A pretrained if students are wearing their uniforms classifier that sorts an image into one of 2 categories. Use the if students are wearing their uniforms 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": "Wearing Uniform",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if students are wearing their uniforms 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.
Schools can implement this function to automatically track whether students are adhering to uniform policies throughout the school year. By analyzing images from school entrances, the system can generate reports on compliance rates and identify trends in uniform adherence.
During school events or activities, this function can be used to verify that students are wearing their uniforms, ensuring alignment with school policy. This helps in maintaining a standard presentation during official functions and can facilitate easier attendee tracking.
Educators can leverage this classification function to correlate uniform compliance with student behavior and performance. Studies could be conducted to determine if there is a relationship between wearing uniforms and particular behavioral outcomes.
Retailers selling school uniforms can utilize this function in conjunction with a rewards program where students receive discounts for wearing their uniforms to participating stores. The system can scan for uniform compliance to provide instant rewards at the point of sale.
School transportation services can adopt this image classification to confirm that students are in compliance with uniform requirements before boarding the bus. This ensures safety and accountability while reinforcing school pride and identity.
Schools can use this function for promotional materials by ensuring all participants are wearing uniforms in photos taken during events. This leads to a consistent image branding for the school in newsletters, social media, and other marketing resources.
Schools can send alerts to parents if their children are not wearing uniforms during designated times via mobile notifications. This feature encourages parents to ensure their children adhere to school dress codes, fostering accountability and communication between parents and schools.
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 students are wearing their uniforms 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.