A pretrained kabaddi teams classifier that sorts an image into one of 10 categories — what kabaddi team a player belongs to. Use the kabaddi teams 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": "Blazers",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 kabaddi teams 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 used to analyze the performance of different kabaddi teams by automatically classifying game footage. By identifying specific teams in the footage, analysts can track performance trends, strategies, and player effectiveness over time.
Kabaddi teams can enhance fan engagement by using this function to customize multimedia content, such as highlight reels and promotional videos. Fans can receive personalized updates featuring their favorite teams and players, boosting their connection to the sport.
Businesses that sponsor kabaddi teams can leverage this function to generate detailed reports on team visibility. By classifying images of sponsorship placements, they can quantify how often their logos are viewed in different contexts, providing valuable analytics to justify sponsorship investments.
Sports networks can implement this function to streamline the broadcasting of kabaddi games. By automatically identifying teams in the footage, networks can dynamically switch camera angles, integrate graphics, and provide real-time statistics based on the teams involved.
This function can help kabaddi team managers monitor social media platforms by identifying images related to their teams. By analyzing trends and sentiment around team performance, managers can better engage with fans and address concerns proactively.
Retailers can use image classification to analyze kabaddi-related content and identify which teams have higher demand for merchandise. This data can guide inventory decisions and marketing campaigns, ensuring that popular team merchandise is always available.
Coaching staff can utilize this function to assess training footage and categorize player performance based on team affiliations. By tracking individual contributions within the team context, coaches can provide tailored feedback and develop more effective training programs.
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 kabaddi teams 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.