A pretrained book count classifier that sorts an image into one of 2 categories — the number of books in an image. Use the book count 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 51 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": "0 Books",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 book count 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 'book count' identifier can automate the tracking of books within a library or retail store. By scanning images of bookshelves or storage areas, it can accurately count and categorize books for effective inventory management, reducing manual errors and saving time.
Online retailers can utilize this function to streamline the cataloging process of books. It can automatically identify and classify multiple book images from suppliers, ensuring accurate listings and up-to-date inventory for a seamless customer shopping experience.
Libraries can implement this technology to digitize their collections efficiently. By identifying and counting books from photos of shelves or boxes, it accelerates the cataloging process, making it easier to convert physical resources into digital formats for online access.
Organizations can use the identifier for analyzing the diversity of their book collections. By categorizing books by genre, author, and other attributes, they can identify gaps and ensure a balanced representation of topics and viewpoints across their collections.
Independent bookstores can leverage the 'book count' identifier to monitor sales trends by analyzing the number of titles on display. By understanding which genres are prevalent versus their sales performance, they can adjust inventory strategies to maximize profitability.
Schools and educational institutions can use this function to assess the availability of textbooks and learning materials. By counting and categorizing educational books, they can ensure that students have access to necessary resources and plan for future acquisitions based on need.
Museums and historical societies can apply the identifier to manage collections of rare books and manuscripts. This functionality allows for accurate inventory tracking, enabling organizations to preserve historical artifacts while maintaining an organized catalog for research and exhibition purposes.
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 book count 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.