A pretrained length of truck in feet classifier that sorts an image into one of 10 categories — the length of the truck in feet. Use the length of truck 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 14 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": "1-5 Feet",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 length of truck 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.
Companies in the logistics sector can utilize the truck length identifier to monitor the dimensions of their freight vehicles. By accurately identifying the length of trucks, businesses can optimize load capacity, ensure compliance with length regulations, and reduce transportation costs.
City planners and traffic management authorities can use truck length data to design effective roadways and traffic signals. This information helps in managing traffic flows, determining appropriate lane usage, and implementing restrictions for oversized vehicles to enhance road safety.
Insurance companies can employ this function during underwriting to assess risk related to commercial vehicle coverage. Understanding vehicle dimensions allows insurers to evaluate potential liability and adjust premiums accordingly based on the truck's length and its associated risks.
Smart parking systems can benefit from the length identifier feature by offering tailored parking solutions for trucks. By analyzing the truck lengths, parking managers can create designated spaces that optimize usage while accommodating larger vehicles, thus preventing over-occupancy in standard spaces.
Fleet management software can integrate the length identifier to enhance vehicle tracking and management features. With accurate truck length data, fleet managers can improve route planning, reduce fuel consumption, and ensure compliance with size-related regulations in different jurisdictions.
Construction companies can utilize the length information of delivery trucks to effectively plan site access and logistics. This helps in ensuring the safe maneuvering of trucks on-site and organizing the scheduling of deliveries based on vehicle sizes, thereby minimizing disruptions.
E-commerce platforms that deal with logistics can use the truck length identifier to validate seller or buyer claims regarding their shipping capacity. This ensures that users receive accurate information, reducing disputes over shipping arrangements and enhancing customer satisfaction.
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 length of truck 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.