A pretrained if wearing makeup classifier that sorts an image into one of 2 categories. Use the if wearing makeup 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 2 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": "Makeup Present",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if wearing makeup 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.
Retailers can utilize the makeup identifier to tailor personalized product suggestions to users based on whether they are wearing makeup. By analyzing the user's makeup status, businesses can push relevant promotions, tutorials, and product recommendations, enhancing the customer experience and increasing sales.
Influencers and content creators can leverage the identifier to optimize their posts based on audience preferences. By analyzing engagement metrics relative to makeup usage, they can curate content that resonates best with their followers, boosting interaction and brand partnerships.
Cosmetic brands can use the identifier to gather data on consumer behavior regarding makeup usage. This insight can guide product development, marketing strategies, and trend forecasting, helping companies stay ahead in the competitive beauty market.
Beauty tech companies can implement the identifier in virtual try-on applications to enhance user experiences. By assessing whether a user is wearing makeup, the app can suggest complementary products or even simulate the appearance of the makeup being tested.
Advertisers can use the makeup identifier for more effective targeting of beauty products and services. By ensuring that ads are shown to users based on their makeup wearing habits, brands can improve ad relevance and reduce advertising spend wastage.
Dermatological services can employ the identifier to assess skin health in relation to makeup usage. By tracking how often individuals wear makeup, professionals can offer advice tailored to each user’s skin needs, increasing customer satisfaction and loyalty.
Event organizers can use the identifier to gauge the makeup trends among attendees for fashion shows or beauty events. This information can assist in customizing the event experience, from product showcases to styling sessions that align with attendees' preferences.
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 if wearing makeup 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.