A pretrained stamp image type classifier that sorts an image into one of 10 categories — what type of stamp it is. Use the stamp image type 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 15 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": "Airmail Stamp",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 stamp image type 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 in financial institutions to classify stamped documents such as contracts and agreements. By accurately identifying the type of stamp, organizations can quickly verify document authenticity and streamline approval processes.
In manufacturing, this image classification function can be utilized to ensure that stamped products meet quality standards. By identifying incorrect or missing stamps, manufacturers can minimize the risk of shipping defective products and enhance overall quality assurance.
Businesses in regulated industries can use this tool to ensure that all necessary approvals and stampings are in place on documents. By automatically classifying and validating stamp types, organizations can maintain compliance with industry standards and avoid legal issues.
Law firms and public institutions can leverage this function to digitize and organize archived stamped documents. By classifying image types, they can create searchable databases, making it easier to retrieve essential documentation when needed.
This function can assist in identifying fraudulent documentation by analyzing stamped images for irregularities or inconsistencies. In sectors like real estate and legal services, it can serve as an additional layer of security against forged or tampered documents.
Retailers can implement this image classification function to manage inventory that requires stamped approval, such as incoming goods and shipments. By streamlining the identification and verification of stamped paperwork, companies can enhance inventory accuracy and reduce discrepancies.
Companies can use this function to analyze customer-submitted images that contain stamps, such as receipts or warranty documents. By categorizing these images, businesses can gain insights into customer interactions, usage patterns, and improve their offerings based on reliable data.
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 image type 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.