A pretrained document margin size classifier that sorts an image into one of 6 categories — the appropriate margin size for various document formats. Use the document margin size 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 6 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": "Custom",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 6 document margin size 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 integrated into compliance management systems to ensure that documents adhere to specified margin size regulations. By automatically flagging documents that do not meet standards, organizations can streamline the compliance review process and reduce the risk of non-compliance penalties.
In the printing industry, this function can be employed to verify that submitted documents have the correct margin sizes before production begins. Ensuring proper margins prevents wasted materials and costly reprints, thus enhancing operational efficiency.
Incorporating this function into word processing applications can assist users in maintaining consistent formatting. By alerting users when margins do not meet predefined guidelines, it improves the overall quality and professionalism of documents created.
Law firms often have strict document format requirements. This function can help legal professionals ensure that all submitted documents meet the necessary margin specifications for filing, reducing the risk of rejections or delays in court processes.
Educational institutions can use this function to assess student submissions automatically against established margin size requirements. This helps maintain uniformity in student work submissions, making grading and reviewing more efficient.
In digitization projects, verifying margin sizes is critical to ensure that scanned documents maintain their original formatting. This function can be utilized to flag documents that need adjustments, ensuring better quality control in archival processes.
Within document management systems, this function can automate the verification of margin sizes across large volumes of documents. This enhances the accuracy of document classifications and retrieval processes, ensuring that documents are stored and organized according to organizational standards.
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 document margin size 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.