A pretrained width of cushion in inches classifier that sorts an image into one of 10 categories — the width of the cushion in inches. Use the width of cushion in inches 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 15 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": "1-3 Inches",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 width of cushion in inches 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 in furniture manufacturing to verify the dimensions of cushions during the production process. By ensuring that the width of cushions meets specified standards, manufacturers can reduce waste and improve product consistency.
Online retailers can leverage this classification function to validate product sizes listed on their websites. By cross-checking the actual width of cushions against the advertised measurements, retailers can minimize customer returns due to size discrepancies.
Interior design firms can use this function to ensure that custom-designed cushions fit perfectly within specified furniture dimensions. This accuracy in width measurement can enhance customer satisfaction and reduce the likelihood of redesigns.
Furniture stores can implement this function as part of their inventory management systems. By accurately assessing cushion widths, they can optimize stock levels and ensure the right products are available to meet customer demand.
Publishers of consumer reports on furniture quality can incorporate this function to provide verified width specifications for cushions. By supplying accurate information, they can help consumers make informed purchasing decisions based on quality assessments.
Companies focused on sustainability can use this function to analyze the width uniformity of cushions produced from sustainable materials. Understanding variations in dimensions can lead to more efficient material usage and environmental savings.
Manufacturers can integrate this classification function into their warranty claims systems to assess the legitimacy of width-related issues reported by customers. Accurate identification of cushion dimensions can streamline the claims process and ensure fair assessments of product defects.
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 width of cushion in inches 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.