A pretrained if image has out of focus blur classifier that sorts an image into one of 2 categories. Use the if image has out of focus blur 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 2 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": "Focus Blur",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image has out of focus 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.
This function can be used by photography studios to automatically filter out images that are out of focus before they are reviewed by editors. It allows studios to save time and resources by ensuring only high-quality images are processed for final production.
E-commerce platforms can utilize this function to ensure that product images uploaded by sellers meet quality standards. By flagging out-of-focus images, the platform can prompt sellers to resubmit clearer, more appealing product photos, ultimately enhancing customer trust and sales.
Design software can integrate this image classification function to evaluate user-uploaded images for clarity. If an image is found to be out-of-focus, the software can suggest alternatives or adjustments, helping users create polished designs more efficiently.
Social media platforms could employ this technology to identify and moderate user-generated content that lacks visual quality. By filtering out blurry images, the platform can enhance the overall aesthetic of user feeds, improving user engagement and satisfaction.
In medical imaging, this classifier can be crucial for ensuring that diagnostic images (like X-rays or MRIs) are clear and focused before analysis. It helps healthcare providers avoid misdiagnosis due to poor image quality, ultimately improving patient care.
Security firms can utilize this function to assess the quality of surveillance footage in real-time. By identifying out-of-focus frames, they can trigger automatic adjustments to cameras or notify operators to enhance the clarity, ensuring better security outcomes.
Mobile camera applications can incorporate this feature to inform users when their photos are out of focus. Providing real-time feedback will help users take better pictures instantly, increasing satisfaction with the app and encouraging social sharing of high-quality images.
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 if image has out of focus 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.
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.