Pretrained computer vision classifier

Identify lighting fixtures with one API call.

A pretrained lighting fixtures classifier that sorts an image into one of 10 categories — what type of lighting fixture it is. Use the lighting fixtures API immediately, no training required, then adapt it to your own data when you need more.

Pretrained · Nyckel-trained 10 labels out of the box Image input

Try the lighting fixtures classifier

Drop in a photo and get the prediction back. No signup, no setup.

What this lighting fixtures classifier recognizes

A sample of the 31 labels this pretrained classifier chooses between.

Artistic
Basic
Bright
Chandelier
Classic
Colorful
Contemporary
Customizable
Decorative
Designer

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 lighting fixtures API

Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:

API quick start
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"}'

Example response

{
  "labelName": "Artistic",
  "labelId": "label_...",
  "confidence": 0.92
}

Under the hood

Model type
Nyckel-trained

Trained on a Nyckel-curated dataset covering 10 lighting fixtures categories, served on Nyckel's own infrastructure — your image stays on Nyckel.

Input
Image

Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.

Make it yours
Adaptable

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.

More than a demo: this page is one of thousands of pretrained functions on Nyckel, an ML classification platform. You can invoke classifiers by API, review predictions, correct labels, collect samples from production traffic, and promote any pretrained function to a private custom model — without changing your integration.

Where teams use lighting fixtures classification

Inventory Management

This false image classification function can streamline inventory processes for lighting fixture retailers. By automatically identifying and classifying different types of lighting fixtures, businesses can enhance accuracy in stock levels and quickly pinpoint what needs to be reordered.

Quality Control

Businesses can utilize this function in their quality assurance processes to identify defective or incorrect lighting fixtures during production. By automatically classifying images of products, companies can ensure that only items meeting quality standards reach the market.

E-commerce Optimization

Online retailers can enhance product listings with improved image classification, ensuring that customers find the correct lighting fixtures they seek. This function can help filter and categorize products based on specific attributes, leading to better search and purchase experiences.

Marketing Analysis

The identification of lighting fixtures through image classification can help marketing teams analyze trends within social media or advertising campaigns. By understanding which fixture types resonate best with audiences, businesses can adjust their offerings and promotional strategies accordingly.

Augmented Reality Experiences

This function can support businesses in creating augmented reality applications for customers to visualize lighting fixtures in their homes. By accurately classifying and overlaying fixtures in a user-friendly format, consumers can make informed purchasing decisions based on realistic previews.

Customer Support Automation

The false image classification tool can assist customer service teams in quickly resolving queries related to lighting products. By enabling automatic identification, teams can more effectively help customers select the right fixtures or troubleshoot issues with existing installations.

Data Analytics and Insights

This function can be leveraged to gather insights on market demands and consumer preferences regarding lighting fixtures. By analyzing classified images and associated data, companies can identify popular styles and adapt their product lines to meet evolving market trends.

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 lighting fixtures 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.

Ready to classify lighting fixtures 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.