A pretrained stamp series recognition classifier that sorts an image into one of 10 categories — what type of stamp it is. Use the stamp series recognition 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 30 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": "Aqua Printed",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 stamp series recognition 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.
The stamp series recognition function can be integrated into postal sorting systems to automatically identify and categorize mail based on the stamps used. This will streamline operations, reduce human error, and enhance mail routing efficiency.
Collectors and dealers can utilize the stamp recognition function to assess the value and authenticity of postage stamps in their inventory. By accurately identifying the series and mint condition, users can make informed decisions about buying, selling, or trading stamps.
Libraries and museums can implement this recognition technology to catalog and archive historical postage stamps. This aids in the preservation of philatelic history while making it easier for researchers to access and study postal heritage.
Postal services can use the recognition function to identify counterfeit stamps. By comparing stamp series against a verified database, the system can flag potential fraudulent stamps, protecting revenue integrity.
Online auction platforms can leverage this technology to enhance their stamp selling features. By providing automatic identification and detailed descriptions of stamps, sellers can attract more buyers with accurate listings and bidding information.
Educational institutions can use stamp recognition software as a learning tool for students interested in history, art, or economics. This function can support interactive projects, helping students understand the significance, design, and history of postage stamps.
Developers can create mobile applications that use stamp series recognition for collectors to instantly identify stamps while on the go. Such an app could provide additional information about the stamp, including its history and value, enhancing the collecting experience.
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 stamp series recognition 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.