A pretrained if onion is rotten classifier that sorts an image into one of 2 categories. Use the if onion is rotten 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 2 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": "Onion Is Fresh",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if onion is rotten 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 integrated into agricultural routine checks to identify rotten onions before they reach the market. This ensures that only fresh produce is sold, maintaining quality standards and reducing waste.
Supermarkets can employ this technology to scan their inventory for key products. By identifying rotten onions, retailers can streamline restocking processes and enhance the shopping experience for customers looking for fresh produce.
Food processing companies can utilize this function to assess incoming shipments of onions. By filtering out rotten produce before processing, they can ensure the quality and safety of their end products while minimizing potential health risks.
In a smart supply chain setup, this identifier can be used at various checkpoints to monitor the condition of onions during transportation. This ensures that only fresh onions are delivered, reducing the likelihood of customer complaints and returns.
As a feature in smart kitchen appliances, this function can help home cooks assess the freshness of their ingredients. By easily identifying rotten onions, users can improve meal quality and reduce food waste at home.
Mobile applications focused on reducing food waste can integrate this functionality to help users identify spoilage in their stored onions. This empowers consumers to make informed decisions about using ingredients in a timely manner, helping to minimize waste.
Farmers can use this technology to analyze root crop quality post-harvest. By systematically identifying and measuring the proportion of rotten onions, they can make data-driven decisions regarding planting, harvesting, and market pricing strategies.
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 if onion is rotten 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.