Jonathandav/facade-styles

Facade — synthetic architectural style corpus

1,000 generated reference plates across 20 architectural styles, with structured attribute labels, generated style readings, and a precomputed retrieval index.

Built for a visual-similarity search task: photograph a building, retrieve the closest reference plates, get a reading of the style. Live app: Facade

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What this is

Every plate is generated from a sampled attribute specification — style, massing, material, window rhythm, roofline, palette — plus controlled nuisance factors (viewing angle, crop, light, condition). Nothing is left to the generator's discretion.

That design buys three things:

1. Ground truth for free. Every image inherits its labels from the specification, so retrieval evaluation needs no manual annotation.
2. Measurable instruction compliance. Because each style declares an expected saturation band and dominant edge orientation, whether the generator obeyed is a computed quantity rather than an opinion.
3. Sliceable nuisance factors. Angle, crop and light are controlled, so accuracy can be reported conditioned on them instead of averaged over them.

What this is not

The corpus depicts generic facades in a style. No plate represents a real building and none is attributable to any architect. Style labels describe the generation specification, not an art-historical judgement about any structure in the world.

Intended use is visual-similarity search. This is not an authoritative identification tool and carries no claim about any building's date, architect, or heritage status.

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Files

path	contents
plates/	1,000 reference images, 512 px PNG, <style_id>-<nnn>.png
plate_manifest.parquet	per-plate labels, prompts, seeds and generated readings
style_seed.csv	the 20 style definitions and their expected measurements
index_embeddings.npy	precomputed image embeddings for the shipped model
index_plate_ids.csv	row order for the embedding matrix
index_model.txt	which model produced the index
eda_summary.json	measured compliance and signal statistics
generation_log.csv	per-attempt generation record (acceptance, rejection reasons)
notebooks/	corpus generation, EDA, model comparison, publishing

Loading

import numpy as np, pandas as pd
from huggingface_hub import hf_hub_download

REPO = "Jonathandav/facade-styles"
get  = lambda f: hf_hub_download(REPO, f, repo_type="dataset")

E        = np.load(get("index_embeddings.npy"))          # (1000, d)
ids      = pd.read_csv(get("index_plate_ids.csv"))       # row order
manifest = pd.read_parquet(get("plate_manifest.parquet"))
styles   = pd.read_csv(get("style_seed.csv"))

Plates resolve directly: https://huggingface.co/datasets/Jonathandav/facade-styles/resolve/main/plates/ST01-000.png

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The 20 styles

Bauhaus International · Brutalist · Art Deco · Ottoman Revival · Mandate Eclectic · Neoclassical · Gothic Revival · Venetian Gothic · Georgian · Victorian Terrace · Mid-century Modern · Postmodern · Deconstructivist · Contemporary Curtain Wall · Mediterranean Vernacular · Adobe Pueblo · Soviet Constructivist · Scandinavian Functionalist · Industrial Warehouse · Japanese Metabolist

Fifty plates each. Anything outside these twenty is still forced onto the nearest of them — worth knowing before trusting a result.

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How it was built

stage	detail
image model	SDXL-Turbo, 6 steps, guidance_scale=3.0, 512 px
text model	Qwen2.5-1.5B-Instruct, for the style readings
acceptance	every plate measured after generation; rejects regenerated with a fresh seed, up to 5 attempts

Prompt construction is deliberate. The style name, period and iconic features lead the prompt, because diffusion weights early tokens most heavily. prompt_2 is not passed: in SDXL the second encoder produces the pooled embedding that conditions the whole image, and putting a generic photographic style there collapsed every architectural style into the same contemporary office block.

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Measured properties

Style compliance

Each style declares an expected saturation band and dominant edge orientation. Both are computed from pixels and compared against the declaration.

test	result
declared saturation rank vs measured	Spearman ρ = 0.67, p ≈ 1.5 × 10⁻¹³², n = 1000
saturation within calibrated band	see eda_summary.json
edge orientation as declared	see eda_summary.json

The saturation bands are calibrated from the corpus, not set a priori. The first attempt used absolute thresholds chosen before any image existed and scored 8% compliance — because measured saturation ran 0.30–0.85 while the "medium" band had been guessed at 0.22–0.50. What the style table actually asserts is a ranking, not a value on a calibrated scale, so the headline test is rank agreement and the bands were rederived from corpus quantiles.

Does style dominate the nuisance factors?

Variance in each measured feature attributable to each factor (η²):

feature	style	light	crop	view	condition
saturation	0.63	0.12	~0.01	~0.00	~0.00
brightness	0.66	0.18	~0.01	~0.00	~0.00
contrast	0.43	0.19	~0.01	~0.00	~0.00
orientation ratio	0.56	0.04	~0.00	~0.00	~0.00
edge density	0.63	0.07	0.02	~0.01	~0.00
angle entropy	0.63	~0.01	~0.00	~0.01	~0.00

Style dominates every feature. Crop, view and condition sit at essentially zero — which validates the sampler: those axes are cycled rather than drawn independently, so they cannot correlate with style. If they did, an embedding model could score well by learning framing instead of architecture.

