A pretrained yarn brands classifier that sorts an image into one of 10 categories — what yarn brand it is. Use the yarn 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 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": "Bernat",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 yarn 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.
This use case involves verifying the authenticity of yarn brands in retail settings. Retailers can utilize the false image classification function to distinguish between genuine and counterfeit products, ensuring customers receive the quality they expect.
Yarn manufacturers can deploy this function to streamline inventory management. By classifying yarn brands from supplier photographs, businesses can ensure accurate stock levels and prevent mix-ups between different brands in warehouses.
Online platforms can integrate the image classification function to enhance user experience. When customers upload images of yarn they wish to purchase, the system can identify brands, enabling quick and accurate product recommendations.
Yarn producers can apply this function during the quality control process. By classifying images of yarn samples, manufacturers can ensure that products meet brand specifications and maintain consistent quality across different batches.
Marketing teams can use this function to analyze successful branding strategies. By identifying which yarn brands are being visually shared most often on social media, companies can tailor their advertising efforts according to trending preferences.
Customer service departments can use the false image classification function to automate responses. When customers submit images of yarn with inquiries, the system can automatically identify the brand and provide relevant product information or troubleshooting tips.
This use case focuses on identifying potential fraud in yarn contracting and sales. By analyzing images submitted by vendors, businesses can quickly classify whether the yarn matches the claimed brand, helping to mitigate issues related to misrepresentation.
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 yarn 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.