A pretrained food types classifier that sorts an image into one of 10 categories — what type of food it is. Use the food types 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 20 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": "Beverage",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 food types 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 employed by food manufacturers to ensure that the images used on product packaging accurately represent the food type. By comparing packaging images with the classifier results, businesses can enhance brand consistency and avoid misleading customers.
Restaurants can utilize this image classification to analyze customer-uploaded food pictures on social media and food delivery platforms. Insights gained from popular food types can help improve menu offerings, increasing customer satisfaction and sales.
Health and diet apps can implement this function to analyze users' food photos, helping them track their dietary intake more accurately. By categorizing food types accurately, users can receive personalized feedback and nutritional tips.
Food processing industries can use this classifier for quality assurance, ensuring that only products meeting specific visual criteria are labeled and shipped. This application can help reduce product recalls and enhance consumer trust.
Nonprofits focused on food waste can apply this function to efficiently sort and categorize unsold food items for redistribution. By identifying food types, organizations can better match products with the needs of food banks and shelters.
Nutritionists and health coaches could incorporate this function into virtual coaching platforms. Clients can upload images of their meals, and the system will classify and analyze food types, allowing for more accurate dietary feedback and personalized plans.
Online grocery stores can benefit from this classification tool to enhance product descriptions and recommendations based on food type analysis. The results can streamline inventory management and promote cross-selling of related items, improving the shopping experience.
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 food types 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.