Pretrained computer vision classifier
Identify the color of a cabinet with one API call.
A pretrained the color of a cabinet classifier that sorts an image into one of 10 categories — the color of a cabinet. Use the the color of a cabinet API immediately, no training required, then adapt it to your own data when you need more.
Try the the color of a cabinet classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this the color of a cabinet classifier recognizes
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.
Call the the color of a cabinet API
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;Example response
{
"labelName": "Beige",
"labelId": "label_...",
"confidence": 0.92
}Under the hood
Trained on a Nyckel-curated dataset covering 10 the color of a cabinet 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.
Where teams use the color of a cabinet classification
Virtual Interior Design
Interior design software can use the cabinet color identifier to allow users to visualize different cabinet colors in their virtual spaces. By uploading images of their rooms, customers can receive color matching suggestions that complement other elements, enhancing the overall aesthetic of their home.
E-commerce Product Recommendations
An e-commerce platform can integrate the cabinet color identifier to improve product recommendations for furniture and home decor. By analyzing users' uploaded images, the system can suggest products that match or contrast effectively with the cabinet color, increasing the likelihood of a sale.
Home Renovation Services
Home renovation companies can utilize the identifier to streamline their color selection process for cabinetry. By having an automated system to recognize existing cabinet colors, company representatives can provide tailored design options that align with clients' preferences and existing decor.
Augmented Reality Applications
AR applications can leverage the cabinet color identifier to create immersive shopping experiences for consumers. By allowing users to virtually place different cabinet styles and colors in their homes using real-time image analysis, customers can make informed decisions on color and style before purchasing.
Furniture Compatibility Analysis
Retailers can use this image classification to assess how well different pieces of furniture will match or complement existing cabinetry in a customer's home. By analyzing the cabinet's color, the system can suggest compatible sofa, chair, or table options, thereby enhancing customer satisfaction.
DIY Home Improvement Tools
Home improvement mobile applications can incorporate the cabinet color identifier to assist users in selecting paint or stain colors. By taking a photo of their existing cabinets, users can receive recommendations for the best color matches to refresh or enhance their kitchen or storage areas.
Market Trend Analysis
Companies within the cabinetry and home design industries can utilize aggregated data from the cabinet color identifier to analyze market trends. By understanding popular cabinet colors and styles through image classification, they can better tailor their inventory and marketing strategies to meet consumer demand.
Common questions
What's the difference between a zero-shot and a Nyckel-trained classifier?
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.
How do I know whether this will work for my application?
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.
What happens when it makes a mistake?
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.
Do I need training data to get started?
No. This the color of a cabinet 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.
What does it cost to try?
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.
Related classifiers
Ready to classify the color of a cabinet at scale?
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.