A pretrained height of house in feet classifier that sorts an image into one of 10 categories — the height of the house in feet. Use the height of house in feet 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 12 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": "1-5 Feet",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 height of house in feet 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 false image classification function can assist appraisers in accurately valuing residential properties by automatically identifying the height of houses in feet. This feature allows for more precise assessments of property worth based on height-related factors such as zoning regulations and neighborhood characteristics.
City planners can leverage this function to gather data on residential building heights across different areas. This information can help in making informed decisions about infrastructure development, zoning laws, and community aesthetics.
Insurance companies can utilize the function to assess risk associated with certain properties by identifying their heights. Taller houses may have different risk profiles in terms of storm damage, which can influence policy premiums and coverage options.
Contractors and builders can use this classification to streamline project estimations by confirming the heights of existing structures before renovations or expansions. This can lead to more accurate project planning and reduced material waste.
Researchers conducting environmental studies can incorporate the height identification function to evaluate the implications of building heights on local ecosystems. This can help in assessing factors such as wind flow, sunlight access, and rain runoff.
Real estate agents can enhance their property marketing strategies by using this function to provide potential buyers with comprehensive visual information about house heights. This data can improve listings by highlighting unique architectural features or potential renovation ideas.
Local governments can employ this classification to ensure compliance with building codes and height restrictions within certain zones. By automating the identification process, authorities can monitor and manage construction projects more effectively.
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 height of house in feet 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.