Light is the only meaningful contaminant, and only on the photometric features, which is physically unsurprising. Contrast has both the weakest style margin and the strongest light effect, making it the least trustworthy of the six.

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Retrieval benchmark

Image-to-image retrieval over the corpus, style as the label, chance ≈ 0.049.

model	Recall@1	Recall@3	MRR	lift
SigLIP-B/16	0.972	0.994	0.983	19.8×
LAION-CLIP-B/32	0.970	0.991	0.981	19.8×
CLIP-B/32	0.955	0.988	0.972	19.5×

These are at ceiling and the comparison cannot resolve. Plates of the same style share nearly identical prompt strings, differing only in angle, crop, light and condition, so same-style plates are near-siblings. Every slice — by crop, view, light or condition — falls between 0.975 and 1.000. The differences between models are inside noise.

Note also that material and period are functionally determined by style in this corpus (one value each per style), so they are not independent label axes. ornament_level is.

Most confused pairs

true	retrieved	rate
Georgian	Victorian Terrace	0.16
Victorian Terrace	Georgian	0.08
Mandate Eclectic	Ottoman Revival	0.06
Brutalist	Bauhaus International	0.04

Georgian ↔ Victorian Terrace was predicted before running any embedding model, from centroid distance in measured saturation/orientation space. Two British brick terraces a century apart, and both the pixel metric and CLIP agree they are the hardest pair.

The remaining confusions have a structure the pixel metric could not see: Bauhaus International acts as an attractor for the modernist styles — Brutalist, Soviet Constructivist and Japanese Metabolist all leak toward it. That is a semantic effect, not a geometric one.

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Sim-to-real

The corpus is synthetic; queries are not. Evaluated on 17 hand-labelled photographs of real buildings in Tel Aviv, Jaffa, Paris and the Greek islands, retrieving against the wholly synthetic index.

model	real R@3	synthetic R@3	gap
LAION-CLIP-B/32	0.94 (16/17)	0.991	−0.05
SigLIP-B/16	0.88 (15/17)	0.994	−0.11
CLIP-B/32	0.76 (13/17)	0.988	−0.22

Per style (LAION): Bauhaus 1.00 · Ottoman Revival 1.00 · Mandate Eclectic 0.83 · Neoclassical 1.00 · Gothic Revival 1.00 · Mediterranean Vernacular 1.00.

The headline finding. The synthetic benchmark ranked SigLIP first by 0.002 on Recall@1. On real photographs LAION wins and has less than half the domain gap. Selecting on the saturated benchmark would have shipped the weaker model. A benchmark at ceiling does not merely fail to discriminate — it discriminates wrongly.

Mandate Eclectic scores lowest, and it was also the group flagged as lowest-confidence during hand-labelling. Labelling uncertainty surfacing in the results is a reassuring sign about both.

Failure cases are architecturally sensible rather than random: a Bauhaus building retrieved as Soviet Constructivist (contemporaneous interwar modernism, strip windows, white render), and a night photograph of a Mandate building retrieved as Mediterranean Vernacular (both cream render with shutters).

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Generator failures found during construction

Four, documented in notebooks/02_facade_eda.ipynb. Three were silent — no exception, no warning, and output that looked entirely plausible.

#	failure	how it was caught	silent
1	the pooled embedding overrode every style	Bauhaus, Brutalist and Gothic all rendered as the same beige office block	yes
2	detail crops destroyed the class signal	style unrecoverable from a cropped window grid	yes
3	key_features never reached the image prompt	code review after failure 1	no
4	prompts truncated at 77 tokens	validator token-budget check	yes

A fifth was a measurement bug rather than a generation one: the orientation classifier could only emit three of the five declared labels, so every style declaring curved or diagonal scored zero and looked like a total generator failure. Fixing the metric raised orientation compliance from 60% to 83%.

The general lesson, and the reason this section exists: a synthetic corpus needs instrumented acceptance criteria. Looking reasonable is not evidence of a working generator, and a broken metric can invent a failure as easily as a broken generator can hide one.

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Known limitations

• Style boundaries blur where visual grammars genuinely overlap; the confusion matrix above reports which pairs.
• Synthetic-to-synthetic retrieval saturates and cannot rank models reliably — see the sim-to-real section.
• material and period are determined by style and are not independent labels.
• The real-photograph evaluation is small (n = 17) and unevenly distributed; per-style figures are reported alongside the mean for that reason.
• Generated plates are stylistic composites and may combine period details that would not co-occur on a real building.
• One style's declared edge orientation disagrees with measurement. The declaration is probably wrong, and it has been left visible rather than fitted to the data.

Ethical notes

No plate depicts a real building or is attributable to any architect. The accompanying app states, in the interface, that its output is a stylistic suggestion carrying no claim about a building's architect, date, or heritage status. Both the image and text prompts explicitly bar the models from naming real architects or buildings and from asserting heritage status.

Citation

@misc{facade_styles_2026,
  title  = {Facade: a synthetic architectural style corpus with
            sim-to-real evaluation},
  author = {Jonathan Dav},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Jonathandav/facade-styles}
}