A pretrained dice face value classifier that sorts an image into one of 6 categories — the value of the dice face.. Use the dice face value 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 6 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": "Five",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 6 dice face value 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.
Utilize the 'dice face value' identifier in mobile and desktop gaming applications to accurately detect dice rolls in real-time. This can enhance user experience by providing a more interactive and dynamic game environment, eliminating manual input for players.
Implement this function in online casino platforms to ensure fair play by automatically verifying the results of dice games. It can help prevent cheating and enhance transparency, building trust with players.
Integrate the dice face value identifier in augmented reality (AR) applications to create immersive gaming experiences. Users could roll physical dice while the app analyzes and displays the results in a virtual setting, enriching gameplay with visual and interactive elements.
Deploy the identifier in educational software focused on teaching probability and statistics. By visualizing real-time dice rolls and outcomes, students can gain a better understanding of random events and their probabilities through engaging simulations.
Use the dice face value identifier at social events or parties where dice games are played. Host organizers can facilitate smooth game management by automatically recording and tracking dice results, allowing for a more fluid and enjoyable gaming experience.
Implement this function in quality control processes for manufacturing companies that use dice-like products (e.g., molded plastic components). The identifier can detect any discrepancies in production runs by verifying the correct face values, ensuring consistent quality.
Incorporate the dice face value identifier in academic settings for research purposes in game theory. Researchers can utilize it to experiment with various scenarios involving randomness and decision-making, ensuring precise data collection on dice-based games.
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 dice face value 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.