A pretrained photo light direction classifier that sorts an image into one of 10 categories — the direction of light in the photo. Use the photo light direction 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 10 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": "Back Lit",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 photo light direction 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.
Professional photographers can utilize the photo light direction identifier to determine the optimal lighting conditions for their shoots. By analyzing the direction of light in images, they can adjust their equipment and settings to enhance the overall quality of their photographs.
In virtual reality applications, creators can use this function to ensure that virtual environments reflect realistic lighting conditions. This enhances the immersion of users by aligning virtual objects with the real-world light direction, making the experience more believable.
Social media platforms can implement this technology to flag photos that do not adhere to community standards related to lighting. For instance, images taken in adverse lighting conditions might be flagged for further review to ensure they meet quality and safety guidelines.
E-commerce platforms can leverage the light direction identifier to improve product images. By analyzing the photos, they can suggest edits or recommend best practices to optimize lighting, helping sellers present their products more effectively.
Museums and galleries can use this technology to analyze artworks in images for educational purposes. By identifying light sources and their effects, art historians can better understand the techniques employed by artists and discuss their methodologies with visitors.
AR developers can integrate this function to create realistic lighting effects for virtual objects superimposed on real-world environments. By matching the virtual lighting with that of the physical space, users experience a seamless interaction between the digital and real worlds.
Real estate agents can use the photo light direction identifier to enhance property listings. By ensuring that photographs highlight the home's best features using correct light direction, agents can create more appealing visuals that attract potential buyers.
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 light direction 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.