A pretrained if eggs are rotten classifier that sorts an image into one of 2 categories. Use the if eggs are 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": "Fresh Eggs",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if eggs are 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.
Farmers can leverage the 'if eggs are rotten' identifier to monitor egg quality directly on-site. By integrating this technology into their operations, they can reduce waste and ensure that only fresh, high-quality eggs are sold to consumers.
Distribution centers can implement the identification function to quickly assess the quality of eggs before they are shipped to retailers. This ensures that only safe and fresh products reach the shelves, minimizing customer complaints and food safety risks.
Food processing plants can utilize this function during the quality control phase to verify the freshness of eggs being used in products. This helps maintain high quality in processed food items and can reduce the risk of foodborne illnesses.
Supermarkets and grocery stores can use the rotten egg identifier to inspect the quality of their egg inventory regularly. This technology aids employees in removing spoiled items promptly, ensuring customers receive only the freshest products.
Restaurants can benefit from this function by regularly checking the quality of eggs in their inventory. It helps chefs avoid using spoiled eggs in their dishes, which enhances the overall dining experience for patrons.
Egg producers can incorporate this identifier in their production lines to sort eggs based on quality automatically. The system can help streamline operations, ensuring that rotten eggs do not compromise overall product integrity and reputation.
An app can be developed for consumers to scan eggs at home to check for freshness. This empowers customers with information to make better purchasing decisions and reduces the likelihood of using spoiled eggs in meal preparations.
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 eggs are 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.