A pretrained type of buildings classifier that sorts an image into one of 10 categories — what type of building it is. Use the type of buildings 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 26 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": "Agricultural",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 type of buildings 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.
This function can be utilized by real estate companies to automatically classify and assess the types of buildings within a given area. By accurately identifying residential, commercial, or industrial buildings, agents can provide potential buyers with relevant information and optimize property listings.
City planners can employ this image classification tool to analyze the distribution of building types in different neighborhoods. This data can aid in making informed decisions regarding zoning laws, infrastructure improvements, and resource allocation.
Insurance companies can use the function to identify building types when evaluating risk for property coverage. By understanding the structure and purpose of buildings, insurers can create tailored policies and premiums that reflect the actual risk associated with each property.
Environmental consultants can leverage this tool to categorize buildings in relation to ecological impact assessments. Identifying building types helps in understanding urban heat islands, green space planning, and the overall impact of urban development on the environment.
Smart city applications can integrate this classification function to enhance services like waste management, traffic control, and emergency response. By recognizing building types, systems can be designed to cater to the specific needs of various structures and their occupants.
Architects can use the image classification function to conduct research on building types in specific regions, allowing them to draw inspiration and understand local architectural trends. This insight can foster innovative designs that are both practical and culturally relevant.
Construction firms can implement this tool to monitor compliance with building codes and regulations. By identifying the type of buildings being constructed, firms can ensure that the appropriate guidelines are followed throughout the construction process, reducing legal risks.
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 type of buildings 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.