A pretrained roof condition classifier that sorts an image into one of 10 categories — the condition of the roof.. Use the roof condition 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 10 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": "Damaged",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 roof condition 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 used by real estate agents to quickly assess the condition of a roof during property evaluations. By classifying the roof's condition based on images, agents can provide potential buyers with accurate information about necessary repairs, thereby helping in negotiations.
Insurance companies can employ the roof condition identifier to evaluate claims related to storm damage. By analyzing images submitted by policyholders, insurers can efficiently determine the validity of claims and expedite the underwriting process.
Property managers can leverage this tool to schedule timely maintenance for residential or commercial properties. By regularly assessing roof conditions, they can preempt costly repairs and ensure tenant satisfaction with workplace or living conditions.
Construction firms can use this function for quality assurance checks on newly installed roofs. By validating the roof’s condition against project specifications, they can ensure compliance with safety standards and prevent future liabilities.
Municipalities and emergency responders can use the roof condition identifier to assess damage after natural disasters. Quickly obtaining roof condition data allows for prioritized resource allocation and more efficient disaster recovery efforts.
Solar energy companies can utilize the roof condition classification to determine the suitability of roofs for solar panel installations. By ensuring roofs are in good condition, these companies can avoid potential liabilities and guarantee the longevity of their installations.
Environmental consultants can apply this function to assess the condition of roofs in areas vulnerable to erosion and other environmental factors. The findings can inform sustainability reports and contribute to efforts aimed at promoting greener building practices.
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 roof condition 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.