A pretrained if likely to have nuts classifier that sorts an image into one of 2 categories. Use the if likely to have nuts 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": "Contains Nuts",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if likely to have nuts 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.
Food manufacturers can utilize the 'if likely to have nuts' identifier to accurately label their products, ensuring they meet regulatory requirements. This helps consumers with nut allergies make informed decisions, thereby reducing the risk of allergic reactions.
Cooking platforms can integrate this function to filter recipes based on allergen content. Users with nut allergies can receive personalized recipe suggestions that automatically exclude nut-containing ingredients, enhancing their cooking experience.
Meal kit and meal delivery companies can use the identifier to tailor their offerings for customers with dietary restrictions. By identifying potential allergens, they ensure customers receive nut-free meals, improving customer trust and satisfaction.
Regulatory bodies or food safety inspection services can employ this classification tool to quickly assess food items for allergen compliance. This enhances the safety of food products entering the marketplace and ensures consumer protection.
Health and fitness applications can leverage the identifier to help users track allergen exposure in their diets. Users can receive alerts or suggestions on nut-free alternatives, supporting better health management for those with allergies.
Online grocery and food retailers can implement this feature to enhance search filters for allergen-free products. Shoppers can easily find and purchase nut-free items, streamlining the shopping experience and catering to their dietary needs.
Food scientists and product developers can use the identification function during the product development phase. This helps guide formulations and innovations in creating new food products that are allergen-friendly and cater to health-conscious 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 likely to have nuts 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.