A pretrained countertop material classifier that sorts an image into one of 10 categories — what type of countertop material it is. Use the countertop material 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 20 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": "Engineered Stone",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 countertop material 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 employed by manufacturers to assess the quality of countertop materials used in their production. By identifying the material type accurately, companies can ensure compliance with industry standards and deliver products that meet customer expectations for durability and aesthetics.
Interior design software can integrate this function to enable designers to visualize how different countertop materials will look in a given space. By providing accurate material classifications, the tool can help clients make informed decisions about style and color combinations.
Companies that focus on kitchen renovations can utilize this function to quickly assess existing countertop materials. By identifying these materials, they can provide tailored recommendations for replacement options that match the client's style and budget.
Home improvement retailers can use this function to streamline their inventory management for countertop materials. By accurately identifying materials in stock, they can optimize product listings, improve stock accuracy, and provide better customer service.
Manufacturers can leverage this function to monitor production processes and ensure that the correct materials are being used consistently. This accuracy can reduce waste and improve overall production efficiency.
Appliance and home goods companies can incorporate this function to support warranty and maintenance claims. By accurately classifying materials, they can provide customers with the correct care instructions and any warranties specific to the countertop type.
Sustainable building firms can use this function to assess the environmental impact of the materials used in a project. By classifying materials accurately, they can evaluate life-cycle impacts and help clients choose more eco-friendly options for their interiors.
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 countertop material 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.