A pretrained autograph verification classifier that sorts an image into one of 2 categories. Use the autograph verification 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": "Authentic",
"labelId": "label_...",
"confidence": 0.92
}No labeled training data behind this function — it picks between the 2 labels using a foundation model's general world knowledge (currently GPT-4o-mini). Your image is forwarded to the model provider at inference time. Because it's zero-shot, cloned label edits take effect immediately, no retraining needed.
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 use case involves verifying the authenticity of autographs on memorabilia, such as sports jerseys, posters, or documents. By comparing the signature against a database of known authentic signatures, collectors can confirm the legitimacy of their items and prevent fraud.
Brands can utilize the autograph verification function to ensure that celebrity signatures used in marketing materials or ads are genuine. This helps maintain the integrity of the brand and prevents the use of counterfeit endorsements that could mislead consumers.
Auction houses can implement autograph verification to authenticate signatures on items being auctioned. This ensures that bidding occurs on legitimate items, boosting buyer confidence and potentially increasing the final sale price.
In the art industry, verifying signatures on artwork is crucial for establishing provenance and value. This use case allows galleries and collectors to authenticate the signatures of artists on their works, thereby preserving the integrity of the art market.
Sports memorabilia dealers can use the autograph verification function to certify the authenticity of player signatures on items like balls, cards, or photographs. This enhances the value of the items by assuring buyers of their authenticity, fostering trust in the marketplace.
Institutions and collectors can utilize autograph verification to authenticate signatures on historical documents and manuscripts. This ensures the legitimacy of historical claims and aids in the preservation of artifacts that are integral to cultural heritage.
Brands can leverage autograph verification in digital campaigns that involve influencer marketing, ensuring that the signatures collected for giveaways or promotions are real. This adds an extra layer of trust and credibility to promotional activities, enhancing audience engagement.
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 autograph verification 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.