A pretrained if the lamp is on classifier that sorts an image into one of 2 categories. Use the if the lamp is on 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": "Lamp Is Off",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if the lamp is on 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.
A smart home system can utilize the lamp identification function to determine whether a lamp is on or off. This feature enables automated actions, such as adjusting room lighting based on occupancy or light levels, enhancing energy efficiency and convenience for homeowners.
Energy monitoring platforms can integrate this identifier to track which lamps are frequently left on. By analyzing usage patterns, users can receive alerts or suggestions for energy-saving measures, contributing to reduced electricity costs and environmental impact.
In security applications, the lamp status identifier can be used to detect unusual activity within a property. If a lamp that is typically off is found to be on at unusual hours, the system can trigger alerts or initiate recordings for further investigation.
Hotels can use this function to optimize energy consumption in guest rooms. By remotely identifying whether lamps are on, hotel staff can proactively manage room conditions for check-ins and check-outs, as well as implement energy-saving protocols during guest absences.
In elder care facilities, the identifier can help caregivers monitor residents' activity levels. By checking whether lamps in their rooms are on, caregivers can infer if a resident is awake and moving about, allowing for timely interventions if necessary.
Retailers can use this function to monitor display lighting in their stores. By receiving insights on when lamps are turned on or off, they can ensure optimal display conditions during operating hours and reduce overhead costs by managing lighting schedules more effectively.
In hybrid work scenarios, businesses can leverage the lamp identifier to assess workspace usage in real time. By monitoring whether lamps in shared offices are on, companies can better plan for resource allocation, improving workspace utilization and employee satisfaction.
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 the lamp is 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.
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.