A pretrained photo white balance classifier that sorts an image into one of 10 categories — the optimal white balance settings needed for your photo. Use the photo white balance 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": "Artificial Light",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 photo white balance 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 integrated into photo editing software to automatically identify and correct white balance issues in images. By detecting incorrect color temperatures, users can achieve more accurate and appealing photos without manual adjustments.
Online retailers can employ this functionality to enhance product images by ensuring consistent and correct color representation. Accurate white balance leads to better customer perception and reduces the likelihood of returns due to color discrepancies.
Content creators and influencers can utilize this feature to streamline their photo editing process, ensuring that every image shared on platforms like Instagram maintains the desired aesthetic. Correctly balanced photos enhance visual appeal, increasing engagement and follower interaction.
Businesses managing large volumes of visual content, such as real estate companies or event organizers, can implement this function in automated photo review systems. By quickly identifying poor white balance, they can either flag these images for manual adjustment or automatically correct them before publication.
Photography schools and online learning platforms can incorporate this function into their curriculum to teach students about the importance of white balance. It helps students analyze their work, understand common pitfalls, and learn how to capture and edit images effectively.
Digital asset management (DAM) systems can leverage this function to categorize and tag images based on their color temperature and white balance accuracy. This enhances searchability and allows businesses to maintain a high standard of visual quality across their asset libraries.
Mobile camera applications can embed this function to provide real-time feedback when capturing photos. By alerting users to white balance issues as they shoot, these apps can help improve the overall quality of images taken in varying lighting conditions.
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 photo white balance 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.