A pretrained jewelry brands classifier that sorts an image into one of 10 categories — what jewelry brand it belongs to. Use the jewelry 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 48 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": "Balenciaga",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 jewelry 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.
The jewelry industry is rife with counterfeit products. This function can help retailers and consumers verify the authenticity of jewelry pieces by identifying the brand based on images, thereby protecting brand integrity and consumer trust.
Jewelry brands can leverage this function to match their products with social media influencers who align with their image. By analyzing influencers’ content, brands can identify suitable partnerships that resonate with target audiences and enhance marketing efforts.
E-commerce platforms can enhance user experience by integrating this classification function into their image search capabilities. Customers can upload images to find similar jewelry pieces from specific brands, leading to increased engagement and sales.
Retailers can utilize this function for efficient inventory management by classifying jewelry items based on their brands. This approach allows for accurate inventory tracking, ensures brand representation is maintained, and aids in better stock decisions.
Marketers can deploy this identification function to create targeted ad campaigns focusing on specific jewelry brands. By understanding which brands are trending or being sought after, marketers can refine their campaigns to reach the right audience effectively.
This function can assist jewelry brands in keeping tabs on their competitors by analyzing which brands are gaining visibility in the market. The insights gained can help brands adjust their strategies, product offerings, and market positioning.
By analyzing customer-uploaded images and their associated brand classifications, jewelry companies can gather insights into consumer preferences. This data can drive product development, marketing strategies, and personalized customer experiences based on detected brand inclinations.
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 jewelry 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.