A pretrained lighting design classifier that sorts an image into one of 10 categories — what type of lighting design is most suitable for the space. Use the lighting design 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 33 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": "Accent",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 lighting design 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 be utilized by interior designers to verify that the lighting in a virtual room setting matches the intended design specifications. By analyzing images, designers can ensure the light placement, color, and intensity reflect their creative vision before any physical installation occurs.
Retail businesses can employ this function to assess the effectiveness of their store lighting. By classifying images of different display setups, managers can make data-driven decisions about adjusting lighting to enhance product visibility and improve customer engagement.
Event planners can use the false image classification to investigate potential venues' lighting conditions. By obtaining a clear picture of how venues utilize lights, planners can select spaces that enhance their event themes and optimize guest experience.
Architects can leverage this function to compare various architectural projects based on their lighting designs. Classifying images from different buildings will help them understand successful lighting strategies and foster innovative solutions in their future designs.
This technology can be integrated into smart home systems to assess and classify lighting setups. By analyzing home lighting images, users can receive personalized recommendations for improving ambiance and efficiency using automated controls based on their preferences.
Building managers can utilize the false image classification to evaluate energy efficiency in commercial spaces. By analyzing images, they can identify areas with poor lighting that waste energy and plan upgrades or modifications to improve overall sustainability.
Manufacturers of lighting fixtures can use this function during product development to classify how their lighting designs are implemented in various environments. By examining images of their products in situ, they can receive insights that guide future designs and enhance market appeal.
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 lighting design 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.