A pretrained oriental rug pattern classifier that sorts an image into one of 10 categories — the design and colors in each oriental rug pattern.. Use the oriental rug pattern 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 30 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": "Abrash",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 oriental rug pattern 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.
Online retailers can implement the 'oriental rug pattern' identifier to automatically verify and classify images of rugs listed on their platforms. This helps ensure that the correct patterns are displayed next to corresponding descriptions, minimizing customer dissatisfaction and return rates.
Appraisers and antique dealers can leverage the identification function to classify and authenticate oriental rugs. By confirming the patterns, they can provide better valuations and insights into the rugs’ historical significance and market value.
Interior design firms can use the oriental rug pattern identifier to recommend complementary decor and furnishings based on a recognized rug pattern. This targeted approach can enhance design proposals, making them more appealing to clients.
Auction houses can employ the image classification tool to identify and flag rugs that may be misrepresented or counterfeit. Ensuring authenticity can protect buyers and uphold the integrity of the auction process.
Retailers can use the identifier to analyze customer preferences based on identified rug patterns. This data can help develop targeted marketing campaigns and promotions that resonate with specific customer segments.
Manufacturers and wholesalers can utilize the tool to streamline the categorization of their rug inventory. By automatically tagging patterns, they can improve inventory tracking and enhance search functionalities for customers.
Museums and cultural organizations can apply the identifier to help catalog and preserve heritage rugs within their collections. This classification can facilitate better education efforts and promote awareness of cultural significance in textiles.
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 oriental rug pattern 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.