A pretrained pasta shapes classifier that sorts an image into one of 10 categories — what type of pasta shape it is. Use the pasta shapes 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 29 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": "Bigoli",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 pasta shapes 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.
Pasta manufacturers can utilize the image classification function to monitor the shape consistency of their products during production. By automatically detecting misformed shapes, they can ensure that only pasta meeting quality standards is packaged and shipped.
Grocery stores and pasta suppliers can implement the identifier to categorize pasta shapes in inventory systems. This would streamline inventory tracking and assist staff in quickly locating specific pasta types based on shape for restocking purposes.
Food delivery and recipe websites can integrate the pasta shape identifier to suggest recipes based on the user's pasta shape preference. This functionality enhances user experience by providing tailored meal ideas that utilize specific pasta varieties.
Cooking and culinary educational platforms can incorporate the image classifier to help users learn about different pasta shapes and their traditional uses. This can drive engagement and improve user skills in pairing pasta shapes with appropriate sauces or dishes.
Food tech companies can utilize the pasta shape identifier for apps that track dietary intake. By recognizing the shape, the app can offer nutritional information and serving suggestions specific to the type of pasta consumed, promoting healthier eating habits.
Restaurants and cooking apps can use this technology to power augmented reality (AR) experiences, allowing users to scan pasta shapes and receive instant information on origin, cooking time, and pairings. This interactive approach can enhance the dining experience and educate consumers.
Online pasta retailers can employ the image classifier to improve search functionality on their platforms. By allowing customers to upload images of pasta shapes, the system can recommend matching pasta products, optimizing the shopping experience and potentially increasing sales.
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 pasta shapes 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.