A pretrained beer brands by can classifier that sorts an image into one of 10 categories — what beer brand it is. Use the beer brands by can 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 46 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": "Anchor Steam",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 beer brands by can 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 retail stores to quickly identify and categorize beer cans on the shelves. By accurately recognizing various brands, stores can manage inventory better and ensure that popular items are stocked properly.
Marketing teams can utilize this classification function to analyze consumer behavior by tracking the popularity of different beer brands. By collecting data on which cans are most frequently identified, brands can tailor marketing strategies and promotional efforts more effectively.
Breweries and distributors can implement this function to automate inventory management processes. By scanning incoming shipments with the beer brand identifier, they can ensure that stock levels are accurately recorded and prevent discrepancies.
Mobile applications aimed at beer enthusiasts can leverage this function to provide users with augmented reality experiences. When users point their smartphones at a beer can, the app can instantly provide information about the brand, including reviews, food pairings, and nutritional facts.
Breweries can integrate this image classification tool into their quality control systems. By verifying the brand and ensuring that the correct labeling is applied to each can, companies can maintain consistency and uphold their branding standards.
Social media analytics firms can use this classification function to monitor brand presence through user-generated content. By identifying and quantifying their beer cans in photos and posts, brands can gauge engagement levels and public sentiment regarding their products.
Logistics companies can use the identifier to streamline the shipping process of beer cans. By categorizing loads based on brand identification, they can optimize delivery routes and schedules, leading to increased efficiency and reduced transportation costs.
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 beer brands by can 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.