Identify gym members count
using AI
Below is a free classifier to identify gym members count. Just upload your image, and our AI will predict the number of gym members in different age groups - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("gym-members-count", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/gym-members-count/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/gym-members-count/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the number of gym members in different age groups.
This pretrained image model uses a Nyckel-created dataset and has 10 labels, including 1-5, 101-200, 11-20, 201-300, 21-30, 301-500, 31-50, 500+, 51-100 and 6-10.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the number of gym members in different age groups).
Whether you're just curious or building gym members count detection into your application, we hope our classifier proves helpful.
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Need to identify gym members count at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Membership Growth Analysis: This function can help gym owners analyze the trends in their member counts over time. By identifying false or fraudulent members, gym management can gain insights into genuine membership growth and optimize marketing strategies accordingly.
- Fraud Detection: The false image classification function can be utilized to detect fraudulent membership sign-ups or suspensions. By identifying images that do not belong to legitimate members, gyms can maintain an accurate account of active members for revenue and safety reasons.
- Engagement Metrics Improvement: Gyms can use the classification function to ensure that member engagement metrics reflect actual participation. By filtering out false accounts, they can develop targeted engagement programs tailored for reliable metrics.
- Personalized Marketing Campaigns: By accurately identifying true gym members, marketing teams can tailor promotional offers to the correct audience. This ensures that marketing resources are efficiently allocated and that campaigns resonate with genuine gym-goers.
- Enhanced Security Protocols: The false image classification could support enhanced security measures within gyms. By ensuring only verified members are recognized, gyms can prevent unauthorized access and maintain a secure environment for all members.
- Improved Analytics for Facility Planning: Understanding the true number of gym members enables better resource allocation, such as staff scheduling and equipment management. Accurate member counts can inform future expansions or modifications to accommodate actual usage patterns.
- Customer Experience Optimization: By identifying and resolving false member accounts, gyms can enhance the overall customer experience. Ensuring that only legitimate members are counted can lead to more personalized services and increased member satisfaction, ultimately boosting retention rates.