A pretrained width of road lane in feet classifier that sorts an image into one of 10 categories — the width of the road lane in feet.. Use the width of road lane in feet 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": "1 Foot",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 width of road lane in feet 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 use case involves analyzing the width of road lanes to identify unsafe conditions that could lead to accidents. By classifying lanes sufficiently wide or too narrow, city planners can make informed decisions about road improvements and traffic safety measures.
Urban planners can use the width classification to design new roads that comply with regulatory standards. Accurate lane width measurements help ensure that new infrastructures provide safe and comfortable travel for vehicles and pedestrians.
Autonomous vehicle systems can leverage lane width data to improve navigation accuracy. By understanding lane dimensions, vehicles can optimize their driving algorithms for safe lane changes and proper positioning within lanes.
Municipalities can assess lane widths to prioritize road maintenance efforts. Roads with widths that no longer meet specifications may require immediate maintenance to prevent wear and tear that impacts transportation efficiency.
Transportation analysts can analyze lane widths to identify potential bottlenecks in traffic flow. By correlating lane width data with traffic patterns, solutions can be proposed to alleviate congestion based on lane optimization strategies.
Insurance companies can use lane width classification in evaluating risks associated with different road conditions. Understanding lane widths aids in determining policy premiums and assessing potential claims related to accidents that may result from inadequate road specifications.
Environmental organizations can utilize lane width data for studies related to road expansion and its effects on ecosystems. By classifying existing lane sizes, impacts on wildlife corridors and local habitats can be better understood during the planning of new road constructions.
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 width of road lane in feet 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.