A pretrained nut types classifier that sorts an image into one of 10 categories — what type of nut it is. Use the nut types 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 13 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": "Almonds",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 nut types 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 employed in food manufacturing to ensure that the correct nut types are used in products. By automatically identifying and validating nut types during processing, companies can prevent costly production errors and ensure product consistency.
Businesses can utilize this image classification function to verify the authenticity of nut shipments received from suppliers. By scanning and classifying nuts, companies can maintain quality standards and reduce the risk of fraud or misrepresentation of products.
Health and nutrition apps can incorporate this function to help users identify nuts and track their dietary intake. By allowing users to scan and classify nuts, the application can provide personalized dietary suggestions and monitor allergen consumption.
Grocery stores can apply this function in their inventory management systems to automatically classify nuts for stock management. This can enhance shelf organization, streamline reorder processes, and minimize human error in product identification.
Companies committed to sustainable sourcing can use this function to monitor their nut sourcing practices. By identifying and classifying nut types, businesses can ensure they are sourcing responsibly and in alignment with their environmental values.
Food researchers can utilize this classification function for studies involving the nutritional properties of different nut types. By accurately categorizing samples, researchers can derive meaningful insights into health benefits and applications.
Cooking and recipe websites can integrate this function to allow users to upload images of nuts and receive feedback on their types. This feature can enhance user engagement, making it easier for home cooks to identify ingredients and follow recipes accurately.
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 nut types 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.