A pretrained if patient is naked classifier that sorts an image into one of 2 categories. Use the if patient is naked 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": "Dressed",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if patient is naked 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 help healthcare facilities ensure compliance with privacy regulations by identifying when a patient is improperly exposed during imaging procedures. By automatically flagging such instances, staff can take immediate action to maintain patient dignity and confidentiality.
Hospitals can integrate this image classification function into their medical imaging systems to automatically adjust imaging protocols. If a patient is identified as naked during an imaging process, the system can prompt staff to provide appropriate coverings or reposition the patient, ensuring more practical and ethical imaging procedures.
Telehealth platforms could use this function to evaluate and guide patients remotely, alerting healthcare providers if a patient is not adequately covered during a virtual consultation. This capability enhances the quality of care by ensuring that health assessments are conducted appropriately.
Educational institutions can utilize this technology in a simulated environment to train medical staff about the importance of patient dignity and privacy. By incorporating scenarios where patients are identified as naked during practice imaging, trainees can learn to respond appropriately.
Insurance companies can implement this function as part of their claim validation processes to ensure that all imaging claims meet required standards. If a patient's exposure is flagged, it can lead to denial of payment until corrective measures are confirmed.
In emergency medical situations, this function can assist first responders in ensuring that patients are adequately covered for both medical and legal reasons. Identifying exposure quickly can lead to better patient care and compliance with emergency standards of practice.
Healthcare facilities can leverage data from the image classification function for analytics on patient exposure incidents. This data can help identify trends, areas for improvement in patient handling, and inform policies aimed at enhancing patient privacy and care standards.
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 patient is naked 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.