Identify count of people in the locker room
using AI
Below is a free classifier to identify count of people in the locker room. Just upload your image, and our AI will predict the number of people present in the locker room - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("count-of-people-in-the-locker-room", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/count-of-people-in-the-locker-room/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/count-of-people-in-the-locker-room/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the number of people present in the locker room.
This pretrained image model uses a Nyckel-created dataset and has 15 labels, including 0, 1, 10-20, 2, 21-30, 3, 31-40, 4, 41-50 and 5.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the number of people present in the locker room).
Whether you're just curious or building count of people in the locker room detection into your application, we hope our classifier proves helpful.
Need to identify count of people in the locker room at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Occupancy Monitoring: This function can help gyms and fitness centers monitor the number of people in locker rooms to ensure they do not exceed capacity limits. It aids in maintaining safety standards and ensures a comfortable environment for users.
- Resource Management: By accurately counting the number of individuals in locker rooms, businesses can optimize staffing and resource allocation. For example, if a higher usage pattern is detected, additional staff can be scheduled at peak times to assist customers.
- User Behavior Analysis: The data collected from this image classification function can provide insights into peak usage times and user behavior. This information can inform marketing strategies and the development of new services tailored to customer needs.
- Security Monitoring: The function can enhance security measures by keeping track of the number of individuals in sensitive areas like locker rooms. Anomalies or unusual counts can trigger alerts to security personnel, ensuring a safer environment for all patrons.
- Emergency Response Planning: In the event of an emergency, knowing the exact number of people in a locker room can facilitate efficient evacuation procedures. This function can provide crucial data to first responders or facility managers during critical situations.
- Hygiene and Maintenance Scheduling: Regular cleaning and maintenance can be scheduled based on occupancy data, ensuring locker rooms are serviced when most needed. This can improve hygiene standards and enhance customer satisfaction through a consistently clean environment.
- Membership Engagement Programs: The insights from the locker room occupancy can reveal trends that inform membership programs and promotions. For instance, if data shows low usage at certain times, targeted incentives can encourage members to visit during those periods, boosting overall engagement.