Pretrained computer vision classifier
Identify height of table in feet with one API call.
A pretrained height of table in feet classifier that sorts an image into one of 10 categories — the height of the table in feet.. Use the height of table in feet API immediately, no training required, then adapt it to your own data when you need more.
Try the height of table in feet classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this height of table in feet classifier recognizes
A sample of the 52 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 table 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 table 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 table in feet classification
Furniture Retail Assessment
This function can be employed by furniture retailers to ensure that products displayed online match their physical dimensions. By verifying the height of tables in feet, retailers can reduce return rates caused by customers receiving improperly sized furniture.
Interior Design Planning
Interior designers can utilize this identifier to confirm that the height of tables fits within a specified design layout or concept. This aids in creating compatible spaces where aesthetics and functionality align by ensuring pieces do not overwhelm or underwhelm the room.
E-commerce Product Compliance
E-commerce platforms can implement this function to automatically verify and standardize product listings. Ensuring all table heights are accurately classified in feet enhances user experience and maintains consistency across the website.
Augmented Reality Applications
In augmented reality (AR) applications, this function can help accurately place virtual tables in real environments based on their dimensions. Users can visualize how a table will fit in their space, significantly enhancing the purchasing experience.
Home Improvement Projects
Contractors and DIY enthusiasts can use this identifier in planning home improvement projects. Knowing the height of tables helps ensure that custom installations are designed to accommodate the furniture, avoiding overcrowding or awkward proportions.
Space Optimization Analysis
Businesses focused on optimizing workspaces, such as co-working spaces or offices, can use this function to analyze the furniture they offer. By understanding table heights, they can ensure that all furniture options contribute to a balanced and ergonomic environment.
Accessibility Standards Compliance
Organizations focusing on accessibility can adopt this function to ensure that table heights meet required standards for various users. This is particularly important in public spaces where comfort and usability for everyone, including those with disabilities, must be prioritized.
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 table 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.
Ready to classify height of table 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.