A pretrained motorcycle make by seat classifier that sorts an image into one of 10 categories — what make of motorcycle it is. Use the motorcycle make by seat 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 15 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": "Aprilia",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 motorcycle make by seat 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.
Insurance companies can utilize the motorcycle make by seat identifier to accurately classify motorcycles during policy underwriting. By identifying the make based on seat style, insurers can better assess risk profiles and adjust premiums accordingly.
Businesses in the motorcycle industry can leverage this function to analyze consumer trends related to motorcycle seat designs. By categorizing makes based on seating, companies can tailor their product offerings to meet the identified preferences in specific market segments.
Law enforcement agencies can use the motorcycle make by seat identifier to assist in recovering stolen motorcycles. By accurately identifying the make through visual features, authorities can streamline efforts to trace and return stolen vehicles to their owners.
Dealerships can implement this identifier in their inventory management systems to automatically classify motorcycles based on seat design. This would simplify stock organization, improve search functionality, and enhance sales processes for both staff and customers.
E-commerce platforms can utilize the motorcycle make by seat identifier to enhance their recommendation systems. By understanding which makes correlate with specific seat designs, they can recommend similar motorcycles, enhancing user experience and potentially increasing sales.
Custom motorcycle builders can benefit from this function by integrating it into their design tools. This allows builders to suggest the appropriate make based on seat configurations, leading to more personalized and appealing customizations for clients.
Motorcycle event organizers can use the identifier to streamline registration processes. By categorizing attendees' motorcycles by make and seat type, organizers can better plan layout, amenities, and competitions tailored to varied motorcycle types at events.
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 motorcycle make by seat 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.