A pretrained paper preservation classifier that sorts an image into one of 10 categories — what preservation technique is best suited for the type of paper. Use the paper preservation 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 28 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": "Acidic Deterioration",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 paper preservation 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 paper preservation identifier can be used by museums and libraries to assess the condition of archival documents. This system can classify images of paper artifacts to determine if they are at risk of deterioration, thus guiding restoration efforts.
Publishers and academic institutions can utilize the function to identify the preservation status of historical manuscripts. By analyzing images of fragile texts, the identifier will help prioritize which manuscripts require immediate conservation action to prevent further damage.
In digitization projects, the paper preservation identifier can be implemented to ensure that documents are in suitable condition for scanning. It helps to filter out unusable or deteriorating papers, thereby enhancing the quality of the digital archive.
Art conservationists can use this technology to analyze paintings or mixed media that include paper elements. The identifier can flag sections that may be vulnerable, allowing conservators to develop tailored preservation techniques.
Online booksellers and auction houses can employ this identifier to assess the condition of antique books before listing them for sale. By highlighting potential preservation issues, sellers can provide accurate condition reports to buyers, enhancing trust and transparency.
Educational institutions can adopt the paper preservation identifier in their curricula for courses on conservation and restoration. By using real case studies, students can engage with the technology and learn about the complexities of paper preservation.
Organizations specializing in disaster recovery can leverage this identifier to evaluate the condition of paper records post-disaster. This assessment will allow recovery teams to prioritize their efforts on the most vulnerable items, ensuring efficient recovery processes.
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 paper preservation 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.