A pretrained the color of a rug classifier that sorts an image into one of 10 categories — the color of a rug it is. Use the the color of a rug 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 48 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": "Amber",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 the color of a rug 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 integrate the color identifier to enhance their product filtering options. Customers searching for rugs can easily narrow down their choices by selecting specific colors, improving user experience and increasing sales conversions.
Home decor retailers can use the color identification function to automate their inventory categorization. By tagging rugs with the correct color upon arrival, retailers can maintain an organized inventory, making it easier for staff to locate items and manage stock levels.
Marketing teams can leverage the function to create targeted email campaigns based on users' color preferences. By analyzing customer behavior and preferences, businesses can send personalized promotions for rugs that match customer-selected colors.
Interior design apps can utilize the color identifier to offer users a virtual try-on experience. By allowing users to visualize how specific rug colors would look in their spaces, customers are more likely to make informed purchasing decisions.
Businesses can analyze rug color data to identify emerging trends in home decor. By understanding popular colors, companies can adjust their product lines accordingly, ensuring they remain competitive and relevant in the market.
By integrating the color classification system into customer support queries, businesses can streamline responses related to rug color issues. This direct approach can enhance customer satisfaction by providing quick and accurate assistance regarding product specifications.
Manufacturers can use color identification to synchronize their production processes with market demand. By understanding which rug colors are in higher demand, they can optimize material sourcing, reduce waste, and improve overall operational efficiency.
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 rug 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.