A pretrained elevator presence classifier that sorts an image into one of 2 categories. Use the elevator presence 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": "Elevator Present",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 elevator presence 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.
The elevator presence identifier can be integrated into building management systems to optimize elevator usage. By analyzing real-time data on elevator occupancy, building managers can improve energy efficiency and reduce waiting times for occupants.
In the event of an emergency, the elevator presence identifier can assist first responders by providing information on which elevators are in use and whether they are safe for evacuation. This can enhance the overall safety and efficiency of emergency response protocols.
By monitoring elevator usage patterns, maintenance teams can identify potential wear and tear in elevators. The data can be used to schedule proactive maintenance before issues arise, thus minimizing downtime and reducing repair costs.
The elevator presence identifier can be used to ensure compliance with accessibility regulations. By tracking and reporting elevator usage by individuals with disabilities, building operators can make data-informed decisions to improve access for all visitors.
Retailers and office buildings can use the elevator presence data to create a more comfortable experience for visitors. By predicting peak usage times, they can implement measures like additional signage or staff assistance to manage elevator traffic smoothly.
Integrating elevator presence data with energy management systems allows buildings to optimize energy consumption. The data can inform energy-saving strategies, such as adjusting lighting or climate control based on elevator traffic patterns.
In urban environments, the elevator presence identifier can contribute to smart city initiatives by providing valuable data for traffic and footfall analysis. This could inform city planners about mobility trends, leading to improved infrastructure and public transport planning.
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 elevator presence 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.