A pretrained shampoo brands classifier that sorts an image into one of 10 categories — what shampoo brand it is. Use the shampoo 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 20 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": "Aussie",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 shampoo 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 ensure that the shampoo brands sold in their inventory are genuine. By classifying images of products, retailers can quickly identify counterfeit items and maintain brand integrity, protecting their reputation and customer trust.
Companies can leverage this image classification function to analyze consumer preferences by identifying popular shampoo brands in various demographics through social media or product images. This data can guide marketing strategies and product development tailored to consumer trends.
Salons and beauty Supply Companies can utilize the shampoo brand identifier to streamline their inventory management processes. By analyzing images of stocked products, businesses can automatically track which brands are available, helping to optimize stock levels and reorder cycles.
Digital marketers can use this function to analyze user-generated content, such as photos shared on social media. By classifying the shampoo brands in these images, advertisers can create targeted campaigns that resonate with specific audiences based on brand affinity.
Businesses can monitor competitors’ brand visibility by using the image classification function to aggregate data on their shampoo products across various platforms. This analysis can help companies identify market trends and adjust their strategies to maintain a competitive edge.
Online platforms can improve user experience by using the shampoo brand identifier to recommend products based on visual search. Customers can upload images of shampoos they like, and the system can instantly suggest similar brands available on the site, increasing conversion rates.
Brands can use this classification function to assess the sustainability of their product lineup by identifying eco-friendly shampoo brands. By analyzing the market landscape, companies can highlight their commitment to sustainability and potentially partner with or promote green brands to enhance their offerings.
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 shampoo 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.