A pretrained how many hands are raised in class classifier that sorts an image into one of 10 categories — how many hands are raised in class. Use the how many hands are raised in class 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 21 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": "0 Hands Raised",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 how many hands are raised in class 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 evaluate student engagement during lectures by counting the number of raised hands. Educators can leverage this data to identify which topics captivate students' attention and which may require more interactive methods.
Integrating the hand-raising identifier into adaptive learning platforms allows for real-time feedback to adjust lesson plans. When more students raise their hands, the system can quickly provide additional resources or change the pace to match student interest levels.
Schools can employ this function to assess the effectiveness of different teaching styles and methods. By analyzing the hand-raising data, administrators can identify patterns and provide tailored training for instructors to enhance classroom interaction.
This function can help track individual student participation over time. By recording hand-raising frequency, teachers can identify students who may need additional encouragement or support in contributing to discussions.
Organizers of workshops and seminars can utilize this function to gauge audience engagement during sessions. By counting raised hands, they can assess participant interest and adapt future programming or training offerings accordingly.
Teachers can use real-time hand count data to manage classroom dynamics effectively. If too few hands are raised, the instructor may decide to employ strategies to stimulate discussion or assess comprehension before moving forward.
Researchers can analyze hand-raising data in studies related to educational technology effectiveness. By quantifying engagement levels, insights can be generated about how technology impacts student interaction and learning outcomes in real instructional settings.
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 how many hands are raised in class 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.