A pretrained height of chair in inches classifier that sorts an image into one of 10 categories — the height of the chair in inches.. Use the height of chair 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 height of chair 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.
Businesses can utilize the 'height of chair in inches' identifier to evaluate the ergonomic suitability of office furniture for their employees. This data can help organizations ensure that each employee's chair height aligns with the recommended standards, thereby enhancing comfort and productivity.
Manufacturers can apply this function during the quality assurance process to validate that all chairs produced meet specific height criteria. Identifying incorrect chair heights early can reduce returns and enhance customer satisfaction by ensuring product consistency.
Online retailers can leverage the identifier to verify chair dimensions in their listings. This will reduce discrepancies between advertised and actual product dimensions, minimizing customer complaints and returns due to mismatched expectations.
Interior designers can incorporate the chair height identifier in their client consultations to ensure the selected chairs will fit well within the intended spaces. This function aids in creating functional and aesthetically pleasing environments that cater to the needs of the users.
Retailers can use this identifier as part of their inventory management systems. By tracking chair heights, businesses can quickly identify stock discrepancies and optimize their supply chain operations, ensuring that the right products are available for customers.
Organizations providing seating solutions, such as therapeutic or healthcare centers, can implement this function to monitor and recommend appropriate chair heights for patients. Tailoring chair selection based on individual needs can lead to improved health outcomes and increased customer trust.
Businesses can create a feedback loop that uses the chair height identifier to solicit input from users about their experiences. This information can drive future product improvements, ensuring that customer preferences are aligned with product offerings.
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 chair 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.