A pretrained height of doorway in feet classifier that sorts an image into one of 10 categories — the height of the doorway in feet. Use the height of doorway 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 51 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 Foot",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 height of doorway 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 utilized by architects and construction managers to ensure that doorways meet local building code requirements regarding height. By automatically classifying doorway heights, it helps in speeding up the compliance verification process during project planning and inspection phases.
Interior designers can leverage this function to assess existing door heights when planning renovations or new designs. By understanding doorway measurements, designers can create layouts that enhance flow and accessibility in residential and commercial spaces.
Real estate agents and appraisers can use this function to gather accurate information on property features for listing descriptions. Providing precise doorway measurements can help differentiate properties and inform potential buyers about accessibility options within the home.
Safety inspectors can integrate this function into their toolkit to quickly assess doorway heights in older buildings. This information is crucial for evaluating compliance with safety standards and identifying any risks associated with inadequate height during emergencies.
In logistics, managers can use this function to classify doorway heights as part of space utilization assessments. Understanding doorway dimensions assists in planning for the movement of goods and equipment, ensuring that all items can pass through without issue.
City planners can employ this function to automate the review process of renovation applications. By classifying doorway heights, they can efficiently determine if proposed changes comply with zoning laws and height restrictions, reducing the time taken for permit approvals.
Manufacturers of smart home devices can use this function to enhance the capabilities of automated systems. By integrating doorway height classification, devices can intelligently navigate through spaces, making adjustments based on the spatial limitations they encounter.
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 doorway 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.