A pretrained mahjong tile type classifier that sorts an image into one of 10 categories — what type of mahjong tile it is. Use the mahjong tile type 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 16 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": "Bamboo",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 mahjong tile type 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.
In the gaming industry, developers can use the Mahjong tile type identifier to improve the accuracy of tile recognition in their games. This feature enhances the gameplay experience by ensuring that players interact correctly with tiles, allowing for a more immersive and enjoyable experience.
Educators can leverage the tile identification function to create interactive learning tools for new players. By providing instant feedback on tile recognition, learners can quickly grasp the game's fundamentals and improve their skills.
Businesses offering automated Mahjong platforms can integrate this identifier to streamline scoring processes. The system can automatically recognize the tiles in play, calculate scores, and resolve disputes more efficiently, increasing player satisfaction.
Content creators and influencers can utilize this technology to develop instructional videos and online courses. By showcasing the correct identification and usage of each tile, they can create engaging and informative content that attracts and retains viewers interested in mastering Mahjong.
Online Mahjong platforms can implement this identifier to verify tile authenticity during online games. This prevents cheating by ensuring that players are using legitimate tiles, thus maintaining the integrity of the online gaming experience.
App developers can incorporate the Mahjong tile type identifier into mobile applications for casual players. Features such as tile recognition and playing tips can enhance user engagement and retention, making the app more valuable to users.
Organizations working on preserving cultural heritage can utilize this technology for research and documentation of Mahjong tiles. By accurately identifying tile types, researchers can analyze variations and historical contexts, contributing to the understanding and appreciation of the game's cultural significance.
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 mahjong tile type 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.