A pretrained spice identification classifier that sorts an image into one of 10 categories — what type of spice it is. Use the spice identification 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 45 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": "Allspice",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 spice identification 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.
The spice identification function can be utilized by restaurants to ensure the quality and authenticity of spices used in their dishes. By leveraging this technology, chefs can confirm that the spices meet the establishment's standards for flavor and quality before they are incorporated into meals.
Food manufacturers can use the spice identification function to comply with food labeling regulations. This system can verify the accuracy of ingredient lists and ensure all spices used in products are correctly identified, thereby preventing potential fraud and enhancing consumer trust.
Culinary schools and research institutions can use this technology to analyze the spice content in various recipes. By understanding which spices are commonly used or overused, chefs and scientists can innovate new flavor combinations and improve meal formulations.
Importers and distributors can implement the spice identification function to track the authenticity and quality of imported spices. This will help prevent the distribution of counterfeit products and ensure that the spices meet consumers' expectations and regulatory requirements.
A mobile application can integrate the spice identification function to assist home cooks in identifying spices based on visual inputs or descriptions. This can help amateur chefs enhance their culinary skills by learning about various spices, their uses, and flavor profiles.
Regulatory bodies can utilize this spice identification function for food safety monitoring to detect adulteration in spice products. This ensures that consumers are not exposed to harmful substances, thereby promoting public health and safety in the food supply chain.
Online grocery platforms can use the spice identification function to verify the accuracy of spice listings. This technology can help prevent the sale of mislabeled products, enhancing consumer protection and improving the overall shopping experience on e-commerce sites.
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 spice identification 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.