A pretrained fruit families classifier that sorts an image into one of 10 categories — what type of fruit it belongs to. Use the fruit families 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": "Aggregate Fruits",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 fruit families 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.
Farmers can leverage the fruit families identifier to efficiently categorize fruit during the harvesting process. By ensuring that fruits are sorted into their correct families, they can reduce spoilage and improve the quality of their products before they reach the market.
Grocery stores can use this tool to identify fruit types during inventory assessment. Accurate classification helps retailers manage stock levels more effectively, ensuring that they order optimal quantities of each fruit family based on consumer demand.
Health and wellness applications can utilize the function to classify fruits and provide users with tailored dietary recommendations. By understanding the nutritional benefits of different fruit families, users can make informed choices that align with their health goals.
Meal kit and food delivery services can implement the fruit families identifier to ensure the correct fruits are included in their packages. This reduces customer complaints and enhances satisfaction by delivering exactly what customers expect in their meal plans.
Cooking apps and websites can employ this identification function to modify recipes based on available fruits. Users can input the fruits they have, and the platform will suggest recipes, taking into account the family classifications for flavor compatibility.
Researchers can use the identifier to study the diversity and characteristics of fruit families. This data can help in breeding programs aimed at developing disease-resistant or high-yield fruit varieties, contributing to agricultural innovation.
Educational platforms and institutions can incorporate the fruit families identifier to teach students about plant taxonomy and consumer botany. Interactive tools that classify fruits into their families can enhance learning and engagement in the study of plants.
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 fruit families 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.