A pretrained face sculpting level classifier that sorts an image into one of 10 categories — the face sculpting level it possesses.. Use the face sculpting level 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 20 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": "Angular",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 face sculpting level 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 by cosmetic and beauty brands to analyze and interpret the effects of their products on facial features. By assessing the 'face sculpting level,' brands can tailor their marketing strategies and product development to meet consumer demands for enhanced facial aesthetics.
Makeup and skincare apps can integrate this identification function to recommend suitable products based on the user's facial structure. By understanding the 'face sculpting level,' these applications can provide personalized makeup tutorials, enhancing user experience and engagement.
Clinics and physicians can use this functionality during initial consultations to provide patients with realistic assessments of potential outcomes. By analyzing the 'face sculpting level,' doctors can discuss personalized surgical options that align with the patient’s aesthetic goals.
Social media platforms can incorporate this function into their image filtering capabilities, allowing users to apply virtual facial enhancements. By recognizing the 'face sculpting level,' filters can be designed to accentuate or modify features in real-time, creating more engaging user content.
Health and wellness platforms can leverage this function to provide tailored fitness and nutrition plans based on users’ facial characteristics. By identifying facial structure changes over time, these platforms can motivate users with visual proof of their sculpting progress.
Researchers in fields like anthropology and sociology can use this function to collect and analyze data on facial features across different demographics. The insights derived from 'face sculpting level' can contribute to studies on beauty standards, cultural perceptions, and social behavior.
Security and surveillance systems can enhance their algorithms by integrating this function for better identity verification. By evaluating the 'face sculpting level,' these systems can improve accuracy in matching faces against databases, reducing false positives in security scenarios.
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 sculpting level 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.