A pretrained boat make classifier that sorts an image into one of 10 categories — what type of boat it is. Use the boat make 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 43 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": "Alumacraft",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 boat make 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 boat make identifier to verify claims related to boat accidents or damages. By analyzing images submitted for claims, insurers can ensure the reported make matches the actual boat involved, preventing fraudulent claims.
Market researchers can utilize the classification function to analyze images of boats in social media posts or online marketplaces. By identifying boat makes, researchers can gather data on consumer preferences and emerging trends in the boating industry, allowing for more informed product development and marketing strategies.
Online retail platforms can implement this image classification feature to automatically categorize and tag boats listed for sale by their make. This enhances user experience by helping buyers quickly find the specific brands they are interested in, while also streamlining inventory management.
Boat dealerships can leverage this technology to maintain an accurate inventory database. By automatically identifying and classifying the makes of boats in their showroom or storage, dealers can ensure they have an up-to-date catalog for sales analytics and customer inquiries.
Organizations focused on marine conservation can use the identifier to monitor and track specific boat makes in protected waters. This information can help enforce regulations against unlawful activities conducted by certain boat brands, aiding in the protection of marine ecosystems.
Regulatory bodies can utilize the boat make identifier to assess compliance with safety regulations. By comparing images of boats to their registered makes, authorities can ensure that safety equipment and certifications are in place for various boat brands, promoting safer boating practices.
Law enforcement agencies can employ the classification function to assist in identifying stolen boats. By analyzing images from surveillance footage or public reports, they can quickly ascertain the make of a boat, improving the chances of recovery and reducing the time needed for investigations.
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 boat make 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.