    Other (please describe below)
    What's your budget range? *
    What's your budget range?
    < $1,000
    $1,000 – $5,000
    $5,000 – $10,000
    $10,000 – $50,000
    $50,000+
    Not sure yet
    Where did you hear about Unidata? *
    Where did you hear about Unidata?
    Head of Client Success
    Andrew
    Head of Client Success

    — I'll guide you through every step, from your first
    message to full project delivery

    Thank you for your
    message

    It has been successfully sent!

    This website uses cookies to enhance your experience, analyze traffic, and deliver personalized content and ads. By clicking "Accept", you consent to the use of cookies, as described in our Cookie Policy. Please choose your cookie preference.