A pretrained lighting color temperature classifier that sorts an image into one of 10 categories — the lighting color temperature.. Use the lighting color temperature 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 19 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": "Amber",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 lighting color temperature 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 use case involves integrating the lighting color temperature identifier into smart home systems to automatically adjust lighting based on the time of day. By analyzing the natural light variations, the system can create a more comfortable and energy-efficient living environment, enhancing user satisfaction.
Retail stores can utilize this function to optimize in-store lighting for different areas and times to enhance customer experience. By identifying the ideal lighting color temperature, retailers can create appealing displays and settings that promote products effectively and influence purchasing behavior.
Photographers and videographers can employ this identifier to ensure the correct lighting color temperature during shoots. This capability aids in achieving accurate color reproduction, allowing for consistent quality between different shots and simplifying post-production processes.
In wellness centers and clinics, correct lighting color temperature can influence mood and mental health. By utilizing the identifier, facilities can tailor their lighting to promote relaxation or alertness, thereby improving the overall wellbeing of clients and patients.
Corporations can implement the lighting color temperature function to assess and reconfigure office lighting for increased productivity. By aligning lighting conditions with employee activities—like warm color temperatures for meetings and cooler tones for focused tasks—companies can create a more conducive work environment.
The entertainment industry can leverage this technology during production to ensure the correct lighting conditions are maintained. By identifying and adjusting to optimal color temperatures, filmmakers can achieve the desired aesthetic for their projects more efficiently and consistently.
Architects and interior designers can use this identifier as a tool to select the appropriate lighting in their projects. By identifying the lighting color temperature that complements the architecture and purpose of the space, designers can enhance aesthetic appeal and functional efficiency in their builds.
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 color temperature 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.