A pretrained if image has watermark classifier that sorts an image into one of 2 categories. Use the if image has watermark 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 2 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": "Unwatermarked",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image has watermark 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 automatically scanning images in a digital asset management system to identify and flag any unauthorized usage of copyrighted material. By detecting watermarked images, companies can ensure that their intellectual property rights are respected and take appropriate actions against infringements.
In social media platforms, this function can assist in moderating user-generated content by identifying images with watermarks that indicate they belong to a specific brand or copyright holder. By filtering out these images, platforms can maintain compliance with copyright laws and protect brand identities.
E-commerce websites can use this function to ensure that product images uploaded by users do not infringe on trademarks or copyrights held by others. By identifying watermarked images, the platform can prompt users to replace them with compliant content, reducing legal risks.
Companies can utilize this technology to monitor the web for unauthorized use of their brand images. By detecting watermarked visuals, businesses can track potential misuse and take proactive measures for brand protection and reputation management.
Marketing teams can leverage this function to analyze the effectiveness of their advertising campaigns that utilize watermarked images. By identifying which watermarked assets generate engagement, they can optimize future marketing strategies and asset usage.
This system can help agencies verify that individuals or organizations are using licensed images appropriately by checking for watermarks. If a watermark is detected, it may signify that the image requires a proper license, ensuring adherence to licensing agreements.
In the printing industry, this function can be utilized to ensure that only high-quality, unaltered images are used in print materials. By identifying images with watermarks that must not be printed, it helps maintain brand integrity and the quality of the final product.
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 if image has watermark 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.