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Fine-tuning

Our models, fine-tuned on your drawings

A base model is one we have already trained on floor plans: it knows what a wall, a room, a door and a door tag look like in general. Fine-tuning teaches it your legend, your abbreviations and your sheet quality, so a project starts weeks in rather than at zero. Every client gets their own tuned model — we never hand over the untuned one and call it done.
  • You set the accuracy target
  • You own the trained weights
  • Measured against your manual results
Comparison of model families applied to floor plan analysis, with detection, segmentation and OCR components shown against a drawing
Different parts of the plan need different model families. Each is tuned separately.

Why tuning is not optional

A drawing set is a private language

Architectural drafting has conventions, not standards. Two practices in the same city will draw the same cased opening differently, abbreviate the same material differently, and disagree about where an open-plan room stops.
  • Your legend

    The symbols your office uses, which no general model has ever seen.
  • Your sheet stock

    Vector exports, 300 DPI scans, phone photographs of a print — whichever you actually get.
  • Your fields

    The handful of values you use, tuned properly, rather than fifty measured loosely.
  • Your threshold

    What counts as good enough for the decision the output feeds into.
TopicOur models, before tuningTuned on your drawings
What it is forJudging whether a job is feasible at allRunning as part of a workflow
Your symbolsRecognised only where they resemble common onesTrained on your legend, from labelled examples
AccuracyGenerally above 80% on typical plansMeasured against a target you set, on your own sheets
Awkward casesHandled inconsistentlyYour convention applied the same way every time

What a project looks like

From your first sheet to a model you own

  1. Step 1: Scope on real drawings

    about 2 business days

    Send a sheet, ideally an awkward one. We run our models on it and tell you what already works, what needs tuning, and what is not worth automating at all.
    • Free
    • No call required
    • NDA on request
  2. Step 2: Tune and measure

    4–6 weeks

    We label a representative sample of your sheets, tune the relevant stages, and score the result on a held-out set against your own manual work.
    • Target agreed before we start
    • Scored on fields you nominate
  3. Step 3: Run it, and keep it current

    Ongoing

    Deployed where the work happens, with corrections from review feeding the next round of training as your drawing mix changes.
    • You own the weights
    • Retrained as drawings change

What we need from you

Less than teams expect

The most common worry is that fine-tuning needs a labelled dataset you do not have. It does not — we do the labelling. What we need is the raw material and a clear idea of what matters.
  • A representative sample of your drawings, including the bad ones
  • The fields you actually use, not every field imaginable
  • Any existing manual results for those sheets — they become the ground truth
  • Your conventions, for the cases where the drawing is genuinely ambiguous

Send the awkward sheets, not the clean ones. A tuned model is only as good as the range it was trained on, and a sample of your best drawings produces a model that fails on a Wednesday afternoon when a bad scan arrives.

What tuning will not fix

Some things are not a model problem

Worth saying before a project starts rather than six weeks in.
  • Information that is not on the drawing

    If a dimension was never drawn, no amount of training will recover it. It can be inferred and flagged as inferred; it cannot be known.
  • Drawings too degraded to read

    There is a floor below which a scan carries no recoverable detail. We would rather identify that in the free analysis than bill for discovering it.
  • A decision nobody has made

    Where an open-plan space ends is a convention, not a fact. Until your team agrees one, no model can apply it consistently.

FAQ

Questions, answered

What teams ask before committing to a build.

First, though — send us one sheet.

  • For a symbol class, typically tens of labelled instances rather than thousands — template matching gets a useful way there from a single example, and tuning is what makes it reliable. For a full pipeline across a varied drawing set, a representative sample across the styles you receive matters far more than raw volume.

Get in touch

Tell us what is on your drawings

Describe the sheets you get and what you need off them. We will tell you what our models already handle, what needs fine-tuning on your drawings, and what is not worth automating at all.

Reply time
Within 1 business day

Would rather show than tell? Send one sheet for a free analysis

We reply within 1 business day.

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