A pretrained composition quality classifier that sorts an image into one of 8 categories — the quality of the composition in the image. Use the composition quality 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 8 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": "Adequate",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 8 composition quality 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.
Online retailers can use the composition quality identifier to assess the quality of product images before they are uploaded to their platforms. This ensures that only high-quality images are displayed to customers, potentially increasing conversion rates and customer satisfaction.
Social media platforms can implement this function to filter out low-quality or misleading images in user-generated content. By ensuring that only well-composed visuals are shared, platforms can enhance user engagement and maintain brand integrity.
Marketers can utilize the identifier to evaluate images for advertisements, ensuring that only visually appealing content is used in campaigns. High-composition quality images can lead to better audience retention and higher click-through rates.
Stock photo agencies can employ the composition quality identifier to automatically categorize and recommend images based on their visual appeal. This streamlines the selection process for users looking for high-quality images for various projects.
Real estate platforms can use the function to assess and improve the quality of listings that feature property images. By ensuring that only well-composed photos are highlighted, properties can attract more potential buyers and increase interest.
Graphic design software can integrate the identifier to offer suggestions for image improvement. This can help designers enhance their projects by identifying images needing adjustments in composition before finalizing their designs.
Art galleries and marketplaces can utilize the composition quality identifier to curate art listings, ensuring that only high-quality images are presented. This not only improves the browsing experience for buyers but also enhances the perceived value of the artwork being sold.
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 quality 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.