A pretrained wheel alignment classifier that sorts an image into one of 10 categories — the alignment status of your vehicle's wheels.. Use the wheel alignment 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 24 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": "Aligned",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 wheel alignment 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 false image classification function can help automotive specialists quickly identify misaligned wheels during inspections. By analyzing images of vehicles, the system can flag potential issues, allowing technicians to perform targeted checks instead of thorough, time-consuming evaluations.
Service centers can integrate the wheel alignment identifier to streamline workflow. By automating the identification process, technicians can prioritize vehicles needing alignment services, improving overall service speed and customer satisfaction.
The function can be used to generate reports on wheel alignment trends for different vehicle models. These insights would help manufacturers and service providers understand common alignment issues, leading to proactive maintenance recommendations.
The classification function could serve as an educational tool for training new technicians in identifying wheel alignment issues. By using real-world images, trainees can learn to distinguish between properly aligned and misaligned wheels, enhancing their diagnostic skills.
Insurance companies can use the false image classification function to evaluate claims related to vehicle damage. By accurately identifying wheel alignment problems caused by accidents, assessors can make more informed decisions on coverage and repairs.
Fleet managers can implement this function to monitor their vehicles' alignment status. By analyzing the classification results, they can schedule maintenance proactively, reducing downtime and extending the lifespan of the fleet.
E-commerce platforms selling car maintenance products can utilize the image classification function to suggest alignment services based on photographs submitted by customers. This targeted approach can enhance customer engagement and increase sales of alignment-related products or services.
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 wheel alignment 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.