A pretrained home appliance types classifier that sorts an image into one of 10 categories — the type of home appliance in your image.. Use the home appliance types 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 26 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": "Air Conditioner",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 home appliance types 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.
Integrating the home appliance types identifier into smart home systems can enhance automation capabilities. By accurately classifying appliances such as refrigerators, ovens, and washers, the system can optimize energy usage and improve user convenience through personalized settings.
Insurance companies can utilize image classification to assess appliance damage during claims processing. By accurately identifying appliance types and conditions, insurers can streamline evaluations, expedite claims approvals, and prevent fraud.
Online retailers can leverage this technology to automatically classify and categorize home appliances from uploaded images. This reduces the time and manual labor required for product entry, ensuring a consistent and organized online catalog.
Businesses can utilize appliance type data to create tailored marketing campaigns. By understanding which types of appliances customers own, companies can send personalized offers and recommendations, thus enhancing customer engagement.
Service providers can implement image classification in their mobile applications to help customers identify issues with their appliances. By selecting or photographing an appliance type, users can receive immediate troubleshooting tips or service options, improving customer satisfaction.
Retailers can use the identification function to manage inventory more effectively. By classifying appliances in stock through images, they can better track availability and optimize restocking efforts based on demand trends.
This technology can assist home energy management systems in identifying appliances to provide users with insights into energy consumption patterns. By accurately classifying appliance types, the system can recommend energy-saving tips and help homeowners make informed decisions about appliance usage.
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 home appliance types 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.