A pretrained blush placement classifier that sorts an image into one of 10 categories — the optimal placement for blush on various facial features. Use the blush placement 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 14 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": "Across Cheeks",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 blush placement 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 used to automatically identify inappropriate blush placement in images shared on social media platforms. By flagging these images, platforms can enforce community guidelines and maintain a positive user environment.
Cosmetic companies can leverage this function to enhance virtual makeup try-on applications by ensuring that the blush placement in the simulated images matches user preferences and anatomical realities. This results in more accurate representations and improved customer satisfaction.
Beauty brands collaborating with influencers can utilize this function to evaluate the effectiveness of blush application in influencer content. By analyzing blush placement, brands can determine which looks resonate best with audiences and adjust their marketing strategies accordingly.
Personal beauty coaching apps could integrate this function to provide feedback to users on their blush application techniques. By identifying poorly placed blush, the app can offer tailored tutorials and tips, helping users achieve a more flattering makeup look.
E-commerce sites specializing in cosmetics can use this identifier to recommend blush products based on common misapplications seen in customer-uploaded photos. By providing personalized product suggestions, sites can improve user engagement and increase sales.
Educational platforms offering makeup tutorials can use this function to analyze student-uploaded images and provide real-time feedback on blush application. Such assessments can enhance learning experiences and lead to better results for students.
Retailers can incorporate this function into AR shopping apps to ensure that virtual blush applications are accurately placed on users’ faces. By enhancing the realism of the AR experience, users will feel more confident in their purchases and have a higher likelihood of transactions.
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 blush placement 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.