A pretrained socket type classifier that sorts an image into one of 10 categories — what type of socket it is. Use the socket type 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 34 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": "Adapter",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 socket type 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.
The false image classification function can be integrated into assembly line processes to identify defective socket types swiftly. By ensuring only correctly manufactured sockets pass quality checks, companies can minimize waste and enhance product reliability.
Retailers can utilize this function to automatically categorize and manage their stock of different socket types. This automation aids in maintaining accurate inventory levels, streamlining reordering processes, and reducing mismatches in product listings.
Online marketplaces can implement this function to authenticate the images provided by sellers, ensuring that the socket types listed are accurately represented. This minimizes customer complaints and enhances buyer confidence in the platform.
Companies developing AR apps for electrical tools and equipment can use this function to recognize and display relevant information about socket types. This provides users with instant guidance on proper usage and enhancements, improving their overall experience.
Educational platforms can incorporate this function into their training modules to help students identify socket types in real-time. This makes learning more interactive and effective, allowing students to engage with practical examples.
Insurance companies can use the false image classification function to assess damage claims related to electrical equipment, ensuring the correct identification of socket types. This speeds up the claims process and reduces fraudulent claims by evaluating the validity of submitted images.
Home improvement apps can leverage this function to help consumers determine the compatibility of different socket types for their tools and appliances. By inputting images, users receive instant feedback on whether they have the right type, aiding in purchasing decisions.
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 socket type 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.