A pretrained the color of a countertop classifier that sorts an image into one of 10 categories — the color of a countertop. Use the the color of a countertop 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 13 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": "Beige",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 the color of a countertop 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 integrated into interior design software to assess and suggest countertop colors that complement existing design elements in a space. Designers can upload images and receive a color analysis to facilitate client consultations and improve design outcomes.
Real estate agencies can use the false image classification function to automatically identify and highlight countertop colors in listing photographs. By providing color details, agents can attract potential buyers looking for specific aesthetics in their future homes.
Contractors and renovation consultants can leverage this function to offer clients tailored advice on countertop options. By analyzing current countertop colors in a kitchen or bathroom setting, they can recommend paint shades or decor that harmonize with the identified colors.
Online retailers selling home goods can employ this function to analyze user-uploaded images of kitchens or bathrooms. Based on identified countertop colors, the platform can suggest matching or complementary cabinetry, appliances, or decor items, enhancing the shopping experience.
Virtual staging companies can utilize this identifier to improve the realism of digitally staged properties. By accurately identifying countertop colors, they can select appropriate decor and accessories that effectively match the countertop, leading to more appealing staged images.
Suppliers and manufacturers of countertops can incorporate this function to gather insights about popular colors in homes. By analyzing real estate images, they can adjust inventory and marketing strategies based on trends in countertop color preferences.
DIY enthusiasts can use apps featuring this function to better understand how different countertop colors can influence kitchen aesthetics. By uploading images, users can receive suggestions for painting or refinishing plans that align with their existing countertop colors.
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 the color of a countertop 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.