Pretrained computer vision classifier
Identify height of doorway in feet with one API call.
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.
Try the height of doorway in feet classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this height of doorway in feet classifier recognizes
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.
Call the height of doorway in feet API
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;Example response
{
"labelName": "1 Foot",
"labelId": "label_...",
"confidence": 0.92
}Under the hood
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.
Where teams use height of doorway in feet classification
Building Code Compliance
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 Design Planning
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 Appraisal
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 Inspections
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.
Logistics and Warehouse Management
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.
Renovation Permitting
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.
Smart Home Devices
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.
Common questions
What's the difference between a zero-shot and a Nyckel-trained classifier?
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.
How do I know whether this will work for my application?
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.
What happens when it makes a mistake?
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.
Do I need training data to get started?
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.
What does it cost to try?
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.
Related classifiers
Ready to classify height of doorway in feet at scale?
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.