A pretrained tanker make classifier that sorts an image into one of 10 categories — what type of tanker make it is. Use the tanker 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 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": "Aframax",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tanker 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.
The tanker make identifier can assist fleet managers in maintaining an optimized fleet by correctly identifying the brand and model of tankers in real-time. This information enables better tracking of maintenance schedules, enhances inventory management for parts, and improves operational planning based on the specific features of each tanker model.
Insurance companies can use the tanker make identifier to confirm the type of tanker being insured. By accurately identifying the tanker make, insurers can assess risk factors more effectively and tailor policies that meet the specific needs associated with different brands and models.
Regulatory bodies can utilize the tanker make identifier to ensure that tankers comply with safety and environmental regulations that vary by make and model. By automating the identification process, it simplifies compliance audits and reporting, ensuring that only compliant vehicles are on the road.
Companies in the oil and gas sector can employ the tanker make identifier to improve supply chain transparency. By tracking which tankers are carrying their products, firms can ensure accountability and traceability throughout the distribution process.
Businesses can leverage the tanker make identifier for market analysis to understand which brands and models are most prevalent in the industry. This data can inform strategic decisions, such as marketing initiatives and partnerships, based on the competitive landscape.
Service providers can enhance their offerings by using the tanker make identifier to recommend tailored maintenance and repair services based on the specific characteristics of different tanker models. This ensures that clients receive the most appropriate solutions, leading to improved service efficiency.
The tanker make identifier can play a crucial role in detecting potential fraud in insurance claims related to tanker accidents. By confirming the make of the tanker involved against registered information, insurers can identify discrepancies that may indicate fraudulent activity, reducing losses and maintaining policy integrity.
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 tanker 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.