A pretrained what material a floor is made from classifier that sorts an image into one of 10 categories — what material the floor is made from. Use the what material a floor is made from 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 18 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": "Bamboo",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 what material a floor is made from 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 assist homeowners in identifying the material of their existing floors, enabling them to make informed decisions about renovations or restorations. By accurately determining the type of flooring, homeowners can select compatible materials for repairs or enhancements that match the aesthetic and functional qualities of the original.
Real estate agents and appraisers can use this tool to quickly assess the materials used in flooring during property evaluations. Understanding the type of flooring can impact property value assessments and marketing strategies, as certain materials are perceived as more desirable than others.
Retailers can implement this function as part of their customer interaction tool to provide personalized flooring material recommendations. By identifying the existing flooring, customers can receive tailored product suggestions that complement their home’s design and existing materials.
Construction companies can integrate this identifier into their quality control processes to verify that the materials installed match what was specified in the project plans. This ensures that the flooring materials used meet the required standards and specifications, reducing costly errors and rework.
Insurance adjusters can utilize this function to evaluate claims related to flooring damage. By identifying the material, they can better assess replacement costs and determine the coverage relevant to the specific type of flooring involved in the claim.
Interior designers can employ this tool to quickly gather information about a client’s existing flooring materials during consultations. This understanding allows them to propose cohesive design solutions that consider both the aesthetic elements and practical requirements of the flooring materials present.
Environmental agencies or organizations can use this identifier to assess the materials used in flooring during sustainability audits. By knowing the type of flooring in various buildings, they can evaluate the environmental impact and promote sustainable flooring options among businesses and homeowners.
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 what material a floor is made from 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.