A pretrained how many desks have students in them classifier that sorts an image into one of 10 categories — how many desks have students in them. Use the how many desks have students in them 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 Desks",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 how many desks have students in them 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 help educators determine how many desks are occupied by students in real-time, allowing for better classroom management. Institutions can use this data to optimize space utilization, ensuring that resources are allocated efficiently during peak attendance times.
By analyzing desk occupancy, schools can gain insights into student engagement during in-person classes. This data can be used to adjust lesson plans or tailor hybrid learning environments to promote better student participation.
The image classification function can assist facility managers in monitoring classroom occupancy across multiple rooms. This information can facilitate timely maintenance or cleaning schedules based on actual usage, thereby enhancing the overall learning environment.
Schools can automate attendance tracking by analyzing desk occupancy at the beginning of a class. This system reduces the reliance on manual roll calls and increases accuracy, allowing teachers to focus more on instruction rather than administrative tasks.
During large-scale events or lectures, this image classification function can assess how many participants are present by analyzing desk occupancy. This information aids in effective seating arrangements and caters to safety regulations for crowd control.
By identifying how many desks are occupied during examinations, administrators can make informed decisions regarding resource allocation, such as extra supervision or additional seating. The data can enhance the organization of exam environments and ensure a smooth process.
Researchers can utilize this function to collect data on student behaviors in classroom settings. By correlating desk occupancy with student performance and engagement metrics, educational institutions can derive conclusions that inform teaching methodologies and classroom designs.
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 desks have students in them 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.