A pretrained buddha by picture classifier that sorts an image into one of 10 categories — what type of Buddha statue it is. Use the buddha by picture 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 25 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": "Amitabha",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 buddha by picture 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 utilized in educational apps that aim to teach users about Buddhist culture and artifacts. By identifying images of Buddha, users can learn about different representations, styles, and historical significance associated with each image.
Galleries and collectors can leverage this function to authenticate works of art. By analyzing images and determining their classification as Buddha or non-Buddha representations, stakeholders can assess the value and legitimacy of Buddhist art pieces.
An e-commerce platform can incorporate this image classification to improve the searchability of Buddhist items. Customers can filter products based on their interest in Buddha-related imagery, streamlining their purchasing experience.
This function can help social media platforms identify and flag inappropriate content that misuses or disrespects Buddhist imagery. Automated moderation can ensure that posts adhere to community guidelines concerning cultural sensitivity.
Businesses focusing on spiritual or wellness products can use this function to tailor their marketing messages. By analyzing user-uploaded images that include Buddha representations, companies can deliver targeted advertisements to relevant audiences.
Nonprofit organizations focusing on Buddhist charities can use this classification function to curate visual content for their campaigns. By identifying images featuring the Buddha, they can create meaningful content that resonates with potential donors and raises awareness for their causes.
Mindfulness and meditation apps can incorporate this function to enhance user experience. By allowing users to upload and classify images, the app can curate personalized meditation spaces that reflect the user’s spiritual preferences, promoting a more engaging environment.
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 buddha by picture 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.