Identify whether cigarette is lit
using AI
Below is a free classifier to identify whether cigarette is lit. Just upload your image, and our AI will predict if the cigarette is lit - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("whether-cigarette-is-lit", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/whether-cigarette-is-lit/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/whether-cigarette-is-lit/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict if the cigarette is lit.
This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Cigarette Lit and Cigarette Unlit.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if the cigarette is lit).
Whether you're just curious or building whether cigarette is lit detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify whether cigarette is lit at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Public Health Monitoring: This function can be utilized by public health organizations to monitor smoking habits in different demographics and locations. By analyzing images from social media or public spaces, the data can help identify trends in smoking behavior and inform health initiatives aimed at reducing smoking rates.
- Smoking Violation Enforcement: Municipalities can employ this technology to monitor compliance with anti-smoking laws in public areas. An automated system can flag images of lit cigarettes in prohibited zones, helping enforcement agencies take action more efficiently.
- Behavioral Research: Researchers studying smoking-related behaviors can use this image classification function to collect data for analysis. By identifying when and where individuals smoke, researchers can better understand the triggers and contexts that lead to smoking, contributing to more effective intervention strategies.
- Smart Surveillance Systems: Security systems for venues such as malls, parks, or event spaces can integrate this function to detect smoking in non-smoking areas. Automated alerts can be generated when a lit cigarette is identified, allowing staff to respond quickly and maintain a smoke-free environment.
- Insurance Risk Assessment: Insurance companies can use the function to analyze claims related to smoking-related health issues. By analyzing images of individuals smoking, they can assess risk levels more accurately and tailor policies accordingly, potentially influencing premiums based on detected habits.
- Environmental Monitoring: Environmental organizations can track littering behavior associated with smoking by identifying instances of lit cigarettes in outdoor settings. This information can help in designing campaigns aimed at reducing litter from tobacco products and fostering cleaner public spaces.
- Marketing and Brand Management: Companies in the tobacco industry can leverage the technology to analyze brand visibility and consumer behavior by monitoring when and where their products are used. Understanding lit cigarette instances can inform marketing strategies and product placement decisions to align with consumer trends.