Pretrained computer vision classifier

Identify if patient has wristband on with one API call.

A pretrained if patient has wristband on classifier that sorts an image into one of 2 categories. Use the if patient has wristband on API immediately, no training required, then adapt it to your own data when you need more.

Use this classifier Try with your image

Pretrained · Nyckel-trained 2 labels out of the box Image input

nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke

Wristband Off 0.92

Wristband On 0.05

Try the if patient has wristband on classifier

Drop in a photo and get the prediction back. No signup, no setup.

What this if patient has wristband on classifier recognizes

A sample of the 2 labels this pretrained classifier chooses between.

Wristband Off

Wristband On

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 if patient has wristband on API

Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:

API quick start

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;

Create free account to get your live API key Clone this function to get a live API key

How to invoke a function →

Example response

{
  "labelName": "Wristband Off",
  "labelId": "label_...",
  "confidence": 0.92
}

API documentation Authentication Pricing Get your endpoint

Under the hood

Model type

Nyckel-trained

Trained on a Nyckel-curated dataset covering 2 if patient has wristband on categories, served on Nyckel's own infrastructure — your image stays on Nyckel.

Input

Image

Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.

Make it yours

Adaptable

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.

More than a demo: this page is one of thousands of pretrained functions on Nyckel, an ML classification platform. You can invoke classifiers by API, review predictions, correct labels, collect samples from production traffic, and promote any pretrained function to a private custom model — without changing your integration.

Where teams use if patient has wristband on classification

Patient Identification Verification

This function can be employed in healthcare facilities to ensure that patients are wearing their identification wristbands. By confirming that a wristband is present, it reduces the risk of patient misidentification and enhances safety in administering treatments or medication.

Access Control in Restricted Areas

The image classification can be integrated with security protocols in hospitals, where personnel and visitors wearing wristbands are allowed access to specific areas. This enhances security and ensures that only authorized individuals can enter sensitive locations.

Attendance Tracking in Facilities

In rehabilitation centers or elder care facilities, the system can monitor and track patient attendance by assessing if they have their wristband on. This provides facility managers with accurate data on patient presence for safety and accountability purposes.

Automated Patient Interaction

With the identification of wristbands, interactive kiosks or mobile applications can offer personalized services or health reminders when patients are detected. This helps in engaging patients more effectively regarding their health management while also streamlining communications.

Emergency Response Optimization

In emergency situations, the identification of wristbands can streamline and prioritize responses based on patient need or status. The system can quickly ascertain who is present and potentially in need of urgent medical attention, facilitating quicker care delivery.

Inventory Management of Wristbands

This function can assist in tracking the usage and distribution of wristbands within healthcare settings. By monitoring which patients are wearing wristbands, facilities can ensure they have adequate supplies and manage inventory more efficiently.

Compliance Auditing and Reporting

The image classification can be used in compliance audits to verify that all patients are wearing wristbands as mandated by hospital policies. This ensures adherence to safety standards and can help in identifying areas for improvement in patient management protocols.

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 if patient has wristband on 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.

Ready to classify if patient has wristband on 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.

Use this classifier Read the docs