A pretrained if has circuit board classifier that sorts an image into one of 2 categories. Use the if has circuit board 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 2 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": "Circuit Board",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if has circuit board 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.
This image classification function can be integrated into manufacturing lines to automatically identify products containing circuit boards. By flagging defective or incorrectly assembled items, it enhances the efficiency of quality assurance processes.
The classification function can assist recycling facilities by automatically sorting electronic waste that contains circuit boards. This ensures that valuable materials are properly extracted and processed, reducing environmental impact and improving resource recovery rates.
Retailers can use the identifier to streamline inventory management by categorizing items that contain circuit boards. This allows for better tracking and reporting of high-value electronics, helping in stock optimization and minimizing losses.
Repair shops can utilize the classification function to quickly identify devices that require specialized circuit board repair services. This speeds up the diagnostics process, ensuring technicians can prioritize high-impact repairs while improving customer satisfaction.
Businesses in the electronics sector can incorporate this identifier to verify the presence of circuit boards in components received from suppliers. This minimizes the risk of missing key components and enhances reliability throughout the supply chain.
Insurance companies can employ the image classification function to validate claims related to electronic devices. By identifying whether a device contains a circuit board, they can assess the extent of damage or loss more accurately and expedite claims processing.
Smart home systems can leverage this identifier to recognize devices that contain circuit boards for enhanced functionality. This enables users to monitor and manage their smart devices more effectively, ensuring optimal performance and compatibility.
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 if has circuit board 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.