A pretrained perfume brands classifier that sorts an image into one of 10 categories — what perfume brand it is. Use the perfume 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 19 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": "Armani",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 perfume 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 functionality can be used by retailers and distributors to ensure that all perfumes displayed in their stores or online marketplaces belong to the correct and authentic brands. By scanning the products and checking their images against a database of known brands, businesses can avoid legal issues stemming from counterfeit sales.
Perfume retailers can utilize the false image classification function to automate the identification of products during stock audits and inventory checks. This reduces manual effort and minimizes human error, ensuring that inventory records are accurate and up to date.
Online perfume sellers can leverage this function to ensure that the images associated with their listings accurately represent the actual products. This helps maintain brand integrity and enhances customer trust by preventing misleading representations.
Brands can use this technology in market research to ensure that promotional materials and advertisements feature correct images of their products. This is vital for maintaining brand consistency across all platforms and marketing initiatives.
Luxury perfume manufacturers can implement this function to monitor the market for counterfeit products. By identifying false images of their brands, they can take appropriate actions to combat fakes and protect their brand reputation.
Companies can analyze customer-uploaded images in reviews using this classification function to assess whether customers are using and enjoying authentic products. This feedback can inform marketing strategies and product development based on real customer experiences.
Businesses can integrate the false image classification function to verify that products throughout the supply chain conform to established brand images. This ensures authenticity at every step, from manufacturing to retail, thus preserving brand value and consumer trust.
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 perfume 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.