A pretrained phones by logo classifier that sorts an image into one of 10 categories — what brand of phone it is. Use the phones by logo 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 16 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": "Apple",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 phones by logo 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.
Retailers can utilize the 'phones by logo' identifier to streamline inventory management by automatically categorizing devices based on their logos. This function can help staff quickly assess stock levels and manage reordering processes efficiently.
E-commerce platforms can implement this function to enhance their product listing processes. Automatically identifying phone logos can aid in the accurate assignment of product categories, leading to improved searchability and customer satisfaction.
Service centers can leverage the logo identification feature to verify the brand of the phone when customers come in for warranty claims or repairs. This ensures that only eligible devices receive the appropriate service, minimizing fraud and improving operational efficiency.
Companies specializing in market research can utilize the logo identification tool to analyze the competitive landscape. By examining trends in logo appearances across various platforms, businesses can gain insights into brand popularity and consumer preferences.
Marketing agencies can use the logo classification function to enhance ad targeting. By identifying which phone brands are associated with specific demographic segments, agencies can tailor their advertising campaigns to better reach their intended audiences.
Insurance companies can implement this feature to assist in the tracking and management of insured devices. By ensuring that claims are matched to the correct brand and model, insurers can reduce fraudulent claims while improving customer service.
Social media platforms can use the logo identification function for moderating user-generated content. This tool can automatically flag posts featuring unapproved logos that violate brand guidelines, maintaining a consistent user experience while protecting brand integrity.
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 phones by logo 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.