A pretrained photo focus accuracy classifier that sorts an image into one of 10 categories — what object is present in the image. Use the photo focus accuracy 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 20 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": "Balanced Exposure",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 photo focus accuracy 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 to enhance user-generated content on social media platforms by automatically identifying and flagging photos that lack optimal focus. By ensuring that only high-quality images are displayed, platforms can improve user engagement and overall satisfaction.
E-commerce websites can utilize the photo focus accuracy identifier to assess product images uploaded by sellers. By ensuring that only sharp, focused images are promoted, online marketplaces can enhance the shopping experience and reduce return rates due to misleading product representations.
Companies with large libraries of digital images can implement this function to streamline the organization of their assets. This automated checking system can help categorize images based on quality, making it easier to retrieve high-quality visuals for marketing and promotional materials.
Smartphone manufacturers can integrate this identifier into their camera software to provide users with real-time feedback on focus accuracy while taking photos. This feature can help users capture better images, enhancing the overall photography experience and satisfaction with the device.
Photo editing tools can leverage the false image classification function to suggest improvements to images that are out of focus. This can enable users to make informed edits or automatically refine the images, leading to higher quality outputs for professionals and hobbyists alike.
In medical imaging, ensuring the clarity of diagnostic images such as X-rays or MRIs is crucial. This function can aid radiologists by automatically flagging unsharp images, ensuring that only the most accurate visuals are considered for diagnosis, thereby enhancing patient care.
Security and surveillance systems can improve the accuracy of their facial recognition algorithms by pre-processing images to identify and exclude those with poor focus. This can lead to higher success rates in identification and monitoring, enhancing security measures in various environments.
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 photo focus accuracy 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.