Pretrained computer vision classifier
Identify how sweaty someone is with one API call.
A pretrained how sweaty someone is classifier that sorts an image into one of 5 categories — how sweaty someone is. Use the how sweaty someone is API immediately, no training required, then adapt it to your own data when you need more.
Try the how sweaty someone is classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this how sweaty someone is classifier recognizes
A sample of the 5 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.
Call the how sweaty someone is API
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;Example response
{
"labelName": "Extremely Sweaty",
"labelId": "label_...",
"confidence": 0.92
}Under the hood
Trained on a Nyckel-curated dataset covering 5 how sweaty someone is 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.
Where teams use how sweaty someone is classification
Fitness Tracking
This feature can be integrated into fitness applications to provide users with insights into their perspiration levels during workouts. By analyzing sweat production, users can optimize their hydration strategies and improve their training effectiveness.
Wearable Technology
Smart wearables can incorporate this function to monitor sweat levels in real-time. This can alert users when they are sweating excessively, helping them avoid dehydration or heat-related issues during physical activities.
Health Monitoring for Patients
Healthcare providers can use this feature to assess patients who may be experiencing issues related to sweating, such as hyperhidrosis or endocrine disorders. By continuously monitoring sweat levels, physicians can tailor treatments and track the effectiveness of interventions.
Sports Performance Analysis
Coaches and sports analysts can leverage this function to evaluate athletes' performance under different environmental conditions. By correlating sweat levels with performance metrics, they can identify optimal conditions for training and competition.
Workplace Environment Assessment
Businesses can use this technology to monitor employee comfort levels in various environments, such as manufacturing plants or outdoor job sites. By gauging sweat levels, employers can make informed decisions about ventilation, climate control, and workload on hot days.
Clothing and Gear Development
Apparel companies can utilize this function to design sweat-wicking fabrics and performance gear. By understanding how different materials interact with sweat levels, they can create products that improve comfort and effectiveness for active individuals.
Event Management
Organizers of outdoor events and festivals can implement this technology to monitor attendees' sweat levels for crowd management and safety. By identifying high-sweat zones through real-time data, they can implement cooling measures effectively to enhance visitor comfort and enjoyment.
Common questions
What's the difference between a zero-shot and a Nyckel-trained classifier?
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.
How do I know whether this will work for my application?
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.
What happens when it makes a mistake?
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.
Do I need training data to get started?
No. This how sweaty someone is 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.
What does it cost to try?
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.
Related classifiers
Ready to classify how sweaty someone is at scale?
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.