A pretrained pepper type classifier that sorts an image into one of 10 categories — what type of pepper it is. Use the pepper type 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 25 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": "Aji Amarillo",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 pepper type 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 utilized by agricultural producers to automatically classify different types of peppers during the quality control process. By quickly identifying whether the peppers meet the required standards, farmers can reduce waste and ensure consistent product quality.
Distributors can use this image classification function to verify the types of peppers they are receiving from suppliers. This helps in preventing mix-ups and ensuring that the correct varieties are in stock for retailers, thereby enhancing supply chain efficiency.
Supermarkets can deploy this technology to analyze customer preferences by classifying the types of peppers sold in their stores. Insights gained can help retailers stock popular pepper varieties, improving sales and customer satisfaction.
Food processing companies can integrate the image classification function into their inventory management systems. By automatically identifying pepper types in inventory, they can streamline restocking processes and reduce human error.
Chefs and culinary professionals can use this technology to classify pepper types while developing new recipes. Understanding the flavor profiles of different peppers will enable better ingredient selection and enhance dish innovation.
Developers of culinary apps can incorporate this function to help users learn about various pepper types visually. With a reliable identification tool, consumers can make informed choices in cooking and understand the nuances of different pepper flavors.
Agricultural researchers can leverage this image classification capability to monitor pepper plants for pest and disease impact. By identifying affected plants quickly, interventions can be applied faster, thereby reducing crop damage and improving yield.
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 pepper type 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.