A pretrained if image is in focus classifier that sorts an image into one of 2 categories. Use the if image is in focus 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": "In Focus",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image is in focus 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 use case involves using the image focus identifier to analyze product images during the manufacturing process. By ensuring that only in-focus images are considered valid, companies can reduce errors in quality control and enhance product reliability.
In the healthcare sector, this classification function can be utilized to assess the clarity of medical images such as X-rays or MRIs. It helps healthcare professionals focus on images that are diagnostically useful and eliminate those that may lead to misinterpretation due to blur.
E-commerce platforms can leverage this technology to automatically filter out blurry product images uploaded by sellers. Ensuring that only sharp, focused images are displayed not only enhances user experience but also boosts sales by presenting products in the best light.
Social media platforms can implement the focus identifier to improve user engagement by promoting only high-quality images in feeds. This would enhance user experience and increase time spent on the platform by ensuring visually appealing content is prioritized.
In the field of autonomous driving, the image focus function can be integrated into the vehicle’s visual recognition systems. It could help in analyzing and interpreting road signs, obstacles, and other critical visual cues by ensuring that only focused images are processed for decision-making.
Security systems can incorporate the image focus identifier to ensure that only clear surveillance footage is retained for analysis. This can enhance monitoring accuracy and reliability while reducing storage costs associated with blurry, unusable recordings.
AR developers can use this function to enhance user experience by ensuring that only well-focused images interact with AR elements. This can improve the realism and effectiveness of AR applications, making the experience more immersive and enjoyable for users.
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 is in focus 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.