A pretrained makeup style classifier that sorts an image into one of 10 categories — what makeup style is best suited for you. Use the makeup style 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": "Artistic",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 makeup style 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 enhance the shopping experience on beauty retail websites by suggesting makeup products that align with a user's preferred makeup style. By analyzing users' uploaded photos, the system can provide tailored recommendations, improving customer satisfaction and increasing sales.
This identifier can be integrated into social media platforms to offer users personalized filters that match their preferred makeup styles. Users can see how certain makeup looks would appear on them before applying, leading to higher engagement and sharing of makeup content.
By identifying users' makeup styles, beauty tutorial platforms can recommend video content that matches their interests. This personalization increases the likelihood that users will engage with and follow along with tutorials tailored to their style.
This technology can be applied to virtual try-on applications, allowing users to simulate different makeup looks based on their identified style preferences. This enhances the user experience for cosmetic brands and gives consumers confidence in their purchases.
Brands can leverage makeup style identification to analyze the styles favored by users engaged with specific influencers. This can inform marketing strategies and partnerships by aligning products with influencers who appeal to target audiences' aesthetic preferences.
Event organizers in the beauty industry can utilize this function to suggest appropriate makeup styles for various occasions, such as weddings or parties, based on user preferences. This can simplify the planning process for clients seeking professional makeup services.
Beauty blogs can use the makeup style identifier to generate content tailored to readers' preferences. By providing articles, tips, and product lists that resonate with identified styles, blogs can boost user engagement and drive affiliate marketing revenue.
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 makeup style 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.