Image Annotation

Semantic Segmentation for Interior Design: A Complex Multiclass Annotation Project

Image

How do you segment every single object in a cluttered interior photo — 30+ classes per image? We designed a multi-step annotation pipeline to handle complexity without losing precision.

Industry E-commerce and Retail
Timeline 6 weeks
Data 1,000 high-resolution annotated images
30+ object classes per image
Image
Industry E-commerce and Retail
Timeline 6 weeks
Data 1,000 high-resolution annotated images
30+ object classes per image

The Task

A design-tech company approached us with an ambitious project. Their model needed pixel-perfect semantic segmentation of interior photos—for every object in view.

We had previously collaborated on a simpler task involving just floor and wall segmentation. This new project, however, scaled in complexity. Now, each photo had to be fully segmented by class, covering over 30 categories—furniture, fixtures, decor, appliances, and more.

The accuracy bar was set high. Precise labeling and consistent separation of foreground/background layers were critical. There was no room for ambiguity or missed elements.

The Solution

  • 01

    Preparation and Annotator Training

    • Custom Entry Test: We developed a screening test to ensure annotators could differentiate between visually similar classes like armchairs vs. sofas, or shelves vs. cabinets.
    • Structured Training Program: Annotators went through video-based training with examples of correct and incorrect segmentation.
    • Error Feedback Loop: We set up a fast feedback system with video reviews of common errors, helping annotators improve rapidly.
  • 02

    Annotation Workflow

    • Per-Object Segmentation: Every object on each photo was segmented individually and labeled according to the class hierarchy.
    • Foreground–Background Separation: We created specific guidelines for annotating layered objects (e.g., a vase on a table in front of a wall).
    • Consistency Protocols: The team followed a shared labeling structure to maintain consistency across the dataset.
  • 03

    Quality Assurance and Validation

    • Layered Review: Each annotated image underwent a two-step validation—initial QA check, followed by expert review.
    • Systemic Feedback: Repeated errors were flagged and addressed through daily syncs between annotators and validators.
    • Hierarchy Tweaks: During the mid-project review, the client requested minor adjustments to layer priorities (foreground/background). We implemented changes without delaying delivery.

The Result

  • 100% of data delivered on time with all requested objects correctly segmented and classified.

  • Consistent precision across the dataset—especially in challenging areas like overlapping objects and shadow boundaries.

  • Positive client feedback highlighted the clarity of object boundaries and the low correction rate.

Similar Cases

  • 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
  • Image
    Image Annotation

    Ore Annotation for a Mining Company

    We helped a mining company quickly train a model to detect ore granularity and oversized fragments directly on the conveyor belt—cutting processing delays and freeing up internal resources.

    Lean more
  • Image
    Data Collection

    Weapon Detection on the Streets

    From zero to 99% model accuracy in 28 days: we sourced, staged, and annotated video footage for urban weapon detection systems.

    Lean more
  • Image
    Data Collection

    Audio Dataset of Children’s Laughter and Crying

    Unidata collected 750+ unique audio samples of children’s emotional expressions — enabling emotion recognition in family-focused apps.

    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

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!

    We use cookies to enhance your experience, personalize content, ads, and analyze traffic. By clicking 'Accept All', you agree to our Cookie Policy.