A pretrained qr code version classifier that sorts an image into one of 10 categories — what type of QR code it is. Use the qr code version 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 10 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": "Version 1",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 qr code version 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.
Food distributors can use this function to ensure that the QR codes on food packaging are accurate and trustworthy. This can help in tracing the origin of food products, ensuring compliance with safety regulations, and protecting consumer health.
This function can be used by brands to verify the authenticity of products by scanning the QR codes on packaging. It helps identify if the product is genuine or a counterfeit, thus protecting brand integrity and customer trust.
Retailers can implement this function to streamline inventory processes by verifying the products during stock checks. Scanning QR codes allows for quick identification of items, reducing errors and improving overall inventory accuracy.
Manufacturers can utilize this function to track the authenticity of materials sourced from various suppliers. By scanning QR codes, they can ensure that the materials used in production are legitimate and traceable back to approved vendors.
Event organizers can deploy the function to verify the validity of tickets through QR codes. This can prevent fraud and ensure that attendees have legitimate tickets, enhancing security and customer experience at events.
E-commerce platforms can integrate this function to confirm that the products being shipped match the QR codes generated during the purchase process. This assists in reducing return rates and increasing customer satisfaction through accurate order fulfillment.
Food distributors can use this function to ensure that the QR codes on food packaging are accurate and trustworthy. This can help in tracing the origin of food products, ensuring compliance with safety regulations, and protecting consumer health.
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 qr code version 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.