A pretrained eyewear brands by logo classifier that sorts an image into one of 10 categories — what eyewear brand it is. Use the eyewear brands 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 30 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": "Adidas Eyewear",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 eyewear brands 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.
This function can be used by retailers to verify if the eyewear products they are selling are authentic and match the logos of reputable brands. By scanning the logo on the eyewear, retailers can ensure they are maintaining brand integrity and reducing the risk of counterfeit sales.
Eyewear brands can leverage this classification function to analyze consumer preferences based on logo visibility in social media posts. By identifying which logos are more frequently featured, brands can make data-driven decisions about marketing strategies and partnership opportunities.
Wholesale distributors can utilize the logo identification functionality to streamline inventory processes. By automatically classifying eyewear by logo, distributors can optimize stock levels and manage product lines more efficiently.
E-commerce platforms can integrate this feature to improve product search functionalities. By allowing customers to search for eyewear by brand logo, the user experience is enhanced, making it easier for shoppers to find their desired products quickly.
Market analysts can use the logo identification function to monitor competitors in the eyewear industry. By tracking how often different brand logos appear in various channels, analysts can gain insights into market trends and competitor positioning.
Insurance companies could implement this classification to combat fraud within claims related to eyewear. By identifying logos on claimed products, they can quickly assess the legitimacy of claims made by clients, reducing loss from fraudulent activities.
Fashion analysts can utilize the logo identification function to track which eyewear brands are gaining popularity in specific demographics. This data can be invaluable for trend forecasting and helping brands adjust their product offerings to align with consumer interests.
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 eyewear brands 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.