A pretrained motorcycle make by engine classifier that sorts an image into one of 10 categories — what make of motorcycle it is. Use the motorcycle make by engine 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 20 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 engine 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 use the motorcycle make by engine identifier to verify the make of a motorcycle during claim processing. By accurately identifying the motorcycle, insurers can streamline verification, assess coverage, and reduce fraudulent claims based on mismatches between reported and actual motorcycle details.
Motorcycle dealerships can implement this function to optimize their inventory management. By automatically categorizing motorcycles by make and engine type, dealerships can enhance their inventory accuracy, quickly identify popular models, and manage restocks based on consumer demand trends.
Law enforcement agencies can deploy the motorcycle make by engine identifier in conjunction with vehicle recovery systems. This identification helps officers swiftly verify the ownership and legitimacy of recovered motorcycles, facilitating quicker returns to rightful owners and aiding in criminal investigations.
Government agencies tasked with vehicle registration can use this identifier to ensure compliance with regulations. By cross-referencing the motorcycle make and engine data during the registration process, agencies can mitigate registration fraud and ensure that all vehicles on the road meet safety and environmental standards.
Motorcycle manufacturers can leverage the identifier to gather insights on market trends related to specific engine types and motorcycle makes. By analyzing this data, companies can tailor their marketing strategies to target specific demographics, optimize their product lines, and increase sales effectiveness.
Aftermarket part suppliers can utilize the motorcycle make by engine identifier to enhance their product offerings. By accurately identifying the compatible motorcycles for various parts, suppliers can improve customer satisfaction, reduce return rates, and enhance the overall shopping experience.
Online platforms and forums dedicated to motorcycle enthusiasts can use this function to categorize motorcycles based on their engine make. This allows users to easily find and share information, reviews, and recommendations related to specific types of motorcycles, fostering a sense of community and support among riders.
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 engine 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.