A pretrained if there's a stamp classifier that sorts an image into one of 2 categories. Use the if there's a stamp 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 2 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": "Has Stamp",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if there's a stamp 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 True Image Classification function can be implemented in banks and financial institutions to automatically verify documents for authenticity. By identifying whether a stamp is present, these organizations can streamline their approval processes, reducing manual errors and enhancing operational efficiency.
Law firms can utilize this function to assess the presence of stamps on legal documents quickly. This automated checking helps to ensure compliance with legal standards and reduces the time spent on document reviews, thus improving overall case handling speed.
In e-commerce, this image classification tool can validate receipts or invoices that include stamps. This can significantly enhance fraud detection efforts by ensuring that only genuine, stamped documents are processed for returns or warranty claims.
Manufacturing industries can employ the stamp identifier to verify quality assurance stamps on products. By automating the quality check process, companies can ensure that only products meeting required standards reach consumers, thereby maintaining brand integrity.
Archives and libraries can use this function to categorize and index historical documents that contain stamps. By automating the identification process, institutions can enhance their archival database's accuracy, making it easier for researchers to locate important materials.
Government agencies can incorporate this classification function to authenticate official documents, such as permits and licenses. By ensuring the presence of official stamps, the agency can reduce the risk of fraud and ensure only legitimate documents are processed.
Healthcare organizations can apply this function to ensure that vital medical documents, like consent forms or prescriptions, have the required stamps. This helps to maintain compliance with regulations, reduces legal risks, and enhances patient safety by verifying document authenticity.
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 if there's a stamp 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.