A pretrained light bulb brands classifier that sorts an image into one of 10 categories — what light bulb brand it is. Use the light bulb brands 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 15 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": "Boulevard",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 light bulb brands 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 use the light bulb brands identifier to automatically classify and manage their inventory. By integrating this function with their inventory systems, stores can keep track of stock levels for different brands and ensure that popular items are always available on shelves.
E-commerce platforms can utilize the identifier to enhance product listings by automatically tagging light bulb products with their respective brands. This would improve searchability for customers and ensure better product classification, leading to an improved shopping experience.
Brands can leverage the function to analyze consumer preferences and tailor marketing campaigns accordingly. By identifying which brands are most commonly recognized or purchased, marketing teams can create targeted advertisements that resonate with specific consumer bases.
Customer service departments can implement the identifier to streamline warranty claims and product support for light bulbs. When a customer contacts support, the system can quickly identify the brand of the bulb in question, allowing representatives to provide accurate assistance without delay.
Manufacturers can use the function to gather insights on market trends by analyzing which brands are frequently purchased over time. This data can inform product development and strategy, helping companies stay competitive in the light bulb industry.
Environmental organizations can adopt the identifier to analyze and report on the distribution of different light bulb brands in relation to eco-friendly practices. This information can be crucial for initiatives aimed at promoting energy-efficient lighting solutions and encouraging sustainable brands.
Manufacturers can implement the identifier within production lines to monitor and ensure that the correct branding is applied to light bulbs. This function can help reduce human error in branding, maintaining brand integrity and reducing costly recalls due to mislabeling.
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 light bulb brands 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.