A pretrained house damage classifier that sorts an image into one of 2 categories. Use the house damage 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 2 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": "House Not Damaged",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 house damage 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.
The 'house damage' identifier will be used by insurance companies to facilitate faster and accurate processing of home insurance claims. This tool can review images submitted by claimants, and identify cases of actual structural damage, reducing fraudulent activities and subjectivity in claim assessments.
Government authorities and NGOs can apply the image classification function in creating a snapshot of the extent of damage following natural disasters like hurricanes, earthquakes, or floods. The damage identification can guide with prioritizing areas that require immediate assistance.
Home inspection firms can use this application to expedite and enhance the accuracy of their services. By processing images of homes, the tool aids in identifying issues that might not be immediately visible to the human eye.
The tool could be used by real estate investment firms or individual investors to determine the extent of damage to a property. This assists in making informed decisions regarding the cost of potential repairs and the property's total value.
This solution can be used by home maintenance and repair companies to anticipate the nature of the job ahead. By analyzing images sent by clients, businesses can estimate the extent of the damage and develop an accurate job estimate.
Just after construction or refurbishment, construction enterprises can use the function to confirm the quality of their work. It can identify any structural mishaps, thereby ensuring that any issues are addressed promptly.
In rental properties, the 'house damage' identifier can be applied in identifying property damage against tenant's security deposits. Landlords can use this tool to document damage before the tenant moves out and objectively determine what part of the security deposit should be retained for repairs.
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 house damage 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.