A pretrained car maker by taillight classifier that sorts an image into one of 10 categories — what car maker it is based on the taillight design. Use the car maker by taillight 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 51 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 taillight 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 utilize the taillight identifier to quickly verify the make and model of a car involved in an accident. This can expedite the claims process, ensuring that the correct policies are applied and reducing potential fraud.
Law enforcement agencies can implement the taillight identifier in traffic cameras to automate the recognition of vehicle makes when issuing citations for traffic violations. This can enhance efficiency in enforcing traffic laws and provide accurate data for faster processing of fines.
Car dealerships can use the taillight identifier to streamline their inventory management by quickly identifying the makes and models of vehicles as they arrive or leave the lot. This helps maintain an accurate database and manage stock levels effectively.
Security companies can deploy the taillight identifier in surveillance systems to identify vehicles reported as stolen. This can offer real-time alerts to law enforcement and improve the chances of recovering stolen vehicles.
Market researchers can analyze traffic data using taillight identification to gauge the popularity and distribution of different car makes in various regions. This information can provide insights for brands looking to target specific markets or adjust production strategies.
Companies managing large fleets can integrate the taillight identifier into their tracking systems to monitor vehicle makes. This can assist in maintenance scheduling, ensuring that appropriate parts and services are procured for specific vehicle types.
Smart city initiatives can use the taillight identifier to monitor traffic patterns by analyzing the makes of vehicles passing through certain areas. This helps inform urban planning decisions related to traffic flow, congestion, and public transport routes.
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 taillight 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.