A pretrained wall condition classifier that sorts an image into one of 10 categories — the condition of different types of walls. Use the wall 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 15 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": "Cracks",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 wall 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 utilized by property management companies to regularly assess the wall conditions in residential and commercial buildings. By identifying false classifications, they can prioritize maintenance efforts and allocate resources more effectively, ultimately improving tenant satisfaction and reducing repair costs.
Insurance adjusters can deploy this function to verify wall conditions during claims investigations. By enhancing the accuracy of damage assessments, they can minimize fraudulent claims and ensure legitimate cases are handled promptly and fairly, which improves operational efficiency.
Contractors can implement this function during quality assurance inspections to ensure that walls meet specified condition standards. By accurately identifying wall anomalies early in the construction process, it helps prevent costly rework and project delays.
Integrating this function into smart home systems allows homeowners to receive real-time updates on their property’s wall conditions. This proactive approach can alert homeowners to potential issues such as moisture or structural deterioration before they escalate into larger problems.
Real estate agents can leverage this tool to assess property conditions more accurately before listing homes for sale. By providing potential buyers with reliable wall condition reports, agents can enhance buyers' confidence and improve the sales process.
City planners can use this function to evaluate the condition of exterior walls in public buildings and infrastructure. By understanding wall conditions in urban settings, they can prioritize renovation projects, contributing to safer and more aesthetically pleasing environments.
Environmental agencies can utilize this technology to assess wall damage caused by environmental factors such as humidity, temperature changes, or pollution. Identifying how these conditions affect structures helps in developing better guidelines and policies for building resilience and sustainability.
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 wall 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.