A pretrained motorcycle brands classifier that sorts an image into one of 10 categories — what motorcycle brand it is. Use the motorcycle brands 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 25 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 brands 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 help market analysts identify the specific brands of motorcycles seen in various regions. By aggregating this data, businesses can gain insights into consumer preferences and market trends, allowing for targeted marketing strategies.
Motorcycle dealerships can utilize this image classification function to automate the process of identifying motorcycle brands in their inventory. This automation can streamline inventory management, ensuring accurate records and enabling better stock control.
Insurance companies can leverage this function to quickly identify motorcycle brands in images submitted during claims processing. By automating brand recognition, the claims process can be expedited, reducing manual effort and improving customer satisfaction.
Brands can implement this technology to monitor social media platforms for user-generated content featuring their motorcycles. By tracking mentions and images of specific motorcycle brands, companies can engage with customers and analyze brand sentiment in real time.
With the ability to classify motorcycle brands, marketing firms can create personalized advertising campaigns based on the specific brands of motorcycles customers are interested in. This targeted approach can lead to higher engagement and conversion rates.
Law enforcement and security firms can utilize this function as part of a surveillance system to detect stolen motorcycle brands in real-time. By quickly identifying brands associated with theft reports, it enhances the chances of recovery and deters future thefts.
Manufacturers of motorcycle accessories can use this image classification to understand the distribution of different motorcycle brands in the market. This information will aid in R&D efforts to design accessories that fit a broader range of popular motorcycle models, increasing sales opportunities.
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 brands 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.