A pretrained grocery product categories classifier that sorts an image into one of 10 categories — the category of grocery products it belongs to. Use the grocery product categories 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 36 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": "Baby Food",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 grocery product categories 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.
Utilize the grocery product categories identifier to streamline inventory management by accurately classifying products. This data can help retailers maintain optimal stock levels, reducing both overstock and stockouts, ensuring a smoother supply chain process.
Leverage the classification function for targeted marketing campaigns by understanding customer preferences based on grocery categories. Retailers can create personalized offers and promotions that resonate with specific customer segments, leading to increased engagement and sales.
Implement the identifier in automated checkout systems to enhance user experience by quickly and accurately classifying items being purchased. This application can reduce transaction times and improve accuracy in pricing, leading to higher customer satisfaction.
Enhance e-commerce platforms with the grocery product categories identifier to provide personalized product recommendations. By understanding customer purchase patterns within categories, retailers can suggest complementary items, driving additional sales and improving customer experience.
Use the classification feature for competitive product analysis by assessing the grocery product offerings of competitors. This insight can help businesses identify market gaps, adjust pricing strategies, and refine their product assortment to better meet consumer needs.
Integrate the grocery product categories identifier within the supply chain to improve demand forecasting and logistical planning. By accurately classifying products, businesses can enhance distribution efficiency and reduce operational costs.
Utilize the identifier for advanced data analytics and reporting, enabling businesses to analyze sales trends across various grocery categories. This information can guide strategic decisions regarding product launches, category expansions, and marketing efforts.
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 grocery product categories 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.