Pretrained computer vision classifier
Identify if a phone is counterfeit with one API call.
A pretrained if a phone is counterfeit classifier that sorts an image into one of 2 categories. Use the if a phone is counterfeit API immediately, no training required, then adapt it to your own data when you need more.
Try the if a phone is counterfeit classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this if a phone is counterfeit classifier recognizes
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.
Call the if a phone is counterfeit API
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;Example response
{
"labelName": "Counterfeit Phone",
"labelId": "label_...",
"confidence": 0.92
}Under the hood
Trained on a Nyckel-curated dataset covering 2 if a phone is counterfeit 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.
Where teams use if a phone is counterfeit classification
Retail Fraud Prevention
Retailers can integrate the counterfeit phone identifier into their point-of-sale systems to verify the authenticity of phones being sold. This helps prevent revenue loss due to the sale of fake products and builds consumer trust in legitimate businesses.
Insurance Claim Verification
Insurance companies can use the identifier to assess the authenticity of phones submitted for claims. By verifying whether a claimed lost or damaged phone is counterfeit, insurers can reduce fraudulent claims and ensure that legitimate customers are supported.
E-commerce Marketplaces Safety
Online marketplaces can implement the identification function to screen listings for counterfeit phones. This enhances buyer protection and reduces the risk of selling or purchasing counterfeit products, ultimately improving overall market integrity.
Supply Chain Management
Manufacturers and distributors can utilize the identifier to verify the authenticity of phones and components throughout their supply chain. This helps in tracking counterfeits, reducing losses due to fraud, and ensuring that only genuine products reach consumers.
Consumer Protection Services
Consumer advocacy organizations can use the identifier to help consumers verify the authenticity of phones before purchase. Providing this tool empowers buyers to make informed decisions and minimizes the chances of falling victim to scams.
Mobile Network Providers' Security
Telecom operators can integrate the identifier to check the authenticity of devices connecting to their networks. This prevents counterfeit devices from accessing services, ensuring network security and enhancing the quality of service for legitimate customers.
Phone Repair Services
Repair shops can use the identifier to prevent servicing counterfeit devices. By ensuring that repairs are only performed on authentic products, they can maintain their reputation for quality service and minimize issues related to non-genuine parts.
Common questions
What's the difference between a zero-shot and a Nyckel-trained classifier?
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.
How do I know whether this will work for my application?
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.
What happens when it makes a mistake?
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.
Do I need training data to get started?
No. This if a phone is counterfeit 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.
What does it cost to try?
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.
Related classifiers
Ready to classify if a phone is counterfeit at scale?
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.