A pretrained car maker by side mirror classifier that sorts an image into one of 10 categories — what car maker produced the vehicle. Use the car maker by side mirror 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 42 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 side mirror 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 be utilized in quality assurance for automotive manufacturers by verifying the correct side mirror installation on vehicles. By quickly identifying if the installed side mirror matches the predetermined standards of a specific car model, it helps prevent defects and maintains brand integrity.
Insurance companies can leverage this function during the claim assessment process. By verifying the car make and model through the side mirror design, they can determine if repairs align with insured values and ensure that claims are processed accurately.
Salvage yards can implement this functionality to efficiently identify and categorize incoming vehicles based on their side mirrors. This enhances inventory management, allowing for quicker sorting and retrieval of parts, while improving customer service.
Automotive market analysts can use this function to gain insights into the prevalence and distribution of specific car models within a region by analyzing the side mirror styles seen on the road. This data can inform strategies for dealerships and manufacturers about which models are popular in certain demographics.
Registration authorities can incorporate this technology in their vehicle registration processes. By confirming the make and model of a vehicle through its side mirror, they can streamline registration checks and enforce compliance with local laws.
Traffic safety organizations can utilize this function in road safety audits to identify the most common types of vehicles on the roads. By ensuring that specific car models are on the radar, they can better allocate resources for safety campaigns focused on high-risk models.
Fleet management companies can employ this side mirror identification function to monitor and manage their vehicle fleet accurately. By automatically categorizing vehicles and ensuring compliance with company standards, they can enhance efficiency and reduce maintenance costs.
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 side mirror 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.