A pretrained beer brands by bottle classifier that sorts an image into one of 10 categories — what beer brand it is. Use the beer brands by bottle 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": "Amstel Light",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 beer brands by bottle 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 beer brands before stocking them on shelves. By analyzing bottle images, it can identify counterfeit products, ensuring consumers receive genuine brands.
Distributors can use the classifier to automate inventory management by scanning bottle images as they arrive in storage. This can help streamline supply chain operations, reducing discrepancies and ensuring that the right products are available.
Breweries can leverage this technology for targeted marketing campaigns by analyzing customer preferences based on the brands identified. The insights can help them tailor promotions and advertisements to enhance customer engagement and boost sales.
Beer manufacturers can implement this function within their quality control processes to ensure that packaging and labeling adhere to brand standards. By identifying discrepancies in bottle design and branding, producers can maintain consistency across their products.
Brands can use the image classification tool to monitor and analyze user-generated content on social media featuring their products. Understanding brand visibility and engagement through images can inform marketing strategies and foster community-building efforts.
Online retailers can employ this function to enhance their product listing and recommendation engines. By accurately classifying brands from user-uploaded images, they can provide personalized suggestions and improve the overall shopping experience for customers.
At beer festivals and tasting events, organizers can use this technology to track brand representation and attendee preferences. This data can be critical for enhancing future events, improving brand exposure, and tailoring experiences to participant interests.
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 bottle 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.