A pretrained cookies types classifier that sorts an image into one of 10 categories — what type of cookie it is. Use the cookies types 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 20 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": "Biscotti",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 cookies types 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 implemented in manufacturing facilities to automatically classify cookie types on the production line. By identifying whether the correct type of cookie is being produced, companies can maintain quality standards and reduce the risk of product recalls.
Retailers can use the cookie types identifier to streamline inventory management by categorizing different cookie varieties based on images. This will enable more accurate stock tracking, ensuring that popular items are restocked in a timely manner while minimizing overstock of less popular types.
Supermarkets with self-checkout systems can integrate this image classification function to identify cookies during the scanning process. This will facilitate faster checkouts for customers and reduce the need for price lookups, leading to an improved shopping experience.
E-commerce platforms can leverage the image classification function to analyze customer preferences based on cookie types viewed or purchased. This data can be used to create targeted marketing campaigns, recommending similar products or special offers based on individual customer behavior.
A cooking app can utilize this function to allow users to take pictures of cookies they encounter and receive ingredient and recipe suggestions based on the identified type. This could enhance user engagement by providing tailored content that encourages users to recreate or experiment with various cookie recipes.
Bakeries and cafes can integrate this feature into their point of sale or ordering system to ensure customers with dietary restrictions receive the correct types of cookies. By easily classifying gluten-free, nut-free, or vegan cookies, businesses can improve customer satisfaction and avoid potential allergic reactions.
Food bloggers and influencers can use this classification function to automate the tagging of various cookie types in their content. This will enhance user engagement by allowing their audience to easily discover different cookie recipes or brands, thereby increasing interaction and visibility on social media platforms.
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 cookies types 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.