A pretrained page count classifier that sorts an image into one of 10 categories — the total number of pages in a document. Use the page count 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 10 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": "Compact Pages",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 page count 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 utilized by financial institutions to verify the authenticity of submitted documents. By accurately identifying the page count, organizations can determine if the correct number of pages is submitted, thus preventing fraud and ensuring regulatory compliance.
Companies can employ the page count identifier to automate the invoice verification process. By ensuring that invoices are complete and match the expected page count, businesses can streamline their accounts payable operations and reduce discrepancies.
Law firms can use this function to manage and categorize legal documents effectively. By identifying the number of pages, firms can quickly ascertain if a document is complete and ensure that all necessary information is present before proceeding with a case.
Publishers can leverage the page count identifier to streamline manuscript submissions from academic authors. By ensuring that the manuscripts adhere to the specified page limits, publishers can maintain quality standards and simplify the peer-review process.
E-book platforms can utilize this function to verify the integrity of uploaded content. By checking the page count, platforms can ensure a consistent reading experience and prevent issues related to incomplete or corrupted files.
Organizations can use the page count identifier as part of their digital archiving strategy. By ensuring that archived documents match their expected page counts, companies can maintain accurate records and enhance their retrieval processes in the future.
Subscription box services can employ this function to verify that all printed materials included in a package meet their specified page count. This ensures customer satisfaction by guaranteeing the consistency and completeness of the items delivered.
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 page count 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.