A pretrained width of glasses frame in inches classifier that sorts an image into one of 10 categories — the width of glasses frame in inches. Use the width of glasses frame 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 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 Inch",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 width of glasses frame 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 integrated into online eyewear stores to enhance virtual try-on experiences. By accurately identifying the width of glasses frames, customers can receive personalized recommendations that fit their face dimensions, increasing conversion rates.
Eyewear manufacturers can use this classification to develop augmented reality experiences that allow customers to visualize how different frame sizes will look before purchasing. This feature would help customers make informed choices based on visual feedback, reducing return rates.
Businesses can utilize frame width classification data in their marketing campaigns. By targeting ads based on individual frame dimensions, companies can enhance customer engagement and improve the relevance of their marketing efforts.
Retailers can analyze frame width data to optimize their inventory levels. By understanding which frame sizes are most popular, companies can strategically stock their products to meet customer demand and reduce excess inventory.
Manufacturers can use frame width identification to streamline the custom eyewear production process. By automatically categorizing frame sizes, they can improve production efficiency and offer better service to customers requesting bespoke eyewear.
Optical clinics can implement this classification function during preliminary vision assessments. By quickly determining the appropriate frame width for patients based on their facial structure, clinics can provide tailored suggestions for corrective eyewear, enhancing patient satisfaction.
Fashion analysts can use frame width classification data to identify trends in eyewear styles over time. By tracking which frame widths are gaining popularity, brands can stay ahead of trends and inform their design and marketing strategies accordingly.
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 glasses frame 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.