A pretrained face mapping coverage classifier that sorts an image into one of 10 categories — the facial coverage areas in images.. Use the face mapping coverage 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 21 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": "360 Coverage",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 face mapping coverage 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 use case involves leveraging the face mapping coverage identifier to enhance security protocols. By confirming the identity of individuals in real-time using facial recognition technology, organizations can prevent unauthorized access in sensitive areas like airports, government buildings, and corporate offices.
Businesses can utilize the false image classification function to analyze customer demographics and behavior. By accurately identifying individual customers through facial analysis, companies can tailor marketing campaigns and promotions to target specific audience segments more effectively.
Educational institutions can implement face mapping coverage to automate attendance tracking in classrooms. This technology allows for real-time identification of students entering or leaving the classroom, improving attendance accuracy and administrative efficiency.
Social media platforms can deploy this technology to monitor user-generated content for inappropriate images or false profiles. By classifying and filtering out inaccurate imagery, platforms can maintain community standards and enhance user safety.
Retailers can integrate face mapping coverage to provide personalized shopping experiences. By identifying returning customers, staff can greet them by name and recommend products based on past purchases, ultimately increasing customer satisfaction and loyalty.
Financial institutions can utilize the false image classification function to detect fraudulent activities. By accurately verifying identities during online transactions, banks can minimize the risk of identity theft and financial fraud, protecting both the institution and its clients.
This technology can be integrated into smart home security systems to distinguish between residents, visitors, and potential intruders. By ensuring that only recognized individuals can access the home or receive security alerts, homeowners can enhance their overall safety and peace of mind.
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 face mapping coverage 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.