A pretrained check image alignment classifier that sorts an image into one of 10 categories — how well the objects in the image are aligned.. Use the check image alignment 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": "Centred",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 check image alignment 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 implemented in manufacturing settings to ensure that images of assembled products are properly aligned before they pass through quality checks. Misalignment detection helps reduce the rate of defective products and enhances overall production efficiency.
Online retailers can utilize this functionality to verify that product images uploaded by sellers are properly aligned. This improves the aesthetics of their product listings, leading to higher customer engagement and increased sales.
Organizations can deploy this image alignment check in document scanning processes to ensure digitized records are aligned before archiving. This ensures that all scanned materials are readable and visually consistent, facilitating better document management.
In photography applications, this function can assist in streamlining alignment checks for batch image processing, ensuring images taken in a sequence (e.g., for events) maintain a consistent alignment. This leads to a more professional finish and enhances customer satisfaction.
In healthcare, this function can be combined with medical imaging technologies to verify that images such as X-rays or MRIs are correctly aligned for accurate diagnosis. Ensuring image alignment minimizes the risk of misdiagnosis and supports better treatment decisions.
Social media platforms can use this function to validate that user-uploaded images are properly aligned when displayed on feeds. This keeps the visual content appealing and improves user experience, thereby increasing platform engagement.
In robotics applications, particularly in visual recognition systems, incorporating this function allows for real-time checks on the alignment of objects within a robotic vision model. This can enhance the precision of operations like sorting, packing, and quality inspections in automated systems.
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 check image alignment 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.