Pretrained computer vision classifier
Identify photo background blur with one API call.
A pretrained photo background blur classifier that sorts an image into one of 9 categories — the type of background blur present in your photo. Use the photo background blur API immediately, no training required, then adapt it to your own data when you need more.
Try the photo background blur classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this photo background blur classifier recognizes
A sample of the 9 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 background blur 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": "Background Only",
"labelId": "label_...",
"confidence": 0.92
}Under the hood
Trained on a Nyckel-curated dataset covering 9 photo background blur 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 background blur classification
Content Moderation
In social media platforms, the photo background blur identifier can be used to flag images that improperly blur backgrounds for aesthetic purposes, potentially violating community guidelines. This can help ensure user-uploaded content adheres to site standards, enhancing overall content quality.
Photography Review Software
Photography platforms can integrate this function to assess whether submitted images have incorrect background blurring. By identifying these images, the software can provide critiques and suggestions to photographers for improving the quality of their work.
E-Commerce Image Verification
E-commerce sites can use this identifier to ensure product images are presented authentically without misleading blur effects in backgrounds. This feature promotes user trust and enhances product presentation, leading to better sales conversion rates.
Online Ad Compliance
Digital advertising platforms can employ the photo background blur identifier to ensure that ad images meet specific visual standards and regulations. It helps maintain consistency and quality in advertising content, aligning with brand guidelines and legal requirements.
AI Art Generation Monitoring
AI art generation tools can use this function to evaluate the visual integrity of generated images. By identifying false background blurs, developers can enhance the algorithms, ensuring that generated art maintains a professional appearance.
Visual Search Enhancement
Image search engines can use the identifier to filter out images with unwanted background blurs, improving the accuracy and relevance of search results. This leads to a more effective visual search experience for users looking for specific content quality.
Social Networking Filters
Social networking apps can integrate this identifier to ensure that users are not misusing background blur effects in their images. By doing so, these platforms can maintain a high-quality visual environment, encouraging genuine interactions and content sharing among users.
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 background blur 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 background blur 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.