A pretrained lip dimension effect classifier that sorts an image into one of 10 categories — the dimensions of a person's lips.. Use the lip dimension effect 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 15 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.
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'
import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())
const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;
Example response
{
"labelName": "Asymmetrical",
"labelId": "label_...",
"confidence": 0.92
}
Trained on a Nyckel-curated dataset covering 10 lip dimension effect 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 'lip dimension effect' identifier can help cosmetic manufacturers analyze the impact of lip dimensions on the effectiveness and appeal of their products. By understanding how different lip shapes and sizes respond to various cosmetics, brands can tailor formulations for specific demographics, improving customer satisfaction and sales.
Retailers can leverage the false image classification function to create personalized marketing campaigns based on lip dimension effects. By segmenting customers according to their lip characteristics, brands can target specific audiences with customized product recommendations and advertisements, leading to higher conversion rates.
Beauty apps can integrate this technology to enhance virtual try-on experiences by accurately depicting how lip products will appear on users with different lip dimensions. This feature can increase user engagement and reduce return rates, as customers will have a better idea of product outcomes before making a purchase.
Brands can employ the 'lip dimension effect' identifier to analyze the performance of beauty influencers’ posts related to lip products. By assessing how different lip dimensions contribute to influencer success, brands can make informed decisions about partnerships and targeted campaigns based on real-time data.
Plastic surgeons can utilize this technology to better predict and visualize the outcomes of lip enhancement procedures. The tool can aid in pre-surgical consultations, helping to set realistic expectations for patients based on their existing lip dimensions and desired effects.
Analysts can use the false image classification function to identify trends in lip dimensions that correlate with changing beauty standards over time. Understanding these trends can inform product design and marketing strategies, enabling brands to stay ahead of market demands.
Researchers can leverage the lip dimension effect identifier to study how different lip dimensions affect the absorption and efficacy of lip care ingredients. This data can lead to innovative formulations that are better suited for specific lip shapes and sizes, thereby enhancing product effectiveness.
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 lip dimension effect 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.