A pretrained fabric brands classifier that sorts an image into one of 10 categories — what fabric brand it is. Use the fabric 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 49 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": "Abercrombie And Fitch",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 fabric 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 function can be used by retailers to verify the authenticity of fabric brands before making bulk purchases. By classifying images of fabric, it helps prevent counterfeit goods from entering the supply chain, ensuring customers receive genuine products.
Online retailers can integrate this image classification function into their platforms to automatically tag fabrics based on their brand. This would streamline the inventory management process and enhance the shopping experience by allowing users to filter products by specific fabric brands.
Textile manufacturers can utilize the function in their quality control processes. By analyzing fabric images during production, they can quickly identify and rectify instances of brand misrepresentation or defective batches, maintaining brand integrity.
Fashion brands and designers can apply this function to analyze trending fabric types and brands in social media or e-commerce images. This data-driven insight enables them to adapt their collections according to prevailing market preferences and competition.
Customer support teams in textile businesses can use this image classification tool to help resolve brand-related inquiries. By uploading product images into the system, support agents can quickly identify the brand and provide accurate information or assistance to customers.
Researchers studying fabric technology or sustainability can employ the function to classify large datasets of fabric images from various brands. This analysis can lead to insights into material usage trends, sustainable practices, and innovations within the textile industry.
Marketing teams can leverage the classification function for campaign analysis and targeting. By identifying fabrics associated with certain brands in consumers' images, they can tailor advertising efforts and influencer partnerships to effectively reach and engage their target audience.
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 fabric 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.