A pretrained architectural style classifier that sorts an image into one of 10 categories — what architectural style it is. Use the architectural style API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 29 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Apartment",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 architectural style categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
The architectural style identifier can assist real estate appraisers in determining the value of properties based on their architectural styles. By comparing similar style properties in a neighborhood, appraisers can more accurately assess market trends and property values.
Urban planners can utilize the architectural style identifier to ensure that new developments align with the existing architectural landscape of a community. This helps maintain historical character and aesthetic coherence, which can positively influence public perception and property values.
Historical societies and preservationists can use the architectural style identifier to catalog and protect buildings of historical significance. By identifying and classifying architectural styles, stakeholders can prioritize conservation efforts and educate the public about architectural heritage.
Architecture firms can leverage the architectural style identifier to tailor their marketing strategies. By understanding the trending architectural styles in specific regions, firms can create targeted campaigns that appeal to prospective clients seeking specific aesthetics.
Interior designers can use the architectural style identifier to align their design elements with the building's architecture. This ensures harmonious integration of interior spaces with exterior styles, enhancing the overall appeal of a renovation or new build.
An automated real estate platform can utilize the architectural style identifier to categorize and filter listings. This feature would allow potential buyers to search for properties based on preferred architectural styles, streamlining their property search experience.
Regulatory bodies can use the architectural style identifier to monitor and enforce construction compliance with local design guidelines. This helps maintain community standards and promotes adherence to zoning laws and architectural requirements in new developments.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This architectural style classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.