A pretrained cabinet style classifier that sorts an image into one of 10 categories — what style of cabinet it is. Use the cabinet 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 27 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 cabinet 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.
Retailers can utilize the cabinet style identifier to analyze customer preferences in furniture design. By classifying images of cabinets based on style, retailers can curate inventory that aligns better with trending designs, enhancing customer satisfaction and sales.
Online marketplaces can employ the identifier to automatically tag and categorize cabinet images by style. This automation can improve search functionality, helping customers find their desired styles quickly while enhancing the overall shopping experience.
Interior designers can use the cabinet style identifier to streamline their projects. By quickly identifying cabinet styles in reference images, designers can ensure their selections align with clients' aesthetic preferences and project themes, saving time in the selection process.
AR applications for home improvement can generate style recommendations based on the identified cabinet styles in user-uploaded images. This feature allows users to visualize how certain styles would look in their spaces, aiding in decision-making and boosting user engagement.
Blogging platforms about home décor can automate image classification for articles. By identifying cabinet styles in images, the platform can generate relevant tags and descriptions, improving SEO and user reach without extensive manual input.
Analysts can leverage the identifier to track trends in cabinet styles over time by analyzing large datasets of branded images. This insight can inform manufacturers and retailers about evolving consumer preferences, enabling them to adapt their product lines accordingly.
Manufacturers can implement the cabinet style identifier in quality control processes. By analyzing product images during production, businesses can ensure compliance with design specifications and reduce the rate of style-related product returns.
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 cabinet 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.