Pretrained computer vision classifier
Identify photo frame filling with one API call.
A pretrained photo frame filling classifier that sorts an image into one of 10 categories — what type of photo frame it is. Use the photo frame filling API immediately, no training required, then adapt it to your own data when you need more.
Try the photo frame filling classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this photo frame filling classifier recognizes
A sample of the 16 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.
Call the photo frame filling API
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'
import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())
const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;
Example response
{
"labelName": "Asymmetrical",
"labelId": "label_...",
"confidence": 0.92
}
Under the hood
Trained on a Nyckel-curated dataset covering 10 photo frame filling 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.
Where teams use photo frame filling classification
Personalized Marketing
A retail company utilizes the photo frame filling identifier to analyze customer images shared on social media. By identifying how customers fill their frames, the company can create targeted marketing campaigns that showcase products in a way that resonates with the audience's lifestyle and preferences.
Content Moderation
An online community platform employs the photo frame filling identifier to detect and filter inappropriate content shared by users. This enhances the user experience by ensuring that images meet community guidelines and promotes a safer environment for all members.
Social Media Analytics
A social media analytics firm integrates the identifier to track trends in how users compose their images. The insights gained can help brands understand popular framing styles, improving their content strategy and engagement rates with followers.
Augmented Reality Apps
An augmented reality app developer uses the identifier to enhance the user experience by allowing personalized virtual frames based on the analysis of actual images. This technology helps users visualize how virtual elements integrate into their real-world photos before sharing.
E-commerce Visual Enhancement
An e-commerce platform implements the photo frame filling identifier to recommend optimal image layouts for product listings. By ensuring products are showcased in appealing frames, the platform increases click-through rates and overall sales.
Art and Photography Feedback
An online gallery integrates the identifier to provide artists and photographers with feedback on their composition. By analyzing how effectively they fill frames, the platform encourages improvements in artistic techniques and enhances the quality of submitted works.
Event Planning Services
An event planning agency leverages the photo frame filling identifier to curate a portfolio of successful events. By analyzing which elements filled the frames of guests' photos, the agency can refine its offerings and highlight features that resonate most with potential clients.
Common questions
What's the difference between a zero-shot and a Nyckel-trained classifier?
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.
How do I know whether this will work for my application?
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.
What happens when it makes a mistake?
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.
Do I need training data to get started?
No. This photo frame filling 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.
What does it cost to try?
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.
Related classifiers
Ready to classify photo frame filling at scale?
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.