A pretrained tile pattern type classifier that sorts an image into one of 10 categories — what type of tile pattern it is. Use the tile pattern type 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 45 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": "Abstract",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tile pattern type 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 false image classification function can be utilized to identify and analyze tile patterns in various interior design projects. By categorizing different styles, designers can better understand trending patterns and incorporate them into their designs, ensuring they stay ahead of consumer preferences.
Tile manufacturers can implement this function as part of their quality control process. By automatically classifying tile patterns, it can help in detecting discrepancies or defects in production patterns, leading to higher quality products and reduced waste.
Home renovation firms can use the tile pattern identifier to suggest tile options based on the existing decor of a space. By identifying current patterns, companies can recommend complementary designs to homeowners, enhancing the aesthetic appeal of their renovations.
E-commerce platforms selling tiles can leverage this function to analyze customer-generated images on social media and review sites. By identifying popular tile patterns in user photos, retailers can adjust their inventory and marketing strategies to align with consumer interests.
Developers of augmented reality applications for home improvement can use the false image classification function to recognize floor or wall tiles in a user’s home. This allows the app to suggest new tile options based on existing styles, enriching the user experience and improving decision-making.
Smart home systems can incorporate the identifier to monitor tile patterns for wear and tear. By recognizing specific tile styles, maintenance alerts can be provided to homeowners, ensuring timely care and prolonging the lifespan of their flooring.
Museums and cultural institutions can utilize the tile pattern identifier to categorize and document historical tile designs in their collections. This function can assist researchers and curators in preserving cultural heritage and enhancing educational exhibits that showcase the evolution of tile patterns over time.
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 tile pattern type 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.