A pretrained chocolate brands classifier that sorts an image into one of 10 categories — what chocolate brand it is. Use the chocolate brands 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 35 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": "American Chocolate",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 chocolate brands 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 used by retailers to verify the authenticity of chocolate brands before stocking their shelves. By analyzing images of product packaging, the system can identify legitimate brands and flag counterfeits, ensuring consumers receive genuine products.
Chocolate manufacturers can utilize this function to analyze social media images and understand consumer preferences regarding specific brands. By categorizing images, companies can identify the most popular chocolate brands among customers and tailor their marketing campaigns accordingly.
Chocolate production facilities can implement this image classification function to monitor product packaging during the manufacturing process. By ensuring that only correct brand images are used, producers can avoid packaging errors that lead to brand misrepresentation or recalls.
Businesses can leverage this technology to keep track of competitor branding and product placements in the market. By analyzing the presence of different chocolate brands in stores and online, companies can gain insights about their competitors’ marketing strategies and product availability.
Brands can create interactive mobile applications where users can take photos of chocolate products and receive instant identification. This not only enhances consumer engagement but also provides an opportunity for brands to promote their products or send personalized offers to users.
Online chocolate retailers can integrate this identifier into their product listing systems to ensure accurate branding. This will help eliminate user confusion and improve the overall shopping experience by ensuring customers receive what they expect when they order a specific brand.
Event organizers and marketers can use this function during chocolate tasting events to analyze which brands are being sampled the most. By categorizing imagery collected from various events, they can determine brand popularity and the effectiveness of sampling strategies in real-time.
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 chocolate brands 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.