A pretrained if food is keto-friendly classifier that sorts an image into one of 2 categories. Use the if food is keto-friendly 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 2 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": "Keto Friendly",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if food is keto-friendly 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.
A meal planning application can utilize the keto-friendly identifier to help users create meal plans that adhere to the ketogenic diet. By classifying foods in a user’s grocery list or meal options, the app can suggest recipes and meals that fit within the user's dietary restrictions, improving user experience and adherence to their diet.
Restaurants can leverage this AI capability to tag items on their menus as keto-friendly, enhancing transparency for customers following a keto diet. This classification can aid customers in making informed decisions quickly, potentially increasing sales of keto-compliant dishes.
Online grocery retailers can implement the keto-friendly identifier on their platforms to allow users to filter products easily. This feature enables users following a ketogenic diet to quickly access and purchase items that meet their dietary needs, enhancing their shopping experience.
Nutrition tracking applications can incorporate the identifier to help users log their food intake accurately. By automatically categorizing foods as keto-friendly or not, users can monitor their adherence to the ketogenic diet and make informed choices about their food consumption.
Food delivery platforms can utilize this function to categorize meals available for delivery based on keto-friendliness. By highlighting keto options, these services can better serve customers looking for compliant meals while improving customer satisfaction and loyalty.
Health and wellness platforms can utilize the identifier in tools used by dietitians and nutritionists. By analyzing food images provided by clients, professionals can quickly assess dietary compliance and provide tailored advice for managing a ketogenic diet.
Supermarkets can enhance their mobile applications by integrating the keto-friendly identifier to assist customers while shopping. Shoppers can scan items in-store to receive instant feedback on whether they align with their ketogenic dietary goals, driving better purchasing decisions.
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 if food is keto-friendly 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.