A pretrained lipstick color classifier that sorts an image into one of 10 categories — what lipstick color it is. Use the lipstick color 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": "Berry",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 lipstick color 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 enables users to upload their photos and receive real-time feedback on how different lipstick colors would look on them. Cosmetic brands can integrate this feature into their apps or websites, enhancing customer engagement and reducing return rates for lip products.
By analyzing user-uploaded images, businesses can identify preferred lipstick shades and target them with tailored marketing. This allows for more effective email campaigns and social media ads that resonate with individual customer preferences.
Retailers can leverage the function to track popular lipstick colors in user images uploaded across different platforms. This data can inform inventory decisions, ensuring that stores stock trending shades and stay ahead of market demands.
Brands can include an augmented reality feature that identifies the lipstick shade in videos when influencers or customers unbox products. This adds a layer of interactivity and education to social media marketing efforts, showcasing how specific colors appear on different skin tones.
Users can upload images of multiple lipstick products to receive suggestions on similar shades available in their favorite brands. This function can improve customer satisfaction by simplifying the decision-making process when comparing different lip products.
Beauty salons or makeup artists can utilize this function in conjunction with virtual consultations to recommend the most suitable lipstick shades based on a customer’s uploaded photo. This can enhance the client experience by providing personalized and expert advice.
Cosmetic companies can analyze classification data from user images to identify unmet needs or gaps in the market for certain lipstick shades. This insight can drive new product development that is closely aligned with consumer demand.
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 lipstick color 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.