A pretrained decor style classifier that sorts an image into one of 10 categories — what decor style you should use. Use the decor style 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 25 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": "Art Deco",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 decor style 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 serve as a valuable tool for interior designers by categorizing images based on decor styles, such as modern, vintage, or bohemian. Designers can quickly curate references and create mood boards that align with a client's vision, enhancing the design process.
Online retailers can use the decor style identifier to automatically tag products in their inventory based on the styles they represent. This tagging system enables more accurate search filtering for customers, improving the shopping experience and boosting sales.
Influencers and content creators in the home decor niche can leverage this function to categorize their images by decor style. This helps them craft content tailored to their audience's interests, increasing engagement and follower retention.
Real estate professionals can use this function to analyze property images and classify them by their interior style. This classification allows them to create targeted marketing strategies that appeal to specific buyer demographics looking for homes with particular aesthetic preferences.
A home improvement or decor app can utilize this function to provide personalized styling recommendations based on users' uploaded images. By identifying the decor style, the app can suggest complementary furniture and accessories, streamlining the decorating process for users.
Market researchers can employ the decor style identifier to analyze trends by examining images across social media platforms or design websites. By aggregating data on popular decor styles, businesses can make informed decisions about product development and marketing strategies.
Educational platforms and online courses can use this function to help students learn about different decor styles. By providing examples and categorizing them correctly, students can engage with practical exercises that reinforce their understanding of design principles and aesthetics.
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 decor style 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.