A pretrained the paper size of a document classifier that sorts an image into one of 10 categories — what paper size a document is. Use the the paper size of a document 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 39 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": "A0",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 the paper size of a document 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 in document management systems to automatically categorize and organize digital files according to their paper size. By knowing the size of the original document, businesses can implement more efficient storage solutions and ensure compliance with document handling regulations.
Companies can leverage this function to analyze the paper sizes being printed in bulk to determine the most cost-effective printing options. By understanding the distribution of document sizes, businesses can negotiate better printer contracts and reduce paper waste.
Organizations can integrate this classifier into their workflows for converting physical documents into digital formats. By identifying paper sizes first, the document can be processed accordingly, ensuring that the conversion maintains proper formatting and is suitable for electronic storage.
In the print industry, knowing the paper size of incoming documents is crucial for quality assurance processes. This function can be used to verify that the correct paper size is being used in production, minimizing errors and ensuring product consistency.
Law firms can utilize this image classification function to ensure that all legal documents adhere to specific formatting standards, including paper size. This will help in meeting regulatory requirements and maintaining professional standards across various legal submissions.
This function can support archival systems by sorting incoming documents based on their paper size. By automating the sorting process, organizations can streamline archival workflows, improve retrieval times, and enhance overall document management.
Software applications that involve document handling can incorporate this function to personalize user interfaces based on the detected paper size. By suggesting appropriate templates or layouts, systems can enhance user experience and efficiency when creating new documents.
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 the paper size of a document 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.