A pretrained length of fence in feet classifier that sorts an image into one of 10 categories — the length of the fence in feet based on the provided image.. Use the length of fence 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 length of fence 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 function can be utilized by fence installation companies to accurately assess the length of fencing required for various properties. By inputting images of the property, the algorithm can provide a precise measurement, allowing for efficient planning and pricing.
Real estate agents can use this feature to quickly determine the length of fence lines for properties they are listing. Accurate measurements enhance property descriptions and provide better visuals for potential buyers, improving engagement and sales prospects.
Landscape architects and designers can leverage this function to evaluate existing fence conditions during appraisal processes. Knowing the length of the fence helps in planning and budgeting for renovations or new installations related to landscaping projects.
Insurance companies can incorporate this function to automate the inspection process for properties with fences. By determining the fence length from images, underwriters can quickly assess risk factors related to property boundaries and liability coverage.
Farmers and agricultural land managers can employ this feature to monitor and manage perimeter fencing on their properties. It aids in ensuring that livestock remains contained while providing data for compliance with local regulations regarding land usage.
City planners can use the functionality to survey residential and commercial areas for zoning and development purposes. Accurate identification of fence lengths facilitates urban design considerations, enhancing overall planning efficiency.
DIY home improvement applications can integrate this function to help users measure their existing fences without needing to do manual calculations. This feature can guide homeowners in purchasing materials for repairs or new fence installations based on accurate data derived from images.
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 fence 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.