Pretrained computer vision classifier

Identify if there is a fruit with one API call.

A pretrained if there is a fruit classifier that sorts an image into one of 2 categories. Use the if there is a fruit API immediately, no training required, then adapt it to your own data when you need more.

Use this classifier Try with your image

Pretrained · Nyckel-trained 2 labels out of the box Image input

nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke

Contains Fruit 0.92

Does Not Contain Fruit 0.05

Try the if there is a fruit classifier

Drop in a photo and get the prediction back. No signup, no setup.

What this if there is a fruit classifier recognizes

A sample of the 2 labels this pretrained classifier chooses between.

Contains Fruit

Does Not Contain Fruit

Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.

Call the if there is a fruit API

Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:

API quick start

curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"data": "https://example.com/photo.jpg"}'

import requests

response = requests.post(
    "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
    headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
    json={"data": "https://example.com/photo.jpg"},
)
print(response.json())

const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
  method: "POST",
  headers: {
    "Authorization": "Bearer YOUR_ACCESS_TOKEN",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());

$ch = curl_init();

curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');

$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);

$result = curl_exec($ch);
curl_close($ch);
echo $result;

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How to invoke a function →

Example response

{
  "labelName": "Contains Fruit",
  "labelId": "label_...",
  "confidence": 0.92
}

API documentation Authentication Pricing Get your endpoint

Under the hood

Model type

Nyckel-trained

Trained on a Nyckel-curated dataset covering 2 if there is a fruit categories, served on Nyckel's own infrastructure — your image stays on Nyckel.

Input

Image

Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.

Make it yours

Adaptable

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.

More than a demo: this page is one of thousands of pretrained functions on Nyckel, an ML classification platform. You can invoke classifiers by API, review predictions, correct labels, collect samples from production traffic, and promote any pretrained function to a private custom model — without changing your integration.

Where teams use if there is a fruit classification

Grocery Store Inventory Management

The fruit identifier can be integrated into grocery store inventory systems to automatically detect the presence of fruits in delivery shipments. This helps streamline inventory management processes, reduce human errors, and ensure shelves are stocked accurately.

Food Waste Reduction

Restaurants can utilize the fruit identification function to monitor the freshness and condition of fruit supplies. By automatically identifying fruits that are nearing spoilage, management can prioritize their use in dishes, helping to minimize food waste and optimize resource use.

Smart Shopping Applications

Mobile applications can leverage this technology to create a more interactive shopping experience for users. By scanning grocery items with their smartphone, users can receive instant information about the types of fruits available, including nutritional benefits and recipe suggestions.

Agricultural Monitoring

Farmers can use fruit identification to assess their crop yields and health. By implementing Drones or cameras equipped with this technology, they can efficiently monitor the growth stages of fruit and identify any potential disease issues early, leading to better crop management.

Nutritional Assessment Tools

Health-conscious applications can incorporate fruit identification to help users track their fruit intake. By simply scanning their food, users can receive a detailed report on their fruit consumption, enabling them to better meet their dietary goals.

Automated Fruit Quality Assessment

Quality control systems in packhouses can benefit from this identification function by assessing the quality of fruits during processing. Automated identification allows for real-time feedback and sorting based on ripeness, improving efficiency in the fruit distribution chain.

Interactive Educational Tools

Educational platforms can use fruit identification to create interactive learning experiences for children. By using AR (Augmented Reality), students can learn about different fruits, their characteristics, and health benefits simply by scanning real-life objects, making learning engaging and fun.

Common questions

What's the difference between a zero-shot and a Nyckel-trained classifier?

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.

How do I know whether this will work for my application?

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.

What happens when it makes a mistake?

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.

Do I need training data to get started?

No. This if there is a fruit 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.

What does it cost to try?

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.

Related classifiers

Ready to classify if there is a fruit at scale?

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.

Use this classifier Read the docs