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.

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