A pretrained photo reflection handling classifier that sorts an image into one of 10 categories — what the subject's reflection represents. Use the photo reflection handling 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 25 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": "Absent",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 photo reflection handling 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.
Online retailers can leverage the photo reflection handling function to ensure that product images are clear and accurately represent items for sale. This helps to avoid misleading potential customers and reduces return rates due to misrepresented products.
Social media platforms can implement this function to check and filter out photos with problematic reflections, preventing the circulation of inappropriate images. This enhances user experience and maintains community guidelines.
Real estate agencies can utilize this technology to automatically assess property images for distortions caused by reflective surfaces. This ensures that listings present an accurate view of the properties, ultimately aiding in customer decision-making.
Graphic design software can integrate photo reflection handling to improve image quality for professionals and amateurs alike. By mitigating reflection issues, users can create higher-quality marketing materials without extensive photo editing.
Automotive inspection applications can use this function to analyze vehicle images for flaws that may be obscured by reflections. This can enhance the evaluation of used vehicles, ensuring buyers receive complete information on their prospective purchases.
Security systems can apply photo reflection handling to enhance the clarity of surveillance images taken in reflective environments. This can aid in identifying individuals or objects of interest more accurately during security reviews.
Insurance companies can implement this function in claim processing systems for assessing damages captured in photographs. By reducing reflections in claims images, companies can achieve more accurate evaluations and streamline the claims approval process.
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 reflection handling 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.