A pretrained type of pen classifier that sorts an image into one of 2 categories — what type of pen it is. Use the type of pen 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": "Ballpoint Pen",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 type of pen 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.
Retailers can utilize the multilabel image classification function to automatically categorize different types of pens in their inventory. This aids in real-time tracking of stock levels and helps in making informed purchasing decisions based on observed sales trends.
E-commerce platforms can automate the tagging of pen products with this function, ensuring that customers easily find specific types of pens, such as gel, ballpoint, or fountain pens. Enhanced tagging improves the user experience and increases the likelihood of conversion through better searchability.
Businesses can analyze consumer preferences by collecting images of pen types shared on social media. The classification function can identify which types are most popular, guiding product development and marketing strategies.
Customer service teams can leverage this image classification function to expedite product returns and exchanges by identifying the type of pen in customer-uploaded images. This reduces resolution time and improves customer satisfaction.
By understanding the types of pens customers already own (via images uploaded to their profiles), companies can create personalized marketing campaigns suggesting complementary products, such as ink refills or specific accessories tailored to the identified pen types.
Manufacturers can implement this multilabel image classification system on production lines to ensure that only specified types of pens are packaged. It can help in detecting mispackaged products and minimizing errors before products reach the market.
Educational institutions can use this classification function as a teaching tool to help students learn about different types of pens and their uses in various artistic techniques. By providing feedback on student assignments involving pen illustrations, it encourages exploration and understanding of writing instruments.
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 type of pen 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.