A pretrained banks by logo classifier that sorts an image into one of 10 categories — what bank the logo belongs to. Use the banks 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": "Anz",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 banks 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.
Banks can use the 'banks by logo' identifier to verify the authenticity of their branding in advertisements and promotions. This ensures that only official logos are used, preventing brand dilution and maintaining a consistent image across all marketing materials.
Financial institutions can implement the logo identification function to detect fraudulent activities where scammers use fake logos to mislead customers. By cross-referencing logos against a database, banks can alert users to potential scams featuring counterfeit branding.
This function can be integrated into customer service applications where users can take a picture of a logo for assistance. The system would identify the bank and provide tailored information or help, improving user engagement and satisfaction.
Marketing teams can leverage the logo identification capabilities to monitor social media platforms for unauthorized or incorrect use of their logos. This enables banks to address violations quickly and protect their brand integrity.
During events or partnerships, the function can be used to identify logos for sponsorship visibility. This helps banks assess brand exposure and engagement with target demographics at events or through media coverage.
Banks can utilize the logo identification tool to analyze competitors’ branding efforts. By tracking usage of competitor logos in various media, banks can gain insights into market trends and adjust their strategies accordingly.
The function can be deployed in security systems where cameras recognize bank logos in real-time. This allows security teams to monitor and identify legitimate bank premises amidst potential counterfeit establishments, thereby enhancing overall security.
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 banks 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.