A pretrained body fat percentage classifier that sorts an image into one of 10 categories — body fat percentage based on your input data. Use the body fat percentage 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 11 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": "10-15%",
"labelId": "label_...",
"confidence": 0.92
}No labeled training data behind this function — it picks between the 10 labels using a foundation model's general world knowledge (currently GPT-4o-mini). Your image is forwarded to the model provider at inference time. Because it's zero-shot, cloned label edits take effect immediately, no retraining needed.
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 body fat percentage identifier can be integrated into fitness applications to provide users with real-time data about their body composition. This feature can enhance personalized workout and nutrition plans, helping users to set and track their fitness goals more effectively.
Insurance companies can use this function to assess policy applicants' body fat percentages as part of their risk evaluation process. By automating this assessment, insurers can streamline underwriting decisions and potentially offer tailored premiums based on body composition data.
Companies can implement the body fat percentage identifier in their employee wellness programs to promote healthy lifestyles. By periodically measuring body composition, organizations can encourage employees to participate in fitness challenges, which can enhance overall health and reduce healthcare costs.
Personal trainers can utilize this technology to gain insights into their clients' body fat percentages without needing expensive equipment. This allows trainers to adjust fitness regimens and nutritional advice efficiently, providing targeted and measurable outcomes for their clients.
Athletic coaches can employ the body fat percentage identifier to monitor their athletes' body compositions. Understanding body fat levels can help in optimizing training regimens, enhancing performance, and managing the risk of injuries through better weight and diet control.
Nutritionists can use this identifier to provide evidence-based dietary advice tailored to clients' body fat percentages. By showcasing how dietary changes affect body composition, professionals can create more engaging and educational experiences for their clients.
Researchers can implement the body fat percentage identifier in studies to gather data related to obesity and body composition. This information can lead to deeper insights in public health studies, informing strategies for combating obesity and promoting healthier lifestyles in various demographics.
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 body fat percentage 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.