A pretrained facial hair classifier that sorts an image into one of 10 categories — the type of facial hair present. Use the facial hair 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 19 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": "Along The Jawline Beard",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 facial hair 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.
The facial hair identifier can enhance security measures at checkpoints and access controls by accurately identifying individuals based on their facial hair traits. This function can help in reducing impersonation and unauthorized access by cross-referencing facial features with stored images.
Businesses can leverage the facial hair identifier to segment their audience based on their facial hair styles, allowing for more targeted marketing strategies. By tailoring promotions and advertisements to specific facial hair categories, brands can increase engagement and conversion rates.
Social media platforms can utilize the facial hair identifier to suggest suitable filters and effects for users' photos based on their facial hair. This feature can enhance user experience and boost engagement by offering personalized content options aligned with users' appearances.
Grooming service providers can implement this function in virtual fitting rooms to show customers how different facial hair styles would look on them. By allowing users to experiment with various styles digitally, businesses can increase customer satisfaction and drive sales.
Insurance companies can use the facial hair identifier to verify the authenticity of the individuals involved in claims, especially in cases where identity might be contested. By ensuring that the person submitting a claim looks like the one in their records, fraud can be mitigated more effectively.
Photography applications can integrate the facial hair identifier to provide users with insights and analytics about the facial hair present in their photos. This functionality can create an engaging user experience by enabling users to track changes in their grooming styles over time.
Public health organizations can use the facial hair identifier to monitor trends in grooming styles, which can help in tailoring health campaigns or educational materials aimed at promoting hygiene and grooming practices. Understanding demographic grooming preferences can lead to more effective public health messaging.
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 facial hair 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.