A pretrained the color of a tile classifier that sorts an image into one of 10 categories — the color of a tile it is. Use the the color of a tile 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 21 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 tile 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 application can automate the quality control process in tile manufacturing by identifying color discrepancies in tiles. By ensuring only tiles that meet color specifications are approved, manufacturers can enhance product consistency and reduce waste.
Interior designers can utilize this image classification function to quickly assess tile colors in various environments. This tool can help in selecting complementary tiles for flooring or backsplashes by providing accurate color classifications, aiding in more effective design choices.
Retailers can implement this function in inventory management systems to categorize tiles based on color. This helps streamline the stocking process and enhance customer experience, making it easier to find specific tile colors in the store.
Online retailers can use the function to automatically classify and tag tile images by color. This results in improved searchability for customers, allowing them to filter tiles based on color preferences and boosting sales through better product organization.
Manufacturers and marketers can analyze color trends in tiles using this classification tool. By tracking the popularity of different tile colors over time, businesses can make informed decisions on production and marketing strategies based on current consumer preferences.
Integrating this image classification function into augmented reality apps can allow users to visualize different tile colors in their own spaces. This enhances customer engagement and helps them make informed purchasing decisions while providing a more immersive shopping experience.
This function can be used in automated systems for cataloging tiles in showrooms or warehouses. By efficiently classifying tiles by color, businesses can maintain organized inventories and simplify the retrieval process for both employees and customers.
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 tile 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.