A pretrained height of ceiling in feet classifier that sorts an image into one of 10 categories — the height of the ceiling in feet.. Use the height of ceiling 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 ceiling 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.
This function can be used by home inspectors to quickly assess whether the ceiling height in a property meets buyers' expectations. By accurately classifying the ceiling height, inspectors can provide valuable feedback to potential homeowners, ensuring they choose properties that fit their preferences.
Interior designers can utilize this function to determine if a room's ceiling height will accommodate desired design elements, such as tall bookcases or oversized light fixtures. Accurate measurements help designers create aesthetically pleasing spaces without overcrowding or overwhelming the area.
Real estate agents can integrate this functionality into their property listing services to automatically extract and display ceiling heights. This feature enhances listings by providing detailed information that can help attract buyers looking for specific architectural features.
Project managers in construction can use this classification function to verify compliance with building codes that often specify minimum ceiling heights. Accurate data helps ensure that projects adhere to regulations and reduces the risk of costly redesigns.
Apps designed for home renovation projects can incorporate this function to assist homeowners in planning renovations. By providing accurate ceiling heights, users can make better decisions regarding new installations or structural changes in their living spaces.
In virtual reality (VR) environments, this function can enhance user experiences by simulating real-life spaces with accurate ceiling height metrics. This realism can help potential buyers or renters make more informed decisions about properties before visiting them in person.
Organizations focused on accessibility can use this function to assess whether public or private spaces meet ADA requirements regarding ceiling height. By identifying potential barriers, they can advocate for modifications that ensure safety and accessibility for all individuals.
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 ceiling 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.