A pretrained if food is vegetarian classifier that sorts an image into one of 2 categories. Use the if food is vegetarian 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": "Non Vegetarian",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if food is vegetarian 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 integrated into catering services and restaurants to ensure that meals labelled as vegetarian comply with dietary requirements. By using an image classification system, establishments can streamline their food preparation processes and reduce the risk of serving non-vegetarian items to customers who have dietary restrictions.
Food delivery platforms can utilize this technology to verify the vegetarian status of dishes prepared by partner restaurants. This ensures that customers receive accurate meal options, enhancing user satisfaction and trust in the service provided.
Health and wellness applications can incorporate this image classification feature to help users make informed dietary choices. Users can snap pictures of their meals to identify whether they align with their vegetarian lifestyle and receive personalized nutritional advice based on their dining choices.
Food manufacturers can apply this function during the quality control process to guarantee that vegetarian products meet industry standards. By automating the verification of ingredients in packaged foods, businesses can improve efficiency and reduce the potential for cross-contamination.
Cooking and recipe platforms can utilize the image classification function to tag or filter vegetarian recipes automatically. This enhances user experience by helping individuals easily find meal options that fit their vegetarian preferences, increasing engagement and traffic to the site.
Organizations focused on reducing food waste can use this technology to analyze unsold or leftover food items for their vegetarian content. By identifying vegetarian items, they can redirect surplus food to vegetarian-oriented charities or food redistribution programs, promoting sustainability.
Grocery stores can implement this function at various touchpoints, such as inventory management and labeling. By identifying and properly categorizing vegetarian products, retailers can optimize shelf space, improve inventory accuracy, and enhance the shopping experience for vegetarian consumers.
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 vegetarian 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.