A pretrained art mediums classifier that sorts an image into one of 2 categories — which art medium is used. Use the art mediums 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 31 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": "Oil Painting",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 art mediums 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.
"An online art marketplace can utilize the art mediums identifier to automatically categorize artworks uploaded by artists. This will enhance user experience by allowing potential buyers to easily filter artworks based on specific mediums like oil painting, watercolor, or sculpture.
"Art authentication organizations can use the multilabel classifier to assist in validating pieces by identifying the mediums used in a painting or sculpture. This can help experts determine the authenticity of a piece based on the medium's characteristics and historical context.
"Educational institutions and online platforms can leverage the classification function for art students to learn about various art mediums. By analyzing artworks in their database, students can gain insights into different styles and their respective materials, enhancing their understanding and appreciation of art.
"Social media platforms focused on art can implement the classifier to automatically tag images based on the art mediums present. This will enable users to discover content more easily, increasing engagement and creating a more organized content interface.
"Art-based recommendation systems can integrate the multilabel image classification function to provide users with personalized suggestions. By analyzing users' preferences in mediums, the system can recommend similar artworks that match their tastes, driving sales and enhancing user satisfaction.
"Art conservatories can employ the art mediums identifier to assist in their restoration processes. By accurately classifying the mediums used in identified artworks, conservators can select appropriate preservation techniques and materials that are compatible with the original art.
Market analysts can use the classifier to evaluate trends in the art industry, identifying shifts in popularity for certain mediums over time. This data can inform galleries and artists about current consumer interests and help shape future marketing and creation strategies.
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 art mediums 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.