A pretrained sauce types classifier that sorts an image into one of 10 categories — what type of sauce it is. Use the sauce 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": "Barbecue Sauce",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 sauce 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.
Implementing the sauce types identifier can help manufacturers ensure that the correct sauces are being packaged and distributed. By using this function as part of a quality control system, discrepancies can be flagged early on, reducing waste and ensuring consistency in product offerings.
Retailers can utilize the sauce types identifier to automatically categorize and manage their inventory of sauces. This enables more efficient stock tracking and reordering, reducing surplus and ensuring that popular items are always available to consumers.
Restaurants and food service providers can use the sauce types identifier to verify that the correct sauces are being used in dish preparation, particularly for allergy-sensitive ingredients. This can enhance food safety protocols and improve customer satisfaction by preventing allergen exposure.
Food apps can integrate the sauce types identifier to suggest recipes based on the sauces identified in the user's inventory. This encourages culinary exploration and reduces food waste by helping users make meals with the items they already have at home.
Brands can analyze the data collected from the sauce types identifier to identify trends and preferences among their customers. This information can inform targeted marketing campaigns and special promotions, increasing sales for underperforming products or seasonal offerings.
Developers can incorporate the sauce types identifier in mobile applications to provide users with fun interactive experiences, such as quizzes or games that revolve around sauce types. This can improve user engagement and increase app retention rates.
Culinary schools and training programs can use the sauce types identifier to teach students about different sauces and their classifications. This hands-on approach can enhance learning outcomes and better prepare students for careers in the culinary arts.
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 sauce 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.