A pretrained lighting type classifier that sorts an image into one of 10 categories — the type of lighting in your image. Use the lighting type 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 22 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": "Ambient",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 lighting type 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 lighting type identifier can be integrated into smart home systems to automatically adjust light sources based on user preferences or time of day. For example, if the system recognizes that a certain room is lit by incandescent bulbs, it could suggest optimal settings for energy efficiency or enhance mood with compatible smart lighting options.
Businesses can leverage a lighting type identifier to perform energy audits and monitor usage effectively. By classifying the types of lights in use, companies can gain insights into their energy consumption patterns and identify areas for potential cost savings through retrofitting or upgrading to energy-efficient lighting.
Retailers can utilize the identifier to assess and optimize their in-store lighting setups. By understanding which types of lighting are present, they can fine-tune illumination to improve product visibility, enhance customer experience, and boost sales through strategic lighting adjustments.
Building managers and compliance officers can use the lighting type identifier to ensure that all facilities adhere to local lighting regulations and energy efficiency standards. This function can streamline inspections and help organizations avoid fines associated with non-compliance.
Event organizers can use the identifier to assess venue lighting conditions before setting up equipment or decorations. By understanding the existing lighting types, organizers can choose complementary lighting designs to create the desired ambiance and atmosphere for their events.
In augmented reality (AR) environments, a lighting type identifier can enhance user experience by adjusting virtual elements according to the real-world lighting conditions. This function ensures that digital overlays blend seamlessly with the physical environment, providing a more immersive and realistic AR experience.
Researchers can utilize the identifier in studies focused on the efficacy and adaptability of different lighting types for various applications. By classifying real-world data on lighting usage, they can drive innovation in creating smarter, more adaptable lighting solutions that cater to specific needs across industries.
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 type 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.