A pretrained car maker by headlight classifier that sorts an image into one of 10 categories — what car maker it is. Use the car maker by headlight 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 47 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": "Acura",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 car maker by headlight 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.
Car manufacturers can leverage the headlight identification function to tailor marketing strategies based on vehicle make, targeting specific audiences with customized advertising. By understanding which models are popular based on their headlights, marketers can develop campaigns that resonate more effectively with potential buyers.
Insurance companies can utilize headlight classification to expedite the claims process by quickly identifying the car make involved in accidents. This function helps assess damage costs and ensures that claims are processed accurately, reducing fraud and improving customer satisfaction.
Online platforms for car enthusiasts can implement this function to help users identify vehicles based solely on headlight designs. This feature fosters engagement within the community, allowing users to share insights, tips, and discussions surrounding specific car brands and models.
Third-party services that verify the authenticity of vehicles can use the headlight classification function as part of their inspection process. By confirming the make through headlights, these services can reduce instances of fraud and provide buyers with confidence in their purchases.
Traffic management systems can incorporate this identification technology for monitoring and analyzing traffic patterns involving different car brands. Understanding the distribution of vehicles on the road can aid urban planners and municipalities in improving traffic flow and infrastructure.
Smart parking systems can use the headlight classification functionality to optimize space allocation based on car makes, helping to streamline the customer experience. By categorizing vehicles and their characteristics, parking managers can enhance service efficiency and data-driven decision-making.
Businesses managing multi-brand fleets can implement this function to simplify vehicle tracking and maintenance scheduling. Identifying vehicles by headlights enables fleet managers to quickly access model-specific information, ensuring that appropriate care and resources are allocated to each vehicle type.
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 car maker by headlight 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.