A pretrained composition balance classifier that sorts an image into one of 10 categories — the visual harmony and symmetry of various compositions. Use the composition balance 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 26 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": "Asymmetrical",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 composition balance 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 employed in a manufacturing setting to automatically identify defects in visual representations of products. By analyzing the balance in composition, it can flag images that do not meet quality standards, ensuring consistent product aesthetics.
Platforms can use the function to classify images that may promote misleading content. By identifying false images based on composition balance, moderators can efficiently filter out harmful posts, improving user safety and content integrity.
E-commerce businesses can utilize this identifier to verify that product images uploaded by sellers meet specific quality criteria. By flagging images that lack balance in composition, the system supports a more professional and trustworthy online shopping experience for consumers.
Marketing agencies can implement this function to ensure that marketing materials adhere to visual guidelines. By examining the composition of images, the system can confirm whether the advertisements maintain brand consistency and are visually engaging without misrepresentation.
Art galleries and curators can apply this identifier to assist in the selection of artwork for exhibitions. By evaluating composition balance, the tool can help identify pieces that convey the intended aesthetic and thematic messages, enhancing the overall exhibition quality.
Researchers studying human perception of images can use this function to classify images in their experiments. By identifying variations in composition balance, they can analyze how these visual elements impact viewer interpretation and emotional response.
Photography software can integrate this function to provide real-time feedback during shooting or editing. By identifying false images based on composition balance, photographers can make adjustments promptly, ensuring optimal image quality before finalizing their work.
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 composition balance 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.