A pretrained badge photo visibility classifier that sorts an image into one of 10 categories — the visibility of badge photos across different contexts. Use the badge photo visibility 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 10 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": "Blurred",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 badge photo visibility 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 applied in onboarding processes to ensure that the badge photos submitted by new employees are clear and recognizable. By processing the badge images, HR can maintain a secure and efficient verification system that reduces the risk of identity fraud.
In organizations where access to secure areas is critical, the badge photo visibility identifier can enhance security systems. By analyzing badge images at checkpoints, companies can ensure that only individuals with clearly visible and valid identification can enter, minimizing unauthorized access.
During corporate events or conferences, this function can be used to verify attendee badges at registration points. Ensuring that badges are visible and easily recognizable streamlines the check-in process, enhances security, and improves the overall attendee experience.
For companies with remote or hybrid work environments, this function can be utilized to verify the identities of employees in video conferences. By requiring participants to display their badge, organizations can ensure that meetings are secure and that the right individuals are present.
In facilities that host many visitors, the badge photo visibility identifier can assist in real-time verification of visitor badges. By ensuring that badges are easily identifiable, companies can enhance safety protocols and efficiently manage guest access.
This technology can be used in compliance audits to ensure that employee identification badges meet visibility standards. Organizations can routinely evaluate badge images to identify any that may need to be updated or reprinted, ensuring compliance with internal policies and regulations.
Organizations can use this function to monitor and control how their employees are represented through ID badges. By ensuring that badge photos meet company branding guidelines, businesses can protect their brand image and maintain a professional appearance in all interactions.
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 badge photo visibility 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